Provided by: ns3-doc_3.35+dfsg-1ubuntu1_all 
NAME
ns-3-model-library - ns-3 Model Library
This is the ns-3 Model Library documentation. Primary documentation for the ns-3 project is available in
five forms:
• ns-3 Doxygen: Documentation of the public APIs of the simulator
• Tutorial, Manual, and Model Library (this document) for the latest release and development tree
• ns-3 wiki
This document is written in reStructuredText for Sphinx and is maintained in the doc/models directory of
ns-3’s source code.
ORGANIZATION
This manual compiles documentation for ns-3 models and supporting software that enable users to construct
network simulations. It is important to distinguish between modules and models:
• ns-3 software is organized into separate modules that are each built as a separate software library.
Individual ns-3 programs can link the modules (libraries) they need to conduct their simulation.
• ns-3 models are abstract representations of real-world objects, protocols, devices, etc.
An ns-3 module may consist of more than one model (for instance, the internet module contains models for
both TCP and UDP). In general, ns-3 models do not span multiple software modules, however.
This manual provides documentation about the models of ns-3. It complements two other sources of
documentation concerning models:
• the model APIs are documented, from a programming perspective, using Doxygen. Doxygen for ns-3 models
is available on the project web server.
• the ns-3 core is documented in the developer’s manual. ns-3 models make use of the facilities of the
core, such as attributes, default values, random numbers, test frameworks, etc. Consult the main web
site to find copies of the manual.
Finally, additional documentation about various aspects of ns-3 may exist on the project wiki.
A sample outline of how to write model library documentation can be found by executing the
create-module.py program and looking at the template created in the file new-module/doc/new-module.rst.
$ cd src
$ ./create-module.py new-module
The remainder of this document is organized alphabetically by module name.
If you are new to ns-3, you might first want to read below about the network module, which contains some
fundamental models for the simulator. The packet model, models for different address formats, and
abstract base classes for objects such as nodes, net devices, channels, sockets, and applications are
discussed there.
ANIMATION
Animation is an important tool for network simulation. While ns-3 does not contain a default graphical
animation tool, we currently have two ways to provide animation, namely using the PyViz method or the
NetAnim method. The PyViz method is described in http://www.nsnam.org/wiki/PyViz.
We will describe the NetAnim method briefly here.
NetAnim
NetAnim is a standalone, Qt4-based software executable that uses a trace file generated during an ns-3
simulation to display the topology and animate the packet flow between nodes.
[image] An example of packet animation on wired-links.UNINDENT
In addition, NetAnim also provides useful features such as tables to display meta-data of packets like
the image below
[image] An example of tables for packet meta-data with protocol filters.UNINDENT
A way to visualize the trajectory of a mobile node
[image] An example of the trajectory of a mobile node.UNINDENT
A way to display the routing-tables of multiple nodes at various points in time
[image]
A way to display counters associated with multiple nodes as a chart or a table
[image]
[image]
A way to view the timeline of packet transmit and receive events
[image]
Methodology
The class ns3::AnimationInterface is responsible for the creation the trace XML file. AnimationInterface
uses the tracing infrastructure to track packet flows between nodes. AnimationInterface registers itself
as a trace hook for tx and rx events before the simulation begins. When a packet is scheduled for
transmission or reception, the corresponding tx and rx trace hooks in AnimationInterface are called. When
the rx hooks are called, AnimationInterface will be aware of the two endpoints between which a packet has
flowed, and adds this information to the trace file, in XML format along with the corresponding tx and rx
timestamps. The XML format will be discussed in a later section. It is important to note that
AnimationInterface records a packet only if the rx trace hooks are called. Every tx event must be matched
by an rx event.
Downloading NetAnim
If NetAnim is not already available in the ns-3 package you downloaded, you can do the following:
Please ensure that you have installed mercurial. The latest version of NetAnim can be downloaded using
mercurial with the following command:
$ hg clone http://code.nsnam.org/netanim
Building NetAnim
Prerequisites
Qt5 (5.4 and over) is required to build NetAnim. This can be obtained using the following ways:
For Ubuntu Linux distributions:
$ apt-get install qt5-default
For Red Hat/Fedora based distribution:
$ yum install qt5
$ yum install qt5-devel
For Mac/OSX, see http://qt.nokia.com/downloads/
Build steps
To build NetAnim use the following commands:
$ cd netanim
$ make clean
$ qmake NetAnim.pro
$ make
Note: qmake could be “qmake-qt5” in some systems
This should create an executable named “NetAnim” in the same directory:
$ ls -l NetAnim
-rwxr-xr-x 1 john john 390395 2012-05-22 08:32 NetAnim
Usage
Using NetAnim is a two-step process
Step 1:Generate the animation XML trace file during simulation using “ns3::AnimationInterface” in the
ns-3 code base.
Step 2:Load the XML trace file generated in Step 1 with the offline Qt4-based animator named NetAnim.
Step 1: Generate XML animation trace file
The class “AnimationInterface” under “src/netanim” uses underlying ns-3 trace sources to construct a
timestamped ASCII file in XML format.
Examples are found under src/netanim/examples Example:
$ ./waf -d debug configure --enable-examples
$ ./waf --run "dumbbell-animation"
The above will create an XML file dumbbell-animation.xml
Mandatory
1. Ensure that your program’s wscript includes the “netanim” module. An example of such a wscript is at
src/netanim/examples/wscript.
2. Include the header [#include “ns3/netanim-module.h”] in your test program
3. Add the statement
AnimationInterface anim ("animation.xml"); // where "animation.xml" is any arbitrary filename
[for versions before ns-3.13 you also have to use the line “anim.SetXMLOutput() to set the XML mode and
also use anim.StartAnimation();]
Optional
The following are optional but useful steps:
// Step 1
anim.SetMobilityPollInterval (Seconds (1));
AnimationInterface records the position of all nodes every 250 ms by default. The statement above sets
the periodic interval at which AnimationInterface records the position of all nodes. If the nodes are
expected to move very little, it is useful to set a high mobility poll interval to avoid large XML files.
// Step 2
anim.SetConstantPosition (Ptr< Node > n, double x, double y);
AnimationInterface requires that the position of all nodes be set. In ns-3 this is done by setting an
associated MobilityModel. “SetConstantPosition” is a quick way to set the x-y coordinates of a node which
is stationary.
// Step 3
anim.SetStartTime (Seconds(150)); and anim.SetStopTime (Seconds(150));
AnimationInterface can generate large XML files. The above statements restricts the window between which
AnimationInterface does tracing. Restricting the window serves to focus only on relevant portions of the
simulation and creating manageably small XML files
// Step 4
AnimationInterface anim ("animation.xml", 50000);
Using the above constructor ensures that each animation XML trace file has only 50000 packets. For
example, if AnimationInterface captures 150000 packets, using the above constructor splits the capture
into 3 files
• animation.xml - containing the packet range 1-50000
• animation.xml-1 - containing the packet range 50001-100000
• animation.xml-2 - containing the packet range 100001-150000
// Step 5
anim.EnablePacketMetadata (true);
With the above statement, AnimationInterface records the meta-data of each packet in the xml trace file.
Metadata can be used by NetAnim to provide better statistics and filter, along with providing some brief
information about the packet such as TCP sequence number or source & destination IP address during packet
animation.
CAUTION: Enabling this feature will result in larger XML trace files. Please do NOT enable this feature
when using Wimax links.
// Step 6
anim.UpdateNodeDescription (5, "Access-point");
With the above statement, AnimationInterface assigns the text “Access-point” to node 5.
// Step 7
anim.UpdateNodeSize (6, 1.5, 1.5);
With the above statement, AnimationInterface sets the node size to scale by 1.5. NetAnim automatically
scales the graphics view to fit the oboundaries of the topology. This means that NetAnim, can abnormally
scale a node’s size too high or too low. Using AnimationInterface::UpdateNodeSize allows you to overwrite
the default scaling in NetAnim and use your own custom scale.
// Step 8
anim.UpdateNodeCounter (89, 7, 3.4);
With the above statement, AnimationInterface sets the counter with Id == 89, associated with Node 7 with
the value 3.4. The counter with Id 89 is obtained using AnimationInterface::AddNodeCounter. An example
usage for this is in src/netanim/examples/resource-counters.cc.
Step 2: Loading the XML in NetAnim
1. Assuming NetAnim was built, use the command “./NetAnim” to launch NetAnim. Please review the section
“Building NetAnim” if NetAnim is not available.
2. When NetAnim is opened, click on the File open button at the top-left corner, select the XML file
generated during Step 1.
3. Hit the green play button to begin animation.
Here is a video illustrating this http://www.youtube.com/watch?v=tz_hUuNwFDs
Wiki
For detailed instructions on installing “NetAnim”, F.A.Qs and loading the XML trace file (mentioned
earlier) using NetAnim please refer: http://www.nsnam.org/wiki/NetAnim
ANTENNA MODULE
Design documentation
Overview
The Antenna module provides:
1. a class (Angles) and utility functions to deal with angles
2. a base class (AntennaModel) that provides an interface for the modeling of the radiation pattern of
an antenna;
3. a set of classes derived from this base class that each models the radiation pattern of different
types of antennas;
4. a base class (PhasedArrayModel) that provides a flexible interface for modeling a number of Phase
Antenna Array (PAA) models
5. a class (UniformPlanarArray) derived from this base class, implementing a Uniform Planar Array
(UPA) supporting both rectangular and linear lattices
Angles
The Angles class holds information about an angle in 3D space using spherical coordinates in radian
units. Specifically, it uses the azimuth-inclination convention, where
• Inclination is the angle between the zenith direction (positive z-axis) and the desired direction. It
is included in the range [0, pi] radians.
• Azimuth is the signed angle measured from the positive x-axis, where a positive direction goes towards
the positive y-axis. It is included in the range [-pi, pi) radians.
Multiple constructors are present, supporting the most common ways to encode information on a direction.
A static boolean variable allows the user to decide whether angles should be printed in radian or degree
units.
A number of angle-related utilities are offered, such as radians/degree conversions, for both scalars and
vectors, and angle wrapping.
AntennaModel
The AntennaModel uses the coordinate system adopted in [Balanis] and depicted in Figure Coordinate system
of the AntennaModel. This system is obtained by translating the Cartesian coordinate system used by the
ns-3 MobilityModel into the new origin o which is the location of the antenna, and then transforming the
coordinates of every generic point p of the space from Cartesian coordinates (x,y,z) into spherical
coordinates (r, \theta,\phi). The antenna model neglects the radial component r, and only considers the
angle components (\theta, \phi). An antenna radiation pattern is then expressed as a mathematical
function g(\theta, \phi) \longrightarrow \mathcal{R} that returns the gain (in dB) for each possible
direction of transmission/reception. All angles are expressed in radians.
[image] Coordinate system of the AntennaModel.UNINDENT
Single antenna models
In this section we describe the antenna radiation pattern models that are included within the antenna
module.
IsotropicAntennaModel
This antenna radiation pattern model provides a unitary gain (0 dB) for all direction.
CosineAntennaModel
This is the cosine model described in [Chunjian]: the antenna gain is determined as:
g(\phi, \theta) = \cos^{n} \left(\frac{\phi - \phi_{0}}{2} \right)
where \phi_{0} is the azimuthal orientation of the antenna (i.e., its direction of maximum gain) and the
exponential
n = -\frac{3}{20 \log_{10} \left( \cos \frac{\phi_{3dB}}{4} \right)}
determines the desired 3dB beamwidth \phi_{3dB}. Note that this radiation pattern is independent of the
inclination angle \theta.
A major difference between the model of [Chunjian] and the one implemented in the class
CosineAntennaModel is that only the element factor (i.e., what described by the above formulas) is
considered. In fact, [Chunjian] also considered an additional antenna array factor. The reason why the
latter is excluded is that we expect that the average user would desire to specify a given beamwidth
exactly, without adding an array factor at a latter stage which would in practice alter the effective
beamwidth of the resulting radiation pattern.
ParabolicAntennaModel
This model is based on the parabolic approximation of the main lobe radiation pattern. It is often used
in the context of cellular system to model the radiation pattern of a cell sector, see for instance
[R4-092042a] and [Calcev]. The antenna gain in dB is determined as:
g_{dB}(\phi, \theta) = -\min \left( 12 \left(\frac{\phi - \phi_{0}}{\phi_{3dB}} \right)^2, A_{max} \right)
where \phi_{0} is the azimuthal orientation of the antenna (i.e., its direction of maximum gain),
\phi_{3dB} is its 3 dB beamwidth, and A_{max} is the maximum attenuation in dB of the antenna. Note that
this radiation pattern is independent of the inclination angle \theta.
ThreeGppAntennaModel
This model implements the antenna element described in [38901]. Parameters are fixed from the technical
report, thus no attributes nor setters are provided. The model is largely based on the
ParabolicAntennaModel.
Phased Array Model
The class PhasedArrayModel has been created with flexibility in mind. It abstracts the basic idea of a
Phased Antenna Array (PAA) by removing any constraint on the position of each element, and instead
generalizes the concept of steering and beamforming vectors, solely based on the generalized location of
the antenna elements. For details on Phased Array Antennas see for instance [Mailloux].
Derived classes must implement the following functions:
• GetNumberOfElements: returns the number of antenna elements
• GetElementLocation: returns the location of the antenna element with the specified index, normalized
with respect to the wavelength
• GetElementFieldPattern: returns the horizontal and vertical components of the antenna element field
pattern at the specified direction. Same polarization (configurable) for all antenna elements of the
array is considered.
The class PhasedArrayModel also assumes that all antenna elements are equal, a typical key assumption
which allows to model the PAA field pattern as the sum of the array factor, given by the geometry of the
location of the antenna elements, and the element field pattern. Any class derived from AntennaModel is
a valid antenna element for the PhasedArrayModel, allowing for a great flexibility of the framework.
UniformPlanarArray
The class UniformPlanarArray is a generic implementation of Uniform Planar Arrays (UPAs), supporting
rectangular and linear regular lattices. It loosely follows the implementation described in the 3GPP TR
38.901 [38901], considering only a single a single panel, i.e., N_{g} = M_{g} = 1.
By default, the array is orthogonal to the x-axis, pointing towards the positive direction, but the
orientation can be changed through the attributes “BearingAngle”, which adjusts the azimuth angle, and
“DowntiltAngle”, which adjusts the elevation angle. The slant angle is instead fixed and assumed to be
0.
The number of antenna elements in the vertical and horizontal directions can be configured through the
attributes “NumRows” and “NumColumns”, while the spacing between the horizontal and vertical elements can
be configured through the attributes “AntennaHorizontalSpacing” and “AntennaVerticalSpacing”.
The polarization of each antenna element in the array is determined by the polarization slant angle
through the attribute “PolSlantAngle”, as described in [38901] (i.e., {\zeta}).
[Balanis]
C.A. Balanis, “Antenna Theory - Analysis and Design”, Wiley, 2nd Ed.
[Chunjian]
Li Chunjian, “Efficient Antenna Patterns for Three-Sector WCDMA Systems”, Master of Science Thesis,
Chalmers University of Technology, Göteborg, Sweden, 2003
[Calcev]
George Calcev and Matt Dillon, “Antenna Tilt Control in CDMA Networks”, in Proc. of the 2nd Annual
International Wireless Internet Conference (WICON), 2006
[R4-092042a]
3GPP TSG RAN WG4 (Radio) Meeting #51, R4-092042, Simulation assumptions and parameters for FDD HeNB
RF requirements.
[38901]
3GPP. 2018. TR 38.901, Study on channel model for frequencies from 0.5 to 100 GHz, V15.0.0.
(2018-06).
[Mailloux]
Robert J. Mailloux, “Phased Array Antenna Handbook”, Artech House, 2nd Ed.
User Documentation
The antenna modeled can be used with all the wireless technologies and physical layer models that support
it. Currently, this includes the physical layer models based on the SpectrumPhy. Please refer to the
documentation of each of these models for details.
Testing Documentation
In this section we describe the test suites included with the antenna module that verify its correct
functionality.
Angles
The unit test suite angles verifies that the Angles class is constructed properly by correct conversion
from 3D Cartesian coordinates according to the available methods (construction from a single vector and
from a pair of vectors). For each method, several test cases are provided that compare the values (\phi,
\theta) determined by the constructor to known reference values. The test passes if for each case the
values are equal to the reference up to a tolerance of 10^{-10} which accounts for numerical errors.
DegreesToRadians
The unit test suite degrees-radians verifies that the methods DegreesToRadians and RadiansToDegrees work
properly by comparing with known reference values in a number of test cases. Each test case passes if the
comparison is equal up to a tolerance of 10^{-10} which accounts for numerical errors.
IsotropicAntennaModel
The unit test suite isotropic-antenna-model checks that the IsotropicAntennaModel class works properly,
i.e., returns always a 0dB gain regardless of the direction.
CosineAntennaModel
The unit test suite cosine-antenna-model checks that the CosineAntennaModel class works properly. Several
test cases are provided that check for the antenna gain value calculated at different directions and for
different values of the orientation, the reference gain and the beamwidth. The reference gain is
calculated by hand. Each test case passes if the reference gain in dB is equal to the value returned by
CosineAntennaModel within a tolerance of 0.001, which accounts for the approximation done for the
calculation of the reference values.
ParabolicAntennaModel
The unit test suite parabolic-antenna-model checks that the ParabolicAntennaModel class works properly.
Several test cases are provided that check for the antenna gain value calculated at different directions
and for different values of the orientation, the maximum attenuation and the beamwidth. The reference
gain is calculated by hand. Each test case passes if the reference gain in dB is equal to the value
returned by ParabolicAntennaModel within a tolerance of 0.001, which accounts for the approximation done
for the calculation of the reference values.
AD HOC ON-DEMAND DISTANCE VECTOR (AODV)
This model implements the base specification of the Ad Hoc On-Demand Distance Vector (AODV) protocol. The
implementation is based on RFC 3561.
The model was written by Elena Buchatskaia and Pavel Boyko of ITTP RAS, and is based on the ns-2 AODV
model developed by the CMU/MONARCH group and optimized and tuned by Samir Das and Mahesh Marina,
University of Cincinnati, and also on the AODV-UU implementation by Erik Nordström of Uppsala University.
Model Description
The source code for the AODV model lives in the directory src/aodv.
Design
Class ns3::aodv::RoutingProtocol implements all functionality of service packet exchange and inherits
from ns3::Ipv4RoutingProtocol. The base class defines two virtual functions for packet routing and
forwarding. The first one, ns3::aodv::RouteOutput, is used for locally originated packets, and the
second one, ns3::aodv::RouteInput, is used for forwarding and/or delivering received packets.
Protocol operation depends on many adjustable parameters. Parameters for this functionality are
attributes of ns3::aodv::RoutingProtocol. Parameter default values are drawn from the RFC and allow the
enabling/disabling protocol features, such as broadcasting HELLO messages, broadcasting data packets and
so on.
AODV discovers routes on demand. Therefore, the AODV model buffers all packets while a route request
packet (RREQ) is disseminated. A packet queue is implemented in aodv-rqueue.cc. A smart pointer to the
packet, ns3::Ipv4RoutingProtocol::ErrorCallback, ns3::Ipv4RoutingProtocol::UnicastForwardCallback, and
the IP header are stored in this queue. The packet queue implements garbage collection of old packets and
a queue size limit.
The routing table implementation supports garbage collection of old entries and state machine, defined in
the standard. It is implemented as a STL map container. The key is a destination IP address.
Some elements of protocol operation aren’t described in the RFC. These elements generally concern
cooperation of different OSI model layers. The model uses the following heuristics:
• This AODV implementation can detect the presence of unidirectional links and avoid them if necessary.
If the node the model receives an RREQ for is a neighbor, the cause may be a unidirectional link. This
heuristic is taken from AODV-UU implementation and can be disabled.
• Protocol operation strongly depends on broken link detection mechanism. The model implements two such
heuristics. First, this implementation support HELLO messages. However HELLO messages are not a good
way to perform neighbor sensing in a wireless environment (at least not over 802.11). Therefore, one
may experience bad performance when running over wireless. There are several reasons for this: 1)
HELLO messages are broadcasted. In 802.11, broadcasting is often done at a lower bit rate than
unicasting, thus HELLO messages can travel further than unicast data. 2) HELLO messages are small, thus
less prone to bit errors than data transmissions, and 3) Broadcast transmissions are not guaranteed to
be bidirectional, unlike unicast transmissions. Second, we use layer 2 feedback when possible. Link
are considered to be broken if frame transmission results in a transmission failure for all retries.
This mechanism is meant for active links and works faster than the first method.
The layer 2 feedback implementation relies on the TxErrHeader trace source, currently supported in
AdhocWifiMac only.
Scope and Limitations
The model is for IPv4 only. The following optional protocol optimizations are not implemented:
1. Local link repair.
2. RREP, RREQ and HELLO message extensions.
These techniques require direct access to IP header, which contradicts the assertion from the AODV RFC
that AODV works over UDP. This model uses UDP for simplicity, hindering the ability to implement certain
protocol optimizations. The model doesn’t use low layer raw sockets because they are not portable.
Future Work
No announced plans.
3GPP HTTP APPLICATIONS
Model Description
The model is a part of the applications library. The HTTP model is based on a commonly used 3GPP model in
standardization [4].
Design
This traffic generator simulates web browsing traffic using the Hypertext Transfer Protocol (HTTP). It
consists of one or more ThreeGppHttpClient applications which connect to a ThreeGppHttpServer
application. The client models a web browser which requests web pages to the server. The server is then
responsible to serve the web pages as requested. Please refer to ThreeGppHttpClientHelper and
ThreeGppHttpServerHelper for usage instructions.
Technically speaking, the client transmits request objects to demand a service from the server. Depending
on the type of request received, the server transmits either:
• a main object, i.e., the HTML file of the web page; or
• an embedded object, e.g., an image referenced by the HTML file.
The main and embedded object sizes are illustrated in figures 3GPP HTTP main object size histogram and
3GPP HTTP embedded object size histogram.
[image] 3GPP HTTP main object size histogram.UNINDENT
[image] 3GPP HTTP embedded object size histogram.UNINDENT
A major portion of the traffic pattern is reading time, which does not generate any traffic. Because of
this, one may need to simulate a good number of clients and/or sufficiently long simulation duration in
order to generate any significant traffic in the system. Reading time is illustrated in 3GPP HTTP
reading time histogram.
[image] 3GPP HTTP reading time histogram.UNINDENT
3GPP HTTP server description
3GPP HTTP server is a model application which simulates the traffic of a web server. This application
works in conjunction with ThreeGppHttpClient applications.
The application works by responding to requests. Each request is a small packet of data which contains
ThreeGppHttpHeader. The value of the content type field of the header determines the type of object that
the client is requesting. The possible type is either a main object or an embedded object.
The application is responsible to generate the right type of object and send it back to the client. The
size of each object to be sent is randomly determined (see ThreeGppHttpVariables). Each object may be
sent as multiple packets due to limited socket buffer space.
To assist with the transmission, the application maintains several instances of
ThreeGppHttpServerTxBuffer. Each instance keeps track of the object type to be served and the number of
bytes left to be sent.
The application accepts connection request from clients. Every connection is kept open until the client
disconnects.
Maximum transmission unit (MTU) size is configurable in ThreeGppHttpServer or in ThreeGppHttpVariables.
By default, the low variant is 536 bytes and high variant is 1460 bytes. The default values are set with
the intention of having a TCP header (size of which is 40 bytes) added in the packet in such way that
lower layers can avoid splitting packets. The change of MTU sizes affects all TCP sockets after the
server application has started. It is mainly visible in sizes of packets received by ThreeGppHttpClient
applications.
3GPP HTTP client description
3GPP HTTP client is a model application which simulates the traffic of a web browser. This application
works in conjunction with an ThreeGppHttpServer application.
In summary, the application works as follows.
1. Upon start, it opens a connection to the destination web server (ThreeGppHttpServer).
2. After the connection is established, the application immediately requests a main object from the
server by sending a request packet.
3. After receiving a main object (which can take some time if it consists of several packets), the
application “parses” the main object. Parsing time is illustrated in figure 3GPP HTTP parsing time
histogram.
4. The parsing takes a short time (randomly determined) to determine the number of embedded objects (also
randomly determined) in the web page. Number of embedded object is illustrated in 3GPP HTTP number of
embedded objects histogram.
•
If at least one embedded object is determined, the application requests
the first embedded object from the server. The request for the next embedded object
follows after the previous embedded object has been completely received.
•
If there is no more embedded object to request, the application enters
the reading time.
5. Reading time is a long delay (again, randomly determined) where the application does not induce any
network traffic, thus simulating the user reading the downloaded web page.
6. After the reading time is finished, the process repeats to step #2.
[image] 3GPP HTTP parsing time histogram.UNINDENT
[image] 3GPP HTTP number of embedded objects histogram.UNINDENT
The client models HTTP persistent connection, i.e., HTTP 1.1, where the connection to the server is
maintained and used for transmitting and receiving all objects.
Each request by default has a constant size of 350 bytes. A ThreeGppHttpHeader is attached to each
request packet. The header contains information such as the content type requested (either main object
or embedded object) and the timestamp when the packet is transmitted (which will be used to compute the
delay and RTT of the packet).
References
Many aspects of the traffic are randomly determined by ThreeGppHttpVariables. A separate instance of
this object is used by the HTTP server and client applications. These characteristics are based on a
legacy 3GPP specification. The description can be found in the following references:
[1] 3GPP TR 25.892, “Feasibility Study for Orthogonal Frequency Division Multiplexing (OFDM) for UTRAN
enhancement”
[2] IEEE 802.16m, “Evaluation Methodology Document (EMD)”, IEEE 802.16m-08/004r5, July 2008.
[3] NGMN Alliance, “NGMN Radio Access Performance Evaluation Methodology”, v1.0, January 2008.
[4] 3GPP2-TSGC5, “HTTP, FTP and TCP models for 1xEV-DV simulations”, 2001.
Usage
The three-gpp-http-example can be referenced to see basic usage of the HTTP applications. In summary,
using the ThreeGppHttpServerHelper and ThreeGppHttpClientHelper allow the user to easily install
ThreeGppHttpServer and ThreeGppHttpClient applications to nodes. The helper objects can be used to
configure attribute values for the client and server objects, but not for the ThreeGppHttpVariables
object. Configuration of variables is done by modifying attributes of ThreeGppHttpVariables, which should
be done prior to helpers installing applications to nodes.
The client and server provide a number of ns-3 trace sources such as “Tx”, “Rx”, “RxDelay”, and
“StateTransition” on the server side, and a large number on the client side (“ConnectionEstablished”,
“ConnectionClosed”,”TxMainObjectRequest”, “TxEmbeddedObjectRequest”, “RxMainObjectPacket”,
“RxMainObject”, “RxEmbeddedObjectPacket”, “RxEmbeddedObject”, “Rx”, “RxDelay”, “RxRtt”,
“StateTransition”).
Building the 3GPP HTTP applications
Building the applications does not require any special steps to be taken. It suffices to enable the
applications module.
Examples
For an example demonstrating HTTP applications run:
$ ./waf --run 'three-gpp-http-example'
By default, the example will print out the web page requests of the client and responses of the server
and client receiving content packets by using LOG_INFO of ThreeGppHttpServer and ThreeGppHttpClient.
Tests
For testing HTTP applications, three-gpp-http-client-server-test is provided. Run:
$ ./test.py -s three-gpp-http-client-server-test
The test consists of simple Internet nodes having HTTP server and client applications installed.
Multiple variant scenarios are tested: delay is 3ms, 30ms or 300ms, bit error rate 0 or 5.0*10^(-6), MTU
size 536 or 1460 bytes and either IPV4 or IPV6 is used. A simulation with each combination of these
parameters is run multiple times to verify functionality with different random variables.
Test cases themselves are rather simple: test verifies that HTTP object packet bytes sent match total
bytes received by the client, and that ThreeGppHttpHeader matches the expected packet.
BRIDGE NETDEVICE
Placeholder chapter
Some examples of the use of Bridge NetDevice can be found in examples/csma/ directory.
BRITE INTEGRATION
This model implements an interface to BRITE, the Boston university Representative Internet Topology
gEnerator [1]. BRITE is a standard tool for generating realistic internet topologies. The ns-3 model,
described herein, provides a helper class to facilitate generating ns-3 specific topologies using BRITE
configuration files. BRITE builds the original graph which is stored as nodes and edges in the ns-3
BriteTopolgyHelper class. In the ns-3 integration of BRITE, the generator generates a topology and then
provides access to leaf nodes for each AS generated. ns-3 users can than attach custom topologies to
these leaf nodes either by creating them manually or using topology generators provided in ns-3.
There are three major types of topologies available in BRITE: Router, AS, and Hierarchical which is a
combination of AS and Router. For the purposes of ns-3 simulation, the most useful are likely to be
Router and Hierarchical. Router level topologies be generated using either the Waxman model or the
Barabasi-Albert model. Each model has different parameters that effect topology creation. For flat
router topologies, all nodes are considered to be in the same AS.
BRITE Hierarchical topologies contain two levels. The first is the AS level. This level can be also be
created by using either the Waxman model or the Barabasi-Albert model. Then for each node in the AS
topology, a router level topology is constructed. These router level topologies can again either use the
Waxman model or the Barbasi-Albert model. BRITE interconnects these separate router topologies as
specified by the AS level topology. Once the hierarchical topology is constructed, it is flattened into
a large router level topology.
Further information can be found in the BRITE user manual:
http://www.cs.bu.edu/brite/publications/usermanual.pdf
Model Description
The model relies on building an external BRITE library, and then building some ns-3 helpers that call out
to the library. The source code for the ns-3 helpers lives in the directory src/brite/helper.
Design
To generate the BRITE topology, ns-3 helpers call out to the external BRITE library, and using a standard
BRITE configuration file, the BRITE code builds a graph with nodes and edges according to this
configuration file. Please see the BRITE documentation or the example configuration files in
src/brite/examples/conf_files to get a better grasp of BRITE configuration options. The graph built by
BRITE is returned to ns-3, and a ns-3 implementation of the graph is built. Leaf nodes for each AS are
available for the user to either attach custom topologies or install ns-3 applications directly.
References
[1] Alberto Medina, Anukool Lakhina, Ibrahim Matta, and John Byers. BRITE: An Approach to Universal
Topology Generation. In Proceedings of the International Workshop on Modeling, Analysis and
Simulation of Computer and Telecommunications Systems- MASCOTS ‘01, Cincinnati, Ohio, August 2001.
Usage
The brite-generic-example can be referenced to see basic usage of the BRITE interface. In summary, the
BriteTopologyHelper is used as the interface point by passing in a BRITE configuration file. Along with
the configuration file a BRITE formatted random seed file can also be passed in. If a seed file is not
passed in, the helper will create a seed file using ns-3’s UniformRandomVariable. Once the topology has
been generated by BRITE, BuildBriteTopology() is called to create the ns-3 representation. Next IP
Address can be assigned to the topology using either AssignIpv4Addresses() or AssignIpv6Addresses(). It
should be noted that each point-to-point link in the topology will be treated as a new network therefore
for IPV4 a /30 subnet should be used to avoid wasting a large amount of the available address space.
Example BRITE configuration files can be found in /src/brite/examples/conf_files/. ASBarbasi and
ASWaxman are examples of AS only topologies. The RTBarabasi and RTWaxman files are examples of router
only topologies. Finally the TD_ASBarabasi_RTWaxman configuration file is an example of a Hierarchical
topology that uses the Barabasi-Albert model for the AS level and the Waxman model for each of the router
level topologies. Information on the BRITE parameters used in these files can be found in the BRITE
user manual.
Building BRITE Integration
The first step is to download and build the ns-3 specific BRITE repository:
$ hg clone http://code.nsnam.org/BRITE
$ cd BRITE
$ make
This will build BRITE and create a library, libbrite.so, within the BRITE directory.
Once BRITE has been built successfully, we proceed to configure ns-3 with BRITE support. Change to your
ns-3 directory:
$ ./waf configure --with-brite=/your/path/to/brite/source --enable-examples
Make sure it says ‘enabled’ beside ‘BRITE Integration’. If it does not, then something has gone wrong.
Either you have forgotten to build BRITE first following the steps above, or ns-3 could not find your
BRITE directory.
Next, build ns-3:
$ ./waf
Examples
For an example demonstrating BRITE integration run:
$ ./waf --run 'brite-generic-example'
By enabling the verbose parameter, the example will print out the node and edge information in a similar
format to standard BRITE output. There are many other command-line parameters including confFile,
tracing, and nix, described below:
confFile
A BRITE configuration file. Many different BRITE configuration file examples exist in the
src/brite/examples/conf_files directory, for example, RTBarabasi20.conf and RTWaxman.conf.
Please refer to the conf_files directory for more examples.
tracing
Enables ascii tracing.
nix Enables nix-vector routing. Global routing is used by default.
The generic BRITE example also support visualization using pyviz, assuming python bindings in ns-3 are
enabled:
$ ./waf --run brite-generic-example --vis
Simulations involving BRITE can also be used with MPI. The total number of MPI instances is passed to
the BRITE topology helper where a modulo divide is used to assign the nodes for each AS to a MPI
instance. An example can be found in src/brite/examples:
$ mpirun -np 2 ./waf --run brite-MPI-example
Please see the ns-3 MPI documentation for information on setting up MPI with ns-3.
BUILDINGS MODULE
cd .. include:: replace.txt
Design documentation
Overview
The Buildings module provides:
1. a new class (Building) that models the presence of a building in a simulation scenario;
2. a new class (MobilityBuildingInfo) that allows to specify the location, size and characteristics of
buildings present in the simulated area, and allows the placement of nodes inside those buildings;
3. a container class with the definition of the most useful pathloss models and the correspondent
variables called BuildingsPropagationLossModel.
4. a new propagation model (HybridBuildingsPropagationLossModel) working with the mobility model just
introduced, that allows to model the phenomenon of indoor/outdoor propagation in the presence of
buildings.
5. a simplified model working only with Okumura Hata (OhBuildingsPropagationLossModel) considering the
phenomenon of indoor/outdoor propagation in the presence of buildings.
6. a channel condition model (BuildingsChannelConditionModel) which determined the LOS/NLOS channel
condition based on the Building objects deployed in the scenario.
7. hybrid channel condition models (ThreeGppV2vUrbanChannelConditionModel and
ThreeGppV2vHighwayChannelConditionModel) specifically designed to model vehicular environments
(more information can be found in the documentation of the propagation module)
The models have been designed with LTE in mind, though their implementation is in fact independent from
any LTE-specific code, and can be used with other ns-3 wireless technologies as well (e.g., wifi, wimax).
The HybridBuildingsPropagationLossModel pathloss model included is obtained through a combination of
several well known pathloss models in order to mimic different environmental scenarios such as urban,
suburban and open areas. Moreover, the model considers both outdoor and indoor indoor and outdoor
communication has to be included since HeNB might be installed either within building and either outside.
In case of indoor communication, the model has to consider also the type of building in outdoor <->
indoor communication according to some general criteria such as the wall penetration losses of the common
materials; moreover it includes some general configuration for the internal walls in indoor
communications.
The OhBuildingsPropagationLossModel pathloss model has been created for simplifying the previous one
removing the thresholds for switching from one model to other. For doing this it has been used only one
propagation model from the one available (i.e., the Okumura Hata). The presence of building is still
considered in the model; therefore all the considerations of above regarding the building type are still
valid. The same consideration can be done for what concern the environmental scenario and frequency since
both of them are parameters of the model considered.
The Building class
The model includes a specific class called Building which contains a ns3 Box class for defining the
dimension of the building. In order to implements the characteristics of the pathloss models included,
the Building class supports the following attributes:
• building type:
• Residential (default value)
• Office
• Commercial
• external walls type
• Wood
• ConcreteWithWindows (default value)
• ConcreteWithoutWindows
• StoneBlocks
• number of floors (default value 1, which means only ground-floor)
• number of rooms in x-axis (default value 1)
• number of rooms in y-axis (default value 1)
The Building class is based on the following assumptions:
• a buildings is represented as a rectangular parallelepiped (i.e., a box)
• the walls are parallel to the x, y, and z axis
• a building is divided into a grid of rooms, identified by the following parameters:
• number of floors
• number of rooms along the x-axis
• number of rooms along the y-axis
• the z axis is the vertical axis, i.e., floor numbers increase for increasing z axis values
• the x and y room indices start from 1 and increase along the x and y axis respectively
• all rooms in a building have equal size
The MobilityBuildingInfo class
The MobilityBuildingInfo class, which inherits from the ns3 class Object, is in charge of maintaining
information about the position of a node with respect to building. The information managed by
MobilityBuildingInfo is:
• whether the node is indoor or outdoor
• if indoor:
• in which building the node is
• in which room the node is positioned (x, y and floor room indices)
The class MobilityBuildingInfo is used by BuildingsPropagationLossModel class, which inherits from the
ns3 class PropagationLossModel and manages the pathloss computation of the single components and their
composition according to the nodes’ positions. Moreover, it implements also the shadowing, that is the
loss due to obstacles in the main path (i.e., vegetation, buildings, etc.).
It is to be noted that, MobilityBuildingInfo can be used by any other propagation model. However, based
on the information at the time of this writing, only the ones defined in the building module are designed
for considering the constraints introduced by the buildings.
ItuR1238PropagationLossModel
This class implements a building-dependent indoor propagation loss model based on the ITU P.1238 model,
which includes losses due to type of building (i.e., residential, office and commercial). The analytical
expression is given in the following.
L_\mathrm{total} = 20\log f + N\log d + L_f(n)- 28 [dB]
where:
N = \left\{ \begin{array}{lll} 28 & residential \\ 30 & office \\ 22 & commercial\end{array} \right. :
power loss coefficient [dB]
L_f = \left\{ \begin{array}{lll} 4n & residential \\ 15+4(n-1) & office \\ 6+3(n-1) &
commercial\end{array} \right.
n : number of floors between base station and mobile (n\ge 1)
f : frequency [MHz]
d : distance (where d > 1) [m]
BuildingsPropagationLossModel
The BuildingsPropagationLossModel provides an additional set of building-dependent pathloss model
elements that are used to implement different pathloss logics. These pathloss model elements are
described in the following subsections.
External Wall Loss (EWL)
This component models the penetration loss through walls for indoor to outdoor communications and
vice-versa. The values are taken from the [cost231] model.
• Wood ~ 4 dB
• Concrete with windows (not metallized) ~ 7 dB
• Concrete without windows ~ 15 dB (spans between 10 and 20 in COST231)
• Stone blocks ~ 12 dB
Internal Walls Loss (IWL)
This component models the penetration loss occurring in indoor-to-indoor communications within the same
building. The total loss is calculated assuming that each single internal wall has a constant penetration
loss L_{siw}, and approximating the number of walls that are penetrated with the manhattan distance (in
number of rooms) between the transmitter and the receiver. In detail, let x_1, y_1, x_2, y_2 denote the
room number along the x and y axis respectively for user 1 and 2; the total loss L_{IWL} is calculated as
L_{IWL} = L_{siw} (|x_1 -x_2| + |y_1 - y_2|)
Height Gain Model (HG)
This component model the gain due to the fact that the transmitting device is on a floor above the
ground. In the literature [turkmani] this gain has been evaluated as about 2 dB per floor. This gain can
be applied to all the indoor to outdoor communications and vice-versa.
Shadowing Model
The shadowing is modeled according to a log-normal distribution with variable standard deviation as
function of the relative position (indoor or outdoor) of the MobilityModel instances involved. One random
value is drawn for each pair of MobilityModels, and stays constant for that pair during the whole
simulation. Thus, the model is appropriate for static nodes only.
The model considers that the mean of the shadowing loss in dB is always 0. For the variance, the model
considers three possible values of standard deviation, in detail:
• outdoor (m_shadowingSigmaOutdoor, defaul value of 7 dB) \rightarrow X_\mathrm{O} \sim
N(\mu_\mathrm{O}, \sigma_\mathrm{O}^2).
• indoor (m_shadowingSigmaIndoor, defaul value of 10 dB) \rightarrow X_\mathrm{I} \sim
N(\mu_\mathrm{I}, \sigma_\mathrm{I}^2).
• external walls penetration (m_shadowingSigmaExtWalls, default value 5 dB) \rightarrow X_\mathrm{W}
\sim N(\mu_\mathrm{W}, \sigma_\mathrm{W}^2)
The simulator generates a shadowing value per each active link according to nodes’ position the first
time the link is used for transmitting. In case of transmissions from outdoor nodes to indoor ones, and
vice-versa, the standard deviation (\sigma_\mathrm{IO}) has to be calculated as the square root of the
sum of the quadratic values of the standard deviatio in case of outdoor nodes and the one for the
external walls penetration. This is due to the fact that that the components producing the shadowing are
independent of each other; therefore, the variance of a distribution resulting from the sum of two
independent normal ones is the sum of the variances.
X \sim N(\mu,\sigma^2) \mbox{ and } Y \sim N(\nu,\tau^2)
Z = X + Y \sim Z (\mu + \nu, \sigma^2 + \tau^2)
\Rightarrow \sigma_\mathrm{IO} = \sqrt{\sigma_\mathrm{O}^2 + \sigma_\mathrm{W}^2}
Pathloss logics
In the following we describe the different pathloss logic that are implemented by inheriting from
BuildingsPropagationLossModel.
HybridBuildingsPropagationLossModel
The HybridBuildingsPropagationLossModel pathloss model included is obtained through a combination of
several well known pathloss models in order to mimic different outdoor and indoor scenarios, as well as
indoor-to-outdoor and outdoor-to-indoor scenarios. In detail, the class
HybridBuildingsPropagationLossModel integrates the following pathloss models:
• OkumuraHataPropagationLossModel (OH) (at frequencies > 2.3 GHz substituted by
Kun2600MhzPropagationLossModel)
• ItuR1411LosPropagationLossModel and ItuR1411NlosOverRooftopPropagationLossModel (I1411)
• ItuR1238PropagationLossModel (I1238)
• the pathloss elements of the BuildingsPropagationLossModel (EWL, HG, IWL)
The following pseudo-code illustrates how the different pathloss model elements described above are
integrated in HybridBuildingsPropagationLossModel:
if (txNode is outdoor)
then
if (rxNode is outdoor)
then
if (distance > 1 km)
then
if (rxNode or txNode is below the rooftop)
then
L = I1411
else
L = OH
else
L = I1411
else (rxNode is indoor)
if (distance > 1 km)
then
if (rxNode or txNode is below the rooftop)
L = I1411 + EWL + HG
else
L = OH + EWL + HG
else
L = I1411 + EWL + HG
else (txNode is indoor)
if (rxNode is indoor)
then
if (same building)
then
L = I1238 + IWL
else
L = I1411 + 2*EWL
else (rxNode is outdoor)
if (distance > 1 km)
then
if (rxNode or txNode is below the rooftop)
then
L = I1411 + EWL + HG
else
L = OH + EWL + HG
else
L = I1411 + EWL
We note that, for the case of communication between two nodes below rooftop level with distance is
greater then 1 km, we still consider the I1411 model, since OH is specifically designed for macro cells
and therefore for antennas above the roof-top level.
For the ITU-R P.1411 model we consider both the LOS and NLoS versions. In particular, we considers the
LoS propagation for distances that are shorted than a tunable threshold (m_itu1411NlosThreshold). In case
on NLoS propagation, the over the roof-top model is taken in consideration for modeling both macro BS and
SC. In case on NLoS several parameters scenario dependent have been included, such as average street
width, orientation, etc. The values of such parameters have to be properly set according to the scenario
implemented, the model does not calculate natively their values. In case any values is provided, the
standard ones are used, apart for the height of the mobile and BS, which instead their integrity is
tested directly in the code (i.e., they have to be greater then zero). In the following we give the
expressions of the components of the model.
We also note that the use of different propagation models (OH, I1411, I1238 with their variants) in
HybridBuildingsPropagationLossModel can result in discontinuities of the pathloss with respect to
distance. A proper tuning of the attributes (especially the distance threshold attributes) can avoid
these discontinuities. However, since the behavior of each model depends on several other parameters
(frequency, node height, etc), there is no default value of these thresholds that can avoid the
discontinuities in all possible configurations. Hence, an appropriate tuning of these parameters is left
to the user.
OhBuildingsPropagationLossModel
The OhBuildingsPropagationLossModel class has been created as a simple means to solve the discontinuity
problems of HybridBuildingsPropagationLossModel without doing scenario-specific parameter tuning. The
solution is to use only one propagation loss model (i.e., Okumura Hata), while retaining the structure of
the pathloss logic for the calculation of other path loss components (such as wall penetration losses).
The result is a model that is free of discontinuities (except those due to walls), but that is less
realistic overall for a generic scenario with buildings and outdoor/indoor users, e.g., because Okumura
Hata is not suitable neither for indoor communications nor for outdoor communications below rooftop
level.
In detail, the class OhBuildingsPropagationLossModel integrates the following pathloss models:
• OkumuraHataPropagationLossModel (OH)
• the pathloss elements of the BuildingsPropagationLossModel (EWL, HG, IWL)
The following pseudo-code illustrates how the different pathloss model elements described above are
integrated in OhBuildingsPropagationLossModel:
if (txNode is outdoor)
then
if (rxNode is outdoor)
then
L = OH
else (rxNode is indoor)
L = OH + EWL
else (txNode is indoor)
if (rxNode is indoor)
then
if (same building)
then
L = OH + IWL
else
L = OH + 2*EWL
else (rxNode is outdoor)
L = OH + EWL
We note that OhBuildingsPropagationLossModel is a significant simplification with respect to
HybridBuildingsPropagationLossModel, due to the fact that OH is used always. While this gives a less
accurate model in some scenarios (especially below rooftop and indoor), it effectively avoids the issue
of pathloss discontinuities that affects HybridBuildingsPropagationLossModel.
User Documentation
How to use buildings in a simulation
In this section we explain the basic usage of the buildings model within a simulation program.
Include the headers
Add this at the beginning of your simulation program:
#include <ns3/buildings-module.h>
Create a building
As an example, let’s create a residential 10 x 20 x 10 building:
double x_min = 0.0;
double x_max = 10.0;
double y_min = 0.0;
double y_max = 20.0;
double z_min = 0.0;
double z_max = 10.0;
Ptr<Building> b = CreateObject <Building> ();
b->SetBoundaries (Box (x_min, x_max, y_min, y_max, z_min, z_max));
b->SetBuildingType (Building::Residential);
b->SetExtWallsType (Building::ConcreteWithWindows);
b->SetNFloors (3);
b->SetNRoomsX (3);
b->SetNRoomsY (2);
This building has three floors and an internal 3 x 2 grid of rooms of equal size.
The helper class GridBuildingAllocator is also available to easily create a set of buildings with
identical characteristics placed on a rectangular grid. Here’s an example of how to use it:
Ptr<GridBuildingAllocator> gridBuildingAllocator;
gridBuildingAllocator = CreateObject<GridBuildingAllocator> ();
gridBuildingAllocator->SetAttribute ("GridWidth", UintegerValue (3));
gridBuildingAllocator->SetAttribute ("LengthX", DoubleValue (7));
gridBuildingAllocator->SetAttribute ("LengthY", DoubleValue (13));
gridBuildingAllocator->SetAttribute ("DeltaX", DoubleValue (3));
gridBuildingAllocator->SetAttribute ("DeltaY", DoubleValue (3));
gridBuildingAllocator->SetAttribute ("Height", DoubleValue (6));
gridBuildingAllocator->SetBuildingAttribute ("NRoomsX", UintegerValue (2));
gridBuildingAllocator->SetBuildingAttribute ("NRoomsY", UintegerValue (4));
gridBuildingAllocator->SetBuildingAttribute ("NFloors", UintegerValue (2));
gridBuildingAllocator->SetAttribute ("MinX", DoubleValue (0));
gridBuildingAllocator->SetAttribute ("MinY", DoubleValue (0));
gridBuildingAllocator->Create (6);
This will create a 3x2 grid of 6 buildings, each 7 x 13 x 6 m with 2 x 4 rooms inside and 2 foors; the
buildings are spaced by 3 m on both the x and the y axis.
Setup nodes and mobility models
Nodes and mobility models are configured as usual, however in order to use them with the buildings model
you need an additional call to BuildingsHelper::Install(), so as to let the mobility model include the
information on their position w.r.t. the buildings. Here is an example:
MobilityHelper mobility;
mobility.SetMobilityModel ("ns3::ConstantPositionMobilityModel");
ueNodes.Create (2);
mobility.Install (ueNodes);
BuildingsHelper::Install (ueNodes);
It is to be noted that any mobility model can be used. However, the user is advised to make sure that the
behavior of the mobility model being used is consistent with the presence of Buildings. For example,
using a simple random mobility over the whole simulation area in presence of buildings might easily
results in node moving in and out of buildings, regardless of the presence of walls.
One dedicated buildings-aware mobility model is the RandomWalk2dOutdoorMobilityModel. This class is
similar to the RandomWalk2dMobilityModel but avoids placing the trajectory on a path that would intersect
a building wall. If a boundary is encountered (either the bounding box or a building wall), the model
rebounds with a random direction and speed that ensures that the trajectory stays outside the buildings.
An example program that demonstrates the use of this model is the
src/buildings/examples/outdoor-random-walk-example.cc which has an associated shell script to plot the
traces generated. Another example program demonstrates how this outdoor mobility model can be used as
the basis of a group mobility model, with the outdoor buildings-aware model serving as the parent or
reference mobility model, and with additional nodes defining a child mobility model providing the offset
from the reference mobility model. This example,
src/buildings/example/outdoor-group-mobility-example.cc, also has an associated shell script
(outdoor-group-mobility-animate.sh) that can be used to generate an animated GIF of the group’s movement.
Place some nodes
You can place nodes in your simulation using several methods, which are described in the following.
Legacy positioning methods
Any legacy ns-3 positioning method can be used to place node in the simulation. The important additional
step is to For example, you can place nodes manually like this:
Ptr<ConstantPositionMobilityModel> mm0 = enbNodes.Get (0)->GetObject<ConstantPositionMobilityModel> ();
Ptr<ConstantPositionMobilityModel> mm1 = enbNodes.Get (1)->GetObject<ConstantPositionMobilityModel> ();
mm0->SetPosition (Vector (5.0, 5.0, 1.5));
mm1->SetPosition (Vector (30.0, 40.0, 1.5));
MobilityHelper mobility;
mobility.SetMobilityModel ("ns3::ConstantPositionMobilityModel");
ueNodes.Create (2);
mobility.Install (ueNodes);
BuildingsHelper::Install (ueNodes);
mm0->SetPosition (Vector (5.0, 5.0, 1.5));
mm1->SetPosition (Vector (30.0, 40.0, 1.5));
Alternatively, you could use any existing PositionAllocator class. The coordinates of the node will
determine whether it is placed outdoor or indoor and, if indoor, in which building and room it is placed.
Building-specific positioning methods
The following position allocator classes are available to place node in special positions with respect to
buildings:
• RandomBuildingPositionAllocator: Allocate each position by randomly choosing a building from the
list of all buildings, and then randomly choosing a position inside the building.
• RandomRoomPositionAllocator: Allocate each position by randomly choosing a room from the list of
rooms in all buildings, and then randomly choosing a position inside the room.
• SameRoomPositionAllocator: Walks a given NodeContainer sequentially, and for each node allocate a
new position randomly in the same room of that node.
• FixedRoomPositionAllocator: Generate a random position uniformly distributed in the volume of a
chosen room inside a chosen building.
Making the Mobility Model Consistent for a node
Initially, a mobility model of a node is made consistent when a node is initialized, which eventually
triggers a call to the DoInitialize method of the MobilityBuildingInfo` class. In particular, it calls
the MakeMobilityModelConsistent method, which goes through the lists of all buildings, determine if the
node is indoor or outdoor, and if indoor it also determines the building in which the node is located and
the corresponding floor number inside the building. Moreover, this method also caches the position of the
node, which is used to make the mobility model consistent for a moving node whenever the IsInside method
of MobilityBuildingInfo class is called.
Building-aware pathloss model
After you placed buildings and nodes in a simulation, you can use a building-aware pathloss model in a
simulation exactly in the same way you would use any regular path loss model. How to do this is specific
for the wireless module that you are considering (lte, wifi, wimax, etc.), so please refer to the
documentation of that model for specific instructions.
Building-aware channel condition models
The class BuildingsChannelConditionModel implements a channel condition model which determines the
LOS/NLOS channel state based on the buildings deployed in the scenario.
The classes ThreeGppV2vUrbanChannelConditionModel and ThreeGppV2vHighwayChannelConditionModel implement
hybrid channel condition models, specifically designed to model vehicular environments. More information
can be found in the documentation of the propagation module.
Main configurable attributes
The Building class has the following configurable parameters:
• building type: Residential, Office and Commercial.
• external walls type: Wood, ConcreteWithWindows, ConcreteWithoutWindows and StoneBlocks.
• building bounds: a Box class with the building bounds.
• number of floors.
• number of rooms in x-axis and y-axis (rooms can be placed only in a grid way).
The BuildingMobilityLossModel parameter configurable with the ns3 attribute system is represented by the
bound (string Bounds) of the simulation area by providing a Box class with the area bounds. Moreover, by
means of its methods the following parameters can be configured:
• the number of floor the node is placed (default 0).
• the position in the rooms grid.
The BuildingPropagationLossModel class has the following configurable parameters configurable with the
attribute system:
• Frequency: reference frequency (default 2160 MHz), note that by setting the frequency the wavelength is
set accordingly automatically and viceversa).
• Lambda: the wavelength (0.139 meters, considering the above frequency).
• ShadowSigmaOutdoor: the standard deviation of the shadowing for outdoor nodes (defaul 7.0).
• ShadowSigmaIndoor: the standard deviation of the shadowing for indoor nodes (default 8.0).
• ShadowSigmaExtWalls: the standard deviation of the shadowing due to external walls penetration for
outdoor to indoor communications (default 5.0).
• RooftopLevel: the level of the rooftop of the building in meters (default 20 meters).
• Los2NlosThr: the value of distance of the switching point between line-of-sigth and non-line-of-sight
propagation model in meters (default 200 meters).
• ITU1411DistanceThr: the value of distance of the switching point between short range (ITU 1211)
communications and long range (Okumura Hata) in meters (default 200 meters).
• MinDistance: the minimum distance in meters between two nodes for evaluating the pathloss (considered
neglictible before this threshold) (default 0.5 meters).
• Environment: the environment scenario among Urban, SubUrban and OpenAreas (default Urban).
• CitySize: the dimension of the city among Small, Medium, Large (default Large).
In order to use the hybrid mode, the class to be used is the HybridBuildingMobilityLossModel, which
allows the selection of the proper pathloss model according to the pathloss logic presented in the design
chapter. However, this solution has the problem that the pathloss model switching points might present
discontinuities due to the different characteristics of the model. This implies that according to the
specific scenario, the threshold used for switching have to be properly tuned. The simple
OhBuildingMobilityLossModel overcome this problem by using only the Okumura Hata model and the wall
penetration losses.
Testing Documentation
Overview
To test and validate the ns-3 Building Pathloss module, some test suites is provided which are integrated
with the ns-3 test framework. To run them, you need to have configured the build of the simulator in this
way:
$ ./waf configure --enable-tests --enable-modules=buildings
$ ./test.py
The above will run not only the test suites belonging to the buildings module, but also those belonging
to all the other ns-3 modules on which the buildings module depends. See the ns-3 manual for generic
information on the testing framework.
You can get a more detailed report in HTML format in this way:
$ ./test.py -w results.html
After the above command has run, you can view the detailed result for each test by opening the file
results.html with a web browser.
You can run each test suite separately using this command:
$ ./test.py -s test-suite-name
For more details about test.py and the ns-3 testing framework, please refer to the ns-3 manual.
Description of the test suites
BuildingsHelper test
The test suite buildings-helper checks that the method BuildingsHelper::MakeAllInstancesConsistent ()
works properly, i.e., that the BuildingsHelper is successful in locating if nodes are outdoor or indoor,
and if indoor that they are located in the correct building, room and floor. Several test cases are
provided with different buildings (having different size, position, rooms and floors) and different node
positions. The test passes if each every node is located correctly.
BuildingPositionAllocator test
The test suite building-position-allocator feature two test cases that check that respectively
RandomRoomPositionAllocator and SameRoomPositionAllocator work properly. Each test cases involves a
single 2x3x2 room building (total 12 rooms) at known coordinates and respectively 24 and 48 nodes. Both
tests check that the number of nodes allocated in each room is the expected one and that the position of
the nodes is also correct.
Buildings Pathloss tests
The test suite buildings-pathloss-model provides different unit tests that compare the expected results
of the buildings pathloss module in specific scenarios with pre calculated values obtained offline with
an Octave script (test/reference/buildings-pathloss.m). The tests are considered passed if the two values
are equal up to a tolerance of 0.1, which is deemed appropriate for the typical usage of pathloss values
(which are in dB).
In the following we detailed the scenarios considered, their selection has been done for covering the
wide set of possible pathloss logic combinations. The pathloss logic results therefore implicitly tested.
Test #1 Okumura Hata
In this test we test the standard Okumura Hata model; therefore both eNB and UE are placed outside at a
distance of 2000 m. The frequency used is the E-UTRA band #5, which correspond to 869 MHz (see table
5.5-1 of 36.101). The test includes also the validation of the areas extensions (i.e., urban, suburban
and open-areas) and of the city size (small, medium and large).
Test #2 COST231 Model
This test is aimed at validating the COST231 model. The test is similar to the Okumura Hata one, except
that the frequency used is the EUTRA band #1 (2140 MHz) and that the test can be performed only for large
and small cities in urban scenarios due to model limitations.
Test #3 2.6 GHz model
This test validates the 2.6 GHz Kun model. The test is similar to Okumura Hata one except that the
frequency is the EUTRA band #7 (2620 MHz) and the test can be performed only in urban scenario.
Test #4 ITU1411 LoS model
This test is aimed at validating the ITU1411 model in case of line of sight within street canyons
transmissions. In this case the UE is placed at 100 meters far from the eNB, since the threshold for
switching between LoS and NLoS is left to default one (i.e., 200 m.).
Test #5 ITU1411 NLoS model
This test is aimed at validating the ITU1411 model in case of non line of sight over the rooftop
transmissions. In this case the UE is placed at 900 meters far from the eNB, in order to be above the
threshold for switching between LoS and NLoS is left to default one (i.e., 200 m.).
Test #6 ITUP1238 model
This test is aimed at validating the ITUP1238 model in case of indoor transmissions. In this case both
the UE and the eNB are placed in a residential building with walls made of concrete with windows. Ue is
placed at the second floor and distances 30 meters far from the eNB, which is placed at the first floor.
Test #7 Outdoor -> Indoor with Okumura Hata model
This test validates the outdoor to indoor transmissions for large distances. In this case the UE is
placed in a residential building with wall made of concrete with windows and distances 2000 meters from
the outdoor eNB.
Test #8 Outdoor -> Indoor with ITU1411 model
This test validates the outdoor to indoor transmissions for short distances. In this case the UE is
placed in a residential building with walls made of concrete with windows and distances 100 meters from
the outdoor eNB.
Test #9 Indoor -> Outdoor with ITU1411 model
This test validates the outdoor to indoor transmissions for very short distances. In this case the eNB is
placed in the second floor of a residential building with walls made of concrete with windows and
distances 100 meters from the outdoor UE (i.e., LoS communication). Therefore the height gain has to be
included in the pathloss evaluation.
Test #10 Indoor -> Outdoor with ITU1411 model
This test validates the outdoor to indoor transmissions for short distances. In this case the eNB is
placed in the second floor of a residential building with walls made of concrete with windows and
distances 500 meters from the outdoor UE (i.e., NLoS communication). Therefore the height gain has to be
included in the pathloss evaluation.
Buildings Shadowing Test
The test suite buildings-shadowing-test is a unit test intended to verify the statistical distribution of
the shadowing model implemented by BuildingsPathlossModel. The shadowing is modeled according to a normal
distribution with mean \mu = 0 and variable standard deviation \sigma, according to models commonly used
in literature. Three test cases are provided, which cover the cases of indoor, outdoor and
indoor-to-outdoor communications. Each test case generates 1000 different samples of shadowing for
different pairs of MobilityModel instances in a given scenario. Shadowing values are obtained by
subtracting from the total loss value returned by HybridBuildingsPathlossModel the path loss component
which is constant and pre-determined for each test case. The test verifies that the sample mean and
sample variance of the shadowing values fall within the 99% confidence interval of the sample mean and
sample variance. The test also verifies that the shadowing values returned at successive times for the
same pair of MobilityModel instances is constant.
Buildings Channel Condition Model Test
The BuildingsChannelConditionModelTestSuite tests the class BuildingsChannelConditionModel. It checks if
the channel condition between two nodes is correctly determined when a building is deployed.
References
[turkmani]
Turkmani A.M.D., J.D. Parson and D.G. Lewis, “Radio propagation into buildings at 441, 900 and 1400
MHz”, in Proc. of 4th Int. Conference on Land Mobile Radio, 1987.
CLICK MODULAR ROUTER INTEGRATION
Click is a software architecture for building configurable routers. By using different combinations of
packet processing units called elements, a Click router can be made to perform a specific kind of
functionality. This flexibility provides a good platform for testing and experimenting with different
protocols.
Model Description
The source code for the Click model lives in the directory src/click.
Design
ns-3’s design is well suited for an integration with Click due to the following reasons:
• Packets in ns-3 are serialised/deserialised as they move up/down the stack. This allows ns-3 packets to
be passed to and from Click as they are.
• This also means that any kind of ns-3 traffic generator and transport should work easily on top of
Click.
• By striving to implement click as an Ipv4RoutingProtocol instance, we can avoid significant changes to
the LL and MAC layer of the ns-3 code.
The design goal was to make the ns-3-click public API simple enough such that the user needs to merely
add an Ipv4ClickRouting instance to the node, and inform each Click node of the Click configuration file
(.click file) that it is to use.
This model implements the interface to the Click Modular Router and provides the Ipv4ClickRouting class
to allow a node to use Click for external routing. Unlike normal Ipv4RoutingProtocol sub types,
Ipv4ClickRouting doesn’t use a RouteInput() method, but instead, receives a packet on the appropriate
interface and processes it accordingly. Note that you need to have a routing table type element in your
Click graph to use Click for external routing. This is needed by the RouteOutput() function inherited
from Ipv4RoutingProtocol. Furthermore, a Click based node uses a different kind of L3 in the form of
Ipv4L3ClickProtocol, which is a trimmed down version of Ipv4L3Protocol. Ipv4L3ClickProtocol passes on
packets passing through the stack to Ipv4ClickRouting for processing.
Developing a Simulator API to allow ns-3 to interact with Click
Much of the API is already well defined, which allows Click to probe for information from the simulator
(like a Node’s ID, an Interface ID and so forth). By retaining most of the methods, it should be possible
to write new implementations specific to ns-3 for the same functionality.
Hence, for the Click integration with ns-3, a class named Ipv4ClickRouting will handle the interaction
with Click. The code for the same can be found in src/click/model/ipv4-click-routing.{cc,h}.
Packet hand off between ns-3 and Click
There are four kinds of packet hand-offs that can occur between ns-3 and Click.
• L4 to L3
• L3 to L4
• L3 to L2
• L2 to L3
To overcome this, we implement Ipv4L3ClickProtocol, a stripped down version of Ipv4L3Protocol.
Ipv4L3ClickProtocol passes packets to and from Ipv4ClickRouting appropriately to perform routing.
Scope and Limitations
• In its current state, the NS-3 Click Integration is limited to use only with L3, leaving NS-3 to handle
L2. We are currently working on adding Click MAC support as well. See the usage section to make sure
that you design your Click graphs accordingly.
• Furthermore, ns-3-click will work only with userlevel elements. The complete list of elements are
available at http://read.cs.ucla.edu/click/elements. Elements that have ‘all’, ‘userlevel’ or ‘ns’
mentioned beside them may be used.
• As of now, the ns-3 interface to Click is Ipv4 only. We will be adding Ipv6 support in the future.
References
• Eddie Kohler, Robert Morris, Benjie Chen, John Jannotti, and M. Frans Kaashoek. The click modular
router. ACM Transactions on Computer Systems 18(3), August 2000, pages 263-297.
• Lalith Suresh P., and Ruben Merz. Ns-3-click: click modular router integration for ns-3. In Proc. of
3rd International ICST Workshop on NS-3 (WNS3), Barcelona, Spain. March, 2011.
• Michael Neufeld, Ashish Jain, and Dirk Grunwald. Nsclick: bridging network simulation and deployment.
MSWiM ‘02: Proceedings of the 5th ACM international workshop on Modeling analysis and simulation of
wireless and mobile systems, 2002, Atlanta, Georgia, USA. http://doi.acm.org/10.1145/570758.570772
Usage
Building Click
The first step is to clone Click from the github repository and build it:
$ git clone https://github.com/kohler/click
$ cd click/
$ ./configure --disable-linuxmodule --enable-nsclick --enable-wifi
$ make
The –enable-wifi flag may be skipped if you don’t intend on using Click with Wifi. * Note: You don’t
need to do a ‘make install’.
Once Click has been built successfully, change into the ns-3 directory and configure ns-3 with Click
Integration support:
$ ./waf configure --enable-examples --enable-tests --with-nsclick=/path/to/click/source
Hint: If you have click installed one directory above ns-3 (such as in the ns-3-allinone directory), and
the name of the directory is ‘click’ (or a symbolic link to the directory is named ‘click’), then the
–with-nsclick specifier is not necessary; the ns-3 build system will successfully find the directory.
If it says ‘enabled’ beside ‘NS-3 Click Integration Support’, then you’re good to go. Note: If running
modular ns-3, the minimum set of modules required to run all ns-3-click examples is wifi, csma and
config-store.
Next, try running one of the examples:
$ ./waf --run nsclick-simple-lan
You may then view the resulting .pcap traces, which are named nsclick-simple-lan-0-0.pcap and
nsclick-simple-lan-0-1.pcap.
Click Graph Instructions
The following should be kept in mind when making your Click graph:
• Only userlevel elements can be used.
• You will need to replace FromDevice and ToDevice elements with FromSimDevice and ToSimDevice elements.
• Packets to the kernel are sent up using ToSimDevice(tap0,IP).
• For any node, the device which sends/receives packets to/from the kernel, is named ‘tap0’. The
remaining interfaces should be named eth0, eth1 and so forth (even if you’re using wifi). Please note
that the device numbering should begin from 0. In future, this will be made flexible so that users can
name devices in their Click file as they wish.
• A routing table element is a mandatory. The OUTports of the routing table element should correspond to
the interface number of the device through which the packet will ultimately be sent out. Violating this
rule will lead to really weird packet traces. This routing table element’s name should then be passed
to the Ipv4ClickRouting protocol object as a simulation parameter. See the Click examples for details.
• The current implementation leaves Click with mainly L3 functionality, with ns-3 handling L2. We will
soon begin working to support the use of MAC protocols on Click as well. This means that as of now,
Click’s Wifi specific elements cannot be used with ns-3.
Debugging Packet Flows from Click
From any point within a Click graph, you may use the Print (http://read.cs.ucla.edu/click/elements/print)
element and its variants for pretty printing of packet contents. Furthermore, you may generate pcap
traces of packets flowing through a Click graph by using the ToDump (‐
http://read.cs.ucla.edu/click/elements/todump) element as well. For instance:
myarpquerier
-> Print(fromarpquery,64)
-> ToDump(out_arpquery,PER_NODE 1)
-> ethout;
and …will print the contents of packets that flow out of the ArpQuerier, then generate a pcap trace file
which will have a suffix ‘out_arpquery’, for each node using the Click file, before pushing packets onto
‘ethout’.
Helper
To have a node run Click, the easiest way would be to use the ClickInternetStackHelper class in your
simulation script. For instance:
ClickInternetStackHelper click;
click.SetClickFile (myNodeContainer, "nsclick-simple-lan.click");
click.SetRoutingTableElement (myNodeContainer, "u/rt");
click.Install (myNodeContainer);
The example scripts inside src/click/examples/ demonstrate the use of Click based nodes in different
scenarios. The helper source can be found inside src/click/helper/click-internet-stack-helper.{h,cc}
Examples
The following examples have been written, which can be found in src/click/examples/:
• nsclick-simple-lan.cc and nsclick-raw-wlan.cc: A Click based node communicating with a normal ns-3 node
without Click, using Csma and Wifi respectively. It also demonstrates the use of TCP on top of Click,
something which the original nsclick implementation for NS-2 couldn’t achieve.
• nsclick-udp-client-server-csma.cc and nsclick-udp-client-server-wifi.cc: A 3 node LAN (Csma and Wifi
respectively) wherein 2 Click based nodes run a UDP client, that sends packets to a third Click based
node running a UDP server.
• nsclick-routing.cc: One Click based node communicates to another via a third node that acts as an IP
router (using the IP router Click configuration). This demonstrates routing using Click.
Scripts are available within <click-dir>/conf/ that allow you to generate Click files for some common
scenarios. The IP Router used in nsclick-routing.cc was generated from the make-ip-conf.pl file and
slightly adapted to work with ns-3-click.
Validation
This model has been tested as follows:
• Unit tests have been written to verify the internals of Ipv4ClickRouting. This can be found in
src/click/ipv4-click-routing-test.cc. These tests verify whether the methods inside Ipv4ClickRouting
which deal with Device name to ID, IP Address from device name and Mac Address from device name
bindings work as expected.
• The examples have been used to test Click with actual simulation scenarios. These can be found in
src/click/examples/. These tests cover the following: the use of different kinds of transports on top
of Click, TCP/UDP, whether Click nodes can communicate with non-Click based nodes, whether Click nodes
can communicate with each other, using Click to route packets using static routing.
• Click has been tested with Csma, Wifi and Point-to-Point devices. Usage instructions are available in
the preceding section.
CSMA NETDEVICE
This is the introduction to CSMA NetDevice chapter, to complement the CSMA model doxygen.
Overview of the CSMA model
The ns-3 CSMA device models a simple bus network in the spirit of Ethernet. Although it does not model
any real physical network you could ever build or buy, it does provide some very useful functionality.
Typically when one thinks of a bus network Ethernet or IEEE 802.3 comes to mind. Ethernet uses CSMA/CD
(Carrier Sense Multiple Access with Collision Detection with exponentially increasing backoff to contend
for the shared transmission medium. The ns-3 CSMA device models only a portion of this process, using the
nature of the globally available channel to provide instantaneous (faster than light) carrier sense and
priority-based collision “avoidance.” Collisions in the sense of Ethernet never happen and so the ns-3
CSMA device does not model collision detection, nor will any transmission in progress be “jammed.”
CSMA Layer Model
There are a number of conventions in use for describing layered communications architectures in the
literature and in textbooks. The most common layering model is the ISO seven layer reference model. In
this view the CsmaNetDevice and CsmaChannel pair occupies the lowest two layers – at the physical (layer
one), and data link (layer two) positions. Another important reference model is that specified by RFC
1122, “Requirements for Internet Hosts – Communication Layers.” In this view the CsmaNetDevice and
CsmaChannel pair occupies the lowest layer – the link layer. There is also a seemingly endless litany of
alternative descriptions found in textbooks and in the literature. We adopt the naming conventions used
in the IEEE 802 standards which speak of LLC, MAC, MII and PHY layering. These acronyms are defined as:
• LLC: Logical Link Control;
• MAC: Media Access Control;
• MII: Media Independent Interface;
• PHY: Physical Layer.
In this case the LLC and MAC are sublayers of the OSI data link layer and the MII and PHY are sublayers
of the OSI physical layer.
The “top” of the CSMA device defines the transition from the network layer to the data link layer. This
transition is performed by higher layers by calling either CsmaNetDevice::Send or
CsmaNetDevice::SendFrom.
In contrast to the IEEE 802.3 standards, there is no precisely specified PHY in the CSMA model in the
sense of wire types, signals or pinouts. The “bottom” interface of the CsmaNetDevice can be thought of as
as a kind of Media Independent Interface (MII) as seen in the “Fast Ethernet” (IEEE 802.3u)
specifications. This MII interface fits into a corresponding media independent interface on the
CsmaChannel. You will not find the equivalent of a 10BASE-T or a 1000BASE-LX PHY.
The CsmaNetDevice calls the CsmaChannel through a media independent interface. There is a method defined
to tell the channel when to start “wiggling the wires” using the method CsmaChannel::TransmitStart, and a
method to tell the channel when the transmission process is done and the channel should begin propagating
the last bit across the “wire”: CsmaChannel::TransmitEnd.
When the TransmitEnd method is executed, the channel will model a single uniform signal propagation delay
in the medium and deliver copes of the packet to each of the devices attached to the packet via the
CsmaNetDevice::Receive method.
There is a “pin” in the device media independent interface corresponding to “COL” (collision). The state
of the channel may be sensed by calling CsmaChannel::GetState. Each device will look at this “pin” before
starting a send and will perform appropriate backoff operations if required.
Properly received packets are forwarded up to higher levels from the CsmaNetDevice via a callback
mechanism. The callback function is initialized by the higher layer (when the net device is attached)
using CsmaNetDevice::SetReceiveCallback and is invoked upon “proper” reception of a packet by the net
device in order to forward the packet up the protocol stack.
CSMA Channel Model
The class CsmaChannel models the actual transmission medium. There is no fixed limit for the number of
devices connected to the channel. The CsmaChannel models a data rate and a speed-of-light delay which can
be accessed via the attributes “DataRate” and “Delay” respectively. The data rate provided to the channel
is used to set the data rates used by the transmitter sections of the CSMA devices connected to the
channel. There is no way to independently set data rates in the devices. Since the data rate is only used
to calculate a delay time, there is no limitation (other than by the data type holding the value) on the
speed at which CSMA channels and devices can operate; and no restriction based on any kind of PHY
characteristics.
The CsmaChannel has three states, IDLE, TRANSMITTING and PROPAGATING. These three states are “seen”
instantaneously by all devices on the channel. By this we mean that if one device begins or ends a
simulated transmission, all devices on the channel are immediately aware of the change in state. There is
no time during which one device may see an IDLE channel while another device physically further away in
the collision domain may have begun transmitting with the associated signals not propagated down the
channel to other devices. Thus there is no need for collision detection in the CsmaChannel model and it
is not implemented in any way.
We do, as the name indicates, have a Carrier Sense aspect to the model. Since the simulator is single
threaded, access to the common channel will be serialized by the simulator. This provides a deterministic
mechanism for contending for the channel. The channel is allocated (transitioned from state IDLE to state
TRANSMITTING) on a first-come first-served basis. The channel always goes through a three state process:
IDLE -> TRANSMITTING -> PROPAGATING -> IDLE
The TRANSMITTING state models the time during which the source net device is actually wiggling the
signals on the wire. The PROPAGATING state models the time after the last bit was sent, when the signal
is propagating down the wire to the “far end.”
The transition to the TRANSMITTING state is driven by a call to CsmaChannel::TransmitStart which is
called by the net device that transmits the packet. It is the responsibility of that device to end the
transmission with a call to CsmaChannel::TransmitEnd at the appropriate simulation time that reflects the
time elapsed to put all of the packet bits on the wire. When TransmitEnd is called, the channel schedules
an event corresponding to a single speed-of-light delay. This delay applies to all net devices on the
channel identically. You can think of a symmetrical hub in which the packet bits propagate to a central
location and then back out equal length cables to the other devices on the channel. The single “speed of
light” delay then corresponds to the time it takes for: 1) a signal to propagate from one CsmaNetDevice
through its cable to the hub; plus 2) the time it takes for the hub to forward the packet out a port;
plus 3) the time it takes for the signal in question to propagate to the destination net device.
The CsmaChannel models a broadcast medium so the packet is delivered to all of the devices on the channel
(including the source) at the end of the propagation time. It is the responsibility of the sending device
to determine whether or not it receives a packet broadcast over the channel.
The CsmaChannel provides following Attributes:
• DataRate: The bitrate for packet transmission on connected devices;
• Delay: The speed of light transmission delay for the channel.
CSMA Net Device Model
The CSMA network device appears somewhat like an Ethernet device. The CsmaNetDevice provides following
Attributes:
• Address: The Mac48Address of the device;
• SendEnable: Enable packet transmission if true;
• ReceiveEnable: Enable packet reception if true;
• EncapsulationMode: Type of link layer encapsulation to use;
• RxErrorModel: The receive error model;
• TxQueue: The transmit queue used by the device;
• InterframeGap: The optional time to wait between “frames”;
• Rx: A trace source for received packets;
• Drop: A trace source for dropped packets.
The CsmaNetDevice supports the assignment of a “receive error model.” This is an ErrorModel object that
is used to simulate data corruption on the link.
Packets sent over the CsmaNetDevice are always routed through the transmit queue to provide a trace hook
for packets sent out over the network. This transmit queue can be set (via attribute) to model different
queuing strategies.
Also configurable by attribute is the encapsulation method used by the device. Every packet gets an
EthernetHeader that includes the destination and source MAC addresses, and a length/type field. Every
packet also gets an EthernetTrailer which includes the FCS. Data in the packet may be encapsulated in
different ways.
By default, or by setting the “EncapsulationMode” attribute to “Dix”, the encapsulation is according to
the DEC, Intel, Xerox standard. This is sometimes called EthernetII framing and is the familiar
destination MAC, source MAC, EtherType, Data, CRC format.
If the “EncapsulationMode” attribute is set to “Llc”, the encapsulation is by LLC SNAP. In this case, a
SNAP header is added that contains the EtherType (IP or ARP).
The other implemented encapsulation modes are IP_ARP (set “EncapsulationMode” to “IpArp”) in which the
length type of the Ethernet header receives the protocol number of the packet; or ETHERNET_V1 (set
“EncapsulationMode” to “EthernetV1”) in which the length type of the Ethernet header receives the length
of the packet. A “Raw” encapsulation mode is defined but not implemented – use of the RAW mode results
in an assertion.
Note that all net devices on a channel must be set to the same encapsulation mode for correct results.
The encapsulation mode is not sensed at the receiver.
The CsmaNetDevice implements a random exponential backoff algorithm that is executed if the channel is
determined to be busy (TRANSMITTING or PPROPAGATING) when the device wants to start propagating. This
results in a random delay of up to pow (2, retries) - 1 microseconds before a retry is attempted. The
default maximum number of retries is 1000.
Using the CsmaNetDevice
The CSMA net devices and channels are typically created and configured using the associated CsmaHelper
object. The various ns-3 device helpers generally work in a similar way, and their use is seen in many
of our example programs.
The conceptual model of interest is that of a bare computer “husk” into which you plug net devices. The
bare computers are created using a NodeContainer helper. You just ask this helper to create as many
computers (we call them Nodes) as you need on your network:
NodeContainer csmaNodes;
csmaNodes.Create (nCsmaNodes);
Once you have your nodes, you need to instantiate a CsmaHelper and set any attributes you may want to
change.:
CsmaHelper csma;
csma.SetChannelAttribute ("DataRate", StringValue ("100Mbps"));
csma.SetChannelAttribute ("Delay", TimeValue (NanoSeconds (6560)));
csma.SetDeviceAttribute ("EncapsulationMode", StringValue ("Dix"));
csma.SetDeviceAttribute ("FrameSize", UintegerValue (2000));
Once the attributes are set, all that remains is to create the devices and install them on the required
nodes, and to connect the devices together using a CSMA channel. When we create the net devices, we add
them to a container to allow you to use them in the future. This all takes just one line of code.:
NetDeviceContainer csmaDevices = csma.Install (csmaNodes);
We recommend thinking carefully about changing these Attributes, since it can result in behavior that
surprises users. We allow this because we believe flexibility is important. As an example of a possibly
surprising effect of changing Attributes, consider the following:
The Mtu Attribute indicates the Maximum Transmission Unit to the device. This is the size of the largest
Protocol Data Unit (PDU) that the device can send. This Attribute defaults to 1500 bytes and corresponds
to a number found in RFC 894, “A Standard for the Transmission of IP Datagrams over Ethernet Networks.”
The number is actually derived from the maximum packet size for 10Base5 (full-spec Ethernet) networks –
1518 bytes. If you subtract DIX encapsulation overhead for Ethernet packets (18 bytes) you will end up
with a maximum possible data size (MTU) of 1500 bytes. One can also find that the MTU for IEEE 802.3
networks is 1492 bytes. This is because LLC/SNAP encapsulation adds an extra eight bytes of overhead to
the packet. In both cases, the underlying network hardware is limited to 1518 bytes, but the MTU is
different because the encapsulation is different.
If one leaves the Mtu Attribute at 1500 bytes and changes the encapsulation mode Attribute to Llc, the
result will be a network that encapsulates 1500 byte PDUs with LLC/SNAP framing resulting in packets of
1526 bytes. This would be illegal in many networks, but we allow you do do this. This results in a
simulation that quite subtly does not reflect what you might be expecting since a real device would balk
at sending a 1526 byte packet.
There also exist jumbo frames (1500 < MTU <= 9000 bytes) and super-jumbo (MTU > 9000 bytes) frames that
are not officially sanctioned by IEEE but are available in some high-speed (Gigabit) networks and NICs.
In the CSMA model, one could leave the encapsulation mode set to Dix, and set the Mtu to 64000 bytes –
even though an associated CsmaChannel DataRate was left at 10 megabits per second (certainly not Gigabit
Ethernet). This would essentially model an Ethernet switch made out of vampire-tapped 1980s-style
10Base5 networks that support super-jumbo datagrams, which is certainly not something that was ever made,
nor is likely to ever be made; however it is quite easy for you to configure.
Be careful about assumptions regarding what CSMA is actually modelling and how configuration (Attributes)
may allow you to swerve considerably away from reality.
CSMA Tracing
Like all ns-3 devices, the CSMA Model provides a number of trace sources. These trace sources can be
hooked using your own custom trace code, or you can use our helper functions to arrange for tracing to be
enabled on devices you specify.
Upper-Level (MAC) Hooks
From the point of view of tracing in the net device, there are several interesting points to insert trace
hooks. A convention inherited from other simulators is that packets destined for transmission onto
attached networks pass through a single “transmit queue” in the net device. We provide trace hooks at
this point in packet flow, which corresponds (abstractly) only to a transition from the network to data
link layer, and call them collectively the device MAC hooks.
When a packet is sent to the CSMA net device for transmission it always passes through the transmit
queue. The transmit queue in the CsmaNetDevice inherits from Queue, and therefore inherits three trace
sources:
• An Enqueue operation source (see Queue::m_traceEnqueue);
• A Dequeue operation source (see Queue::m_traceDequeue);
• A Drop operation source (see Queue::m_traceDrop).
The upper-level (MAC) trace hooks for the CsmaNetDevice are, in fact, exactly these three trace sources
on the single transmit queue of the device.
The m_traceEnqueue event is triggered when a packet is placed on the transmit queue. This happens at the
time that CsmaNetDevice::Send or CsmaNetDevice::SendFrom is called by a higher layer to queue a packet
for transmission.
The m_traceDequeue event is triggered when a packet is removed from the transmit queue. Dequeues from the
transmit queue can happen in three situations: 1) If the underlying channel is idle when the
CsmaNetDevice::Send or CsmaNetDevice::SendFrom is called, a packet is dequeued from the transmit queue
and immediately transmitted; 2) If the underlying channel is idle, a packet may be dequeued and
immediately transmitted in an internal TransmitCompleteEvent that functions much like a transmit complete
interrupt service routine; or 3) from the random exponential backoff handler if a timeout is detected.
Case (3) implies that a packet is dequeued from the transmit queue if it is unable to be transmitted
according to the backoff rules. It is important to understand that this will appear as a Dequeued packet
and it is easy to incorrectly assume that the packet was transmitted since it passed through the transmit
queue. In fact, a packet is actually dropped by the net device in this case. The reason for this behavior
is due to the definition of the Queue Drop event. The m_traceDrop event is, by definition, fired when a
packet cannot be enqueued on the transmit queue because it is full. This event only fires if the queue is
full and we do not overload this event to indicate that the CsmaChannel is “full.”
Lower-Level (PHY) Hooks
Similar to the upper level trace hooks, there are trace hooks available at the lower levels of the net
device. We call these the PHY hooks. These events fire from the device methods that talk directly to the
CsmaChannel.
The trace source m_dropTrace is called to indicate a packet that is dropped by the device. This happens
in two cases: First, if the receive side of the net device is not enabled (see
CsmaNetDevice::m_receiveEnable and the associated attribute “ReceiveEnable”).
The m_dropTrace is also used to indicate that a packet was discarded as corrupt if a receive error model
is used (see CsmaNetDevice::m_receiveErrorModel and the associated attribute “ReceiveErrorModel”).
The other low-level trace source fires on reception of an accepted packet (see CsmaNetDevice::m_rxTrace).
A packet is accepted if it is destined for the broadcast address, a multicast address, or to the MAC
address assigned to the net device.
Summary
The ns3 CSMA model is a simplistic model of an Ethernet-like network. It supports a Carrier-Sense
function and allows for Multiple Access to a shared medium. It is not physical in the sense that the
state of the medium is instantaneously shared among all devices. This means that there is no collision
detection required in this model and none is implemented. There will never be a “jam” of a packet
already on the medium. Access to the shared channel is on a first-come first-served basis as determined
by the simulator scheduler. If the channel is determined to be busy by looking at the global state, a
random exponential backoff is performed and a retry is attempted.
Ns-3 Attributes provide a mechanism for setting various parameters in the device and channel such as
addresses, encapsulation modes and error model selection. Trace hooks are provided in the usual manner
with a set of upper level hooks corresponding to a transmit queue and used in ASCII tracing; and also a
set of lower level hooks used in pcap tracing.
Although the ns-3 CsmaChannel and CsmaNetDevice does not model any kind of network you could build or
buy, it does provide us with some useful functionality. You should, however, understand that it is
explicitly not Ethernet or any flavor of IEEE 802.3 but an interesting subset.
DSDV ROUTING
Destination-Sequenced Distance Vector (DSDV) routing protocol is a pro-active, table-driven routing
protocol for MANETs developed by Charles E. Perkins and Pravin Bhagwat in 1994. It uses the hop count as
metric in route selection.
This model was developed by the ResiliNets research group at the University of Kansas. A paper on this
model exists at this URL.
DSDV Routing Overview
DSDV Routing Table: Every node will maintain a table listing all the other nodes it has known either
directly or through some neighbors. Every node has a single entry in the routing table. The entry will
have information about the node’s IP address, last known sequence number and the hop count to reach that
node. Along with these details the table also keeps track of the nexthop neighbor to reach the
destination node, the timestamp of the last update received for that node.
The DSDV update message consists of three fields, Destination Address, Sequence Number and Hop Count.
Each node uses 2 mechanisms to send out the DSDV updates. They are,
1.
Periodic Updates
Periodic updates are sent out after every m_periodicUpdateInterval(default:15s). In this update
the node broadcasts out its entire routing table.
2.
Trigger Updates
Trigger Updates are small updates in-between the periodic updates. These updates are sent out
whenever a node receives a DSDV packet that caused a change in its routing table. The original
paper did not clearly mention when for what change in the table should a DSDV update be sent
out. The current implementation sends out an update irrespective of the change in the routing
table.
The updates are accepted based on the metric for a particular node. The first factor determining the
acceptance of an update is the sequence number. It has to accept the update if the sequence number of the
update message is higher irrespective of the metric. If the update with same sequence number is received,
then the update with least metric (hopCount) is given precedence.
In highly mobile scenarios, there is a high chance of route fluctuations, thus we have the concept of
weighted settling time where an update with change in metric will not be advertised to neighbors. The
node waits for the settling time to make sure that it did not receive the update from its old neighbor
before sending out that update.
The current implementation covers all the above features of DSDV. The current implementation also has a
request queue to buffer packets that have no routes to destination. The default is set to buffer up to 5
packets per destination.
References
Link to the Paper: http://portal.acm.org/citation.cfm?doid=190314.190336
DSR ROUTING
Dynamic Source Routing (DSR) protocol is a reactive routing protocol designed specifically for use in
multi-hop wireless ad hoc networks of mobile nodes.
This model was developed by the ResiliNets research group at the University of Kansas.
DSR Routing Overview
This model implements the base specification of the Dynamic Source Routing (DSR) protocol. Implementation
is based on RFC 4728, with some extensions and modifications to the RFC specifications.
DSR operates on a on-demand behavior. Therefore, our DSR model buffers all packets while a route request
packet (RREQ) is disseminated. We implement a packet buffer in dsr-rsendbuff.cc. The packet queue
implements garbage collection of old packets and a queue size limit. When the packet is sent out from the
send buffer, it will be queued in maintenance buffer for next hop acknowledgment.
The maintenance buffer then buffers the already sent out packets and waits for the notification of packet
delivery. Protocol operation strongly depends on broken link detection mechanism. We implement the three
heuristics recommended based the RFC as follows:
First, we use link layer feedback when possible, which is also the fastest mechanism of these three to
detect link errors. A link is considered to be broken if frame transmission results in a transmission
failure for all retries. This mechanism is meant for active links and works much faster than in its
absence. DSR is able to detect the link layer transmission failure and notify that as broken.
Recalculation of routes will be triggered when needed. If user does not want to use link layer
acknowledgment, it can be tuned by setting “LinkAcknowledgment” attribute to false in “dsr-routing.cc”.
Second, passive acknowledgment should be used whenever possible. The node turns on “promiscuous” receive
mode, in which it can receive packets not destined for itself, and when the node assures the delivery of
that data packet to its destination, it cancels the passive acknowledgment timer.
Last, we use a network layer acknowledge scheme to notify the receipt of a packet. Route request packet
will not be acknowledged or retransmitted.
The Route Cache implementation support garbage collection of old entries and state machine, as defined in
the standard. It implements as a STL map container. The key is the destination IP address.
DSR operates with direct access to IP header, and operates between network and transport layer. When
packet is sent out from transport layer, it passes itself to DSR and DSR header is appended.
We have two caching mechanisms: path cache and link cache. The path cache saves the whole path in the
cache. The paths are sorted based on the hop count, and whenever one path is not able to be used, we
change to the next path. The link cache is a slightly better design in the sense that it uses different
subpaths and uses Implemented Link Cache using Dijkstra algorithm, and this part is implemented by Song
Luan <lsuper@mail.ustc.edu.cn>.
The following optional protocol optimizations aren’t implemented:
• Flow state
• First Hop External (F), Last Hop External (L) flags
• Handling unknown DSR options
•
Two types of error headers:
1. flow state not supported option
2. unsupported option (not going to happen in simulation)
DSR update in ns-3.17
We originally used “TxErrHeader” in Ptr<WifiMac> to indicate the transmission error of a specific packet
in link layer, however, it was not working quite correctly since even when the packet was dropped, this
header was not recorded in the trace file. We used to a different path on implementing the link layer
notification mechanism. We look into the trace file by finding packet receive event. If we find one
receive event for the data packet, we count that as the indicator for successful data delivery.
Useful parameters
+------------------------- +------------------------------------+-------------+
| Parameter | Description | Default |
+==========================+====================================+=============+
| MaxSendBuffLen | Maximum number of packets that can | 64 |
| | be stored in send buffer | |
+------------------------- +------------------------------------+-------------+
| MaxSendBuffTime | Maximum time packets can be queued | Seconds(30) |
| | in the send buffer | |
+------------------------- +------------------------------------+-------------+
| MaxMaintLen | Maximum number of packets that can | 50 |
| | be stored in maintenance buffer | |
+------------------------- +------------------------------------+-------------+
| MaxMaintTime | Maximum time packets can be queued | Seconds(30) |
| | in maintenance buffer | |
+------------------------- +------------------------------------+-------------+
| MaxCacheLen | Maximum number of route entries | 64 |
| | that can be stored in route cache | |
+------------------------- +------------------------------------+-------------+
| RouteCacheTimeout | Maximum time the route cache can | Seconds(300)|
| | be queued in route cache | |
+------------------------- +------------------------------------+-------------+
| RreqRetries | Maximum number of retransmissions | 16 |
| | for request discovery of a route | |
+------------------------- +------------------------------------+-------------+
| CacheType | Use Link Cache or use Path Cache | "LinkCache" |
| | | |
+------------------------- +------------------------------------+-------------+
| LinkAcknowledgment | Enable Link layer acknowledgment | True |
| | mechanism | |
+------------------------- +------------------------------------+-------------+
Implementation modification
•
The DsrFsHeader has added 3 fields: message type, source id, destination id, and these changes only for
post-processing
1. Message type is used to identify the data packet from control packet
2. source id is used to identify the real source of the data packet since we have to deliver the
packet hop-by-hop and the Ipv4Header is not carrying the real source and destination ip
address as needed
3. destination id is for same reason of above
• Route Reply header is not word-aligned in DSR RFC, change it to word-aligned in implementation
• DSR works as a shim header between transport and network protocol, it needs its own forwarding
mechanism, we are changing the packet transmission to hop-by-hop delivery, so we added two fields in
dsr fixed header to notify packet delivery
Current Route Cache implementation
This implementation used “path cache”, which is simple to implement and ensures loop-free paths:
• the path cache has automatic expire policy
• the cache saves multiple route entries for a certain destination and sort the entries based on hop
counts
• the MaxEntriesEachDst can be tuned to change the maximum entries saved for a single destination
• when adding multiple routes for one destination, the route is compared based on hop-count and expire
time, the one with less hop count or relatively new route is favored
• Future implementation may include “link cache” as another possibility
DSR Instructions
The following should be kept in mind when running DSR as routing protocol:
• NodeTraversalTime is the time it takes to traverse two neighboring nodes and should be chosen to fit
the transmission range
• PassiveAckTimeout is the time a packet in maintenance buffer wait for passive acknowledgment, normally
set as two times of NodeTraversalTime
• RouteCacheTimeout should be set smaller value when the nodes’ velocity become higher. The default value
is 300s.
Helper
To have a node run DSR, the easiest way would be to use the DsrHelper and DsrMainHelpers in your
simulation script. For instance:
DsrHelper dsr;
DsrMainHelper dsrMain;
dsrMain.Install (dsr, adhocNodes);
The example scripts inside src/dsr/examples/ demonstrate the use of DSR based nodes in different
scenarios. The helper source can be found inside src/dsr/helper/dsr-main-helper.{h,cc} and
src/dsr/helper/dsr-helper.{h,cc}
Examples
The example can be found in src/dsr/examples/:
• dsr.cc use DSR as routing protocol within a traditional MANETs environment[3].
DSR is also built in the routing comparison case in examples/routing/:
• manet-routing-compare.cc is a comparison case with built in MANET routing protocols and can generate
its own results.
Validation
This model has been tested as follows:
• Unit tests have been written to verify the internals of DSR. This can be found in
src/dsr/test/dsr-test-suite.cc. These tests verify whether the methods inside DSR module which deal
with packet buffer, headers work correctly.
• Simulation cases similar to [3] have been tested and have comparable results.
• manet-routing-compare.cc has been used to compare DSR with three of other routing protocols.
A paper was presented on these results at the Workshop on ns-3 in 2011.
Limitations
The model is not fully compliant with RFC 4728. As an example, Dsr fixed size header has been extended
and it is four octets longer then the RFC specification. As a consequence, the DSR headers can not be
correctly decoded by Wireshark.
The model full compliance with the RFC is planned for the future.
References
[1] Original paper: http://www.monarch.cs.rice.edu/monarch-papers/dsr-chapter00.pdf
[2] RFC 4728 http://www6.ietf.org/rfc/rfc4728.txt
[3] Broch’s comparison paper: http://www.monarch.cs.rice.edu/monarch-papers/mobicom98.ps
EMULATION OVERVIEW
ns-3 has been designed for integration into testbed and virtual machine environments. We have addressed
this need by providing two kinds of net devices. The first kind of device is a file descriptor net
device (FdNetDevice), which is a generic device type that can read and write from a file descriptor. By
associating this file descriptor with different things on the host system, different capabilities can be
provided. For instance, the FdNetDevice can be associated with an underlying packet socket to provide
emulation capabilities. This allows ns-3 simulations to send data on a “real” network. The second kind,
called a TapBridge NetDevice allows a “real” host to participate in an ns-3 simulation as if it were one
of the simulated nodes. An ns-3 simulation may be constructed with any combination of simulated or
emulated devices.
Note: Prior to ns-3.17, the emulation capability was provided by a special device called an Emu
NetDevice; the Emu NetDevice has been replaced by the FdNetDevice.
One of the use-cases we want to support is that of a testbed. A concrete example of an environment of
this kind is the ORBIT testbed. ORBIT is a laboratory emulator/field trial network arranged as a two
dimensional grid of 400 802.11 radio nodes. We integrate with ORBIT by using their “imaging” process to
load and run ns-3 simulations on the ORBIT array. We can use our EmuFdNetDevice to drive the hardware in
the testbed and we can accumulate results either using the ns-3 tracing and logging functions, or the
native ORBIT data gathering techniques. See http://www.orbit-lab.org/ for details on the ORBIT testbed.
A simulation of this kind is shown in the following figure:
[image] Example Implementation of Testbed Emulation..UNINDENT
You can see that there are separate hosts, each running a subset of a “global” simulation. Instead of
an ns-3 channel connecting the hosts, we use real hardware provided by the testbed. This allows ns-3
applications and protocol stacks attached to a simulation node to communicate over real hardware.
We expect the primary use for this configuration will be to generate repeatable experimental results in
a real-world network environment that includes all of the ns-3 tracing, logging, visualization and
statistics gathering tools.
In what can be viewed as essentially an inverse configuration, we allow “real” machines running native
applications and protocol stacks to integrate with an ns-3 simulation. This allows for the simulation
of large networks connected to a real machine, and also enables virtualization. A simulation of this
kind is shown in the following figure:
[image] Implementation overview of emulated channel..UNINDENT
Here, you will see that there is a single host with a number of virtual machines running on it. An ns-3
simulation is shown running in the virtual machine shown in the center of the figure. This simulation
has a number of nodes with associated ns-3 applications and protocol stacks that are talking to an ns-3
channel through native simulated ns-3 net devices.
There are also two virtual machines shown at the far left and far right of the figure. These VMs are
running native (Linux) applications and protocol stacks. The VM is connected into the simulation by a
Linux Tap net device. The user-mode handler for the Tap device is instantiated in the simulation and
attached to a proxy node that represents the native VM in the simulation. These handlers allow the Tap
devices on the native VMs to behave as if they were ns-3 net devices in the simulation VM. This, in
turn, allows the native software and protocol suites in the native VMs to believe that they are
connected to the simulated ns-3 channel.
We expect the typical use case for this environment will be to analyze the behavior of native
applications and protocol suites in the presence of large simulated ns-3 networks.
The basic testbed mode of emulation uses raw sockets. Two other variants (netmap-based and DPDK-based
emulation) have been recently added; these make use of more recent network interface cards that make
use of directly-mapped memory capabilities to improve packet processing efficiency.
For more details:
File Descriptor NetDevice
The src/fd-net-device module provides the FdNetDevice class, which is able to read and write traffic
using a file descriptor provided by the user. This file descriptor can be associated to a TAP device, to
a raw socket, to a user space process generating/consuming traffic, etc. The user has full freedom to
define how external traffic is generated and ns-3 traffic is consumed.
Different mechanisms to associate a simulation to external traffic can be provided through helper
classes. Three specific helpers are provided:
• EmuFdNetDeviceHelper (to associate the ns-3 device with a physical device in the host machine)
• TapFdNetDeviceHelper (to associate the ns-3 device with the file descriptor from a tap device in the
host machine)
• PlanteLabFdNetDeviceHelper (to automate the creation of tap devices in PlanetLab nodes, enabling ns-3
simulations that can send and receive traffic though the Internet using PlanetLab resource.
Model Description
The source code for this module lives in the directory src/fd-net-device.
The FdNetDevice is a special type of ns-3 NetDevice that reads traffic to and from a file descriptor.
That is, unlike pure simulation NetDevice objects that write frames to and from a simulated channel, this
FdNetDevice directs frames out of the simulation to a file descriptor. The file descriptor may be
associated to a Linux TUN/TAP device, to a socket, or to a user-space process.
It is up to the user of this device to provide a file descriptor. The type of file descriptor being
provided determines what is being modelled. For instance, if the file descriptor provides a raw socket
to a WiFi card on the host machine, the device being modelled is a WiFi device.
From the conceptual “top” of the device looking down, it looks to the simulated node like a device
supporting a 48-bit IEEE MAC address that can be bridged, supports broadcast, and uses IPv4 ARP or IPv6
Neighbor Discovery, although these attributes can be tuned on a per-use-case basis.
Design
The FdNetDevice implementation makes use of a reader object, extended from the FdReader class in the ns-3
src/core module, which manages a separate thread from the main ns-3 execution thread, in order to read
traffic from the file descriptor.
Upon invocation of the StartDevice method, the reader object is initialized and starts the reading
thread. Before device start, a file descriptor must be previously associated to the FdNetDevice with the
SetFileDescriptor invocation.
The creation and configuration of the file descriptor can be left to a number of helpers, described in
more detail below. When this is done, the invocation of SetFileDescriptor is responsibility of the helper
and must not be directly invoked by the user.
Upon reading an incoming frame from the file descriptor, the reader will pass the frame to the
ReceiveCallback method, whose task it is to schedule the reception of the frame by the device as a ns-3
simulation event. Since the new frame is passed from the reader thread to the main ns-3 simulation
thread, thread-safety issues are avoided by using the ScheduleWithContext call instead of the regular
Schedule call.
In order to avoid overwhelming the scheduler when the incoming data rate is too high, a counter is kept
with the number of frames that are currently scheduled to be received by the device. If this counter
reaches the value given by the RxQueueSize attribute in the device, then the new frame will be dropped
silently.
The actual reception of the new frame by the device occurs when the scheduled FordwarUp method is invoked
by the simulator. This method acts as if a new frame had arrived from a channel attached to the device.
The device then decapsulates the frame, removing any layer 2 headers, and forwards it to upper network
stack layers of the node. The ForwardUp method will remove the frame headers, according to the frame
encapsulation type defined by the EncapsulationMode attribute, and invoke the receive callback passing an
IP packet.
An extra header, the PI header, can be present when the file descriptor is associated to a TAP device
that was created without setting the IFF_NO_PI flag. This extra header is removed if EncapsulationMode
is set to DIXPI value.
In the opposite direction, packets generated inside the simulation that are sent out through the device,
will be passed to the Send method, which will in turn invoke the SendFrom method. The latter method will
add the necessary layer 2 headers, and simply write the newly created frame to the file descriptor.
Scope and Limitations
Users of this device are cautioned that there is no flow control across the file descriptor boundary,
when using in emulation mode. That is, in a Linux system, if the speed of writing network packets
exceeds the ability of the underlying physical device to buffer the packets, backpressure up to the
writing application will be applied to avoid local packet loss. No such flow control is provided across
the file descriptor interface, so users must be aware of this limitation.
As explained before, the RxQueueSize attribute limits the number of packets that can be pending to be
received by the device. Frames read from the file descriptor while the number of pending packets is in
its maximum will be silently dropped.
The mtu of the device defaults to the Ethernet II MTU value. However, helpers are supposed to set the mtu
to the right value to reflect the characteristics of the network interface associated to the file
descriptor. If no helper is used, then the responsibility of setting the correct mtu value for the
device falls back to the user. The size of the read buffer on the file descriptor reader is set to the
mtu value in the StartDevice method.
The FdNetDevice class currently supports three encapsulation modes, DIX for Ethernet II frames, LLC for
802.2 LLC/SNAP frames, and DIXPI for Ethernet II frames with an additional TAP PI header. This means
that traffic traversing the file descriptor is expected to be Ethernet II compatible. IEEE 802.1q (VLAN)
tagging is not supported. Attaching an FdNetDevice to a wireless interface is possible as long as the
driver provides Ethernet II frames to the socket API. Note that to associate a FdNetDevice to a wireless
card in ad-hoc mode, the MAC address of the device must be set to the real card MAC address, else any
incoming traffic a fake MAC address will be discarded by the driver.
As mentioned before, three helpers are provided with the fd-net-device module. Each individual helper
(file descriptor type) may have platform limitations. For instance, threading, real-time simulation
mode, and the ability to create TUN/TAP devices are prerequisites to using the provided helpers. Support
for these modes can be found in the output of the waf configure step, e.g.:
Threading Primitives : enabled
Real Time Simulator : enabled
Emulated Net Device : enabled
Tap Bridge : enabled
It is important to mention that while testing the FdNetDevice we have found an upper bound limit for TCP
throughput when using 1Gb Ethernet links of 60Mbps. This limit is most likely due to the processing
power of the computers involved in the tests.
Usage
The usage pattern for this type of device is similar to other net devices with helpers that install to
node pointers or node containers. When using the base FdNetDeviceHelper the user is responsible for
creating and setting the file descriptor by himself.
FdNetDeviceHelper fd;
NetDeviceContainer devices = fd.Install (nodes);
// file descriptor generation
...
device->SetFileDescriptor (fd);
Most commonly a FdNetDevice will be used to interact with the host system. In these cases it is almost
certain that the user will want to run in real-time emulation mode, and to enable checksum computations.
The typical program statements are as follows:
GlobalValue::Bind ("SimulatorImplementationType", StringValue ("ns3::RealtimeSimulatorImpl"));
GlobalValue::Bind ("ChecksumEnabled", BooleanValue (true));
The easiest way to set up an experiment that interacts with a Linux host system is to user the Emu and
Tap helpers. Perhaps the most unusual part of these helper implementations relates to the requirement
for executing some of the code with super-user permissions. Rather than force the user to execute the
entire simulation as root, we provide a small “creator” program that runs as root and does any required
high-permission sockets work. The easiest way to set the right privileges for the “creator” programs, is
by enabling the --enable-sudo flag when performing waf configure.
We do a similar thing for both the Emu and the Tap devices. The high-level view is that the
CreateFileDescriptor method creates a local interprocess (Unix) socket, forks, and executes the small
creation program. The small program, which runs as suid root, creates a raw socket and sends back the raw
socket file descriptor over the Unix socket that is passed to it as a parameter. The raw socket is
passed as a control message (sometimes called ancillary data) of type SCM_RIGHTS.
Helpers
EmuFdNetDeviceHelper
The EmuFdNetDeviceHelper creates a raw socket to an underlying physical device, and provides the socket
descriptor to the FdNetDevice. This allows the ns-3 simulation to read frames from and write frames to a
network device on the host.
The emulation helper permits to transparently integrate a simulated ns-3 node into a network composed of
real nodes.
+----------------------+ +-----------------------+
| host 1 | | host 2 |
+----------------------+ +-----------------------+
| ns-3 simulation | | |
+----------------------+ | Linux |
| ns-3 Node | | Network Stack |
| +----------------+ | | +----------------+ |
| | ns-3 TCP | | | | TCP | |
| +----------------+ | | +----------------+ |
| | ns-3 IP | | | | IP | |
| +----------------+ | | +----------------+ |
| | FdNetDevice | | | | | |
| | 10.1.1.1 | | | | | |
| +----------------+ | | + ETHERNET + |
| | raw socket | | | | | |
|--+----------------+--| | +----------------+ |
| | eth0 | | | | eth0 | |
+-------+------+-------+ +--------+------+-------+
10.1.1.11 10.1.1.12
| |
+----------------------------+
This helper replaces the functionality of the EmuNetDevice found in ns-3 prior to ns-3.17, by bringing
this type of device into the common framework of the FdNetDevice. The EmuNetDevice was deprecated in
favor of this new helper.
The device is configured to perform MAC spoofing to separate simulation network traffic from other
network traffic that may be flowing to and from the host.
One can use this helper in a testbed situation where the host on which the simulation is running has a
specific interface of interest which drives the testbed hardware. You would also need to set this
specific interface into promiscuous mode and provide an appropriate device name to the ns-3 simulation.
Additionally, hardware offloading of segmentation and checksums should be disabled.
The helper only works if the underlying interface is up and in promiscuous mode. Packets will be sent out
over the device, but we use MAC spoofing. The MAC addresses will be generated (by default) using the
Organizationally Unique Identifier (OUI) 00:00:00 as a base. This vendor code is not assigned to any
organization and so should not conflict with any real hardware.
It is always up to the user to determine that using these MAC addresses is okay on your network and won’t
conflict with anything else (including another simulation using such devices) on your network. If you are
using the emulated FdNetDevice configuration in separate simulations, you must consider global MAC
address assignment issues and ensure that MAC addresses are unique across all simulations. The emulated
net device respects the MAC address provided in the Address attribute so you can do this manually. For
larger simulations, you may want to set the OUI in the MAC address allocation function.
Before invoking the Install method, the correct device name must be configured on the helper using the
SetDeviceName method. The device name is required to identify which physical device should be used to
open the raw socket.
EmuFdNetDeviceHelper emu;
emu.SetDeviceName (deviceName);
NetDeviceContainer devices = emu.Install (node);
Ptr<NetDevice> device = devices.Get (0);
device->SetAttribute ("Address", Mac48AddressValue (Mac48Address::Allocate ()));
TapFdNetDeviceHelper
A Tap device is a special type of Linux device for which one end of the device appears to the kernel as a
virtual net_device, and the other end is provided as a file descriptor to user-space. This file
descriptor can be passed to the FdNetDevice. Packets forwarded to the TAP device by the kernel will show
up in the FdNetDevice in ns-3.
Users should note that this usage of TAP devices is different than that provided by the TapBridge
NetDevice found in src/tap-bridge. The model in this helper is as follows:
+-------------------------------------+
| host |
+-------------------------------------+
| ns-3 simulation | |
+----------------------+ |
| ns-3 Node | |
| +----------------+ | |
| | ns-3 TCP | | |
| +----------------+ | |
| | ns-3 IP | | |
| +----------------+ | |
| | FdNetDevice | | |
|--+----------------+--+ +------+ |
| | TAP | | eth0 | |
| +------+ +------+ |
| 192.168.0.1 | |
+-------------------------------|-----+
|
|
------------ (Internet) -----
In the above, the configuration requires that the host be able to forward traffic generated by the
simulation to the Internet.
The model in TapBridge (in another module) is as follows:
+--------+
| Linux |
| host | +----------+
| ------ | | ghost |
| apps | | node |
| ------ | | -------- |
| stack | | IP | +----------+
| ------ | | stack | | node |
| TAP | |==========| | -------- |
| device | <----- IPC ------> | tap | | IP |
+--------+ | bridge | | stack |
| -------- | | -------- |
| ns-3 | | ns-3 |
| net | | net |
| device | | device |
+----------+ +----------+
|| ||
+---------------------------+
| ns-3 channel |
+---------------------------+
In the above, packets instead traverse ns-3 NetDevices and Channels.
The usage pattern for this example is that the user sets the MAC address and either (or both) the IPv4
and IPv6 addresses and masks on the device, and the PI header if needed. For example:
TapFdNetDeviceHelper helper;
helper.SetDeviceName (deviceName);
helper.SetModePi (modePi);
helper.SetTapIpv4Address (tapIp);
helper.SetTapIpv4Mask (tapMask);
...
helper.Install (node);
PlanetLabFdNetDeviceHelper
PlanetLab is a world wide distributed network testbed composed of nodes connected to the Internet.
Running ns-3 simulations in PlanetLab nodes using the PlanetLabFdNetDeviceHelper allows to send simulated
traffic generated by ns-3 directly to the Internet. This setup can be useful to validate ns-3 Internet
protocols or other future protocols implemented in ns-3.
To run experiments using PlanetLab nodes it is required to have a PlanetLab account. Only members of
PlanetLab partner institutions can obtain such accounts ( for more information visit
http://www.planet-lab.org/ or http://www.planet-lab.eu ). Once the account is obtained, a PlanetLab
slice must be requested in order to conduct experiments. A slice represents an experiment unit related
to a group of PlanetLab users, and can be associated to virtual machines in different PlanetLab nodes.
Slices can also be customized by adding configuration tags to it (this is done by PlanetLab
administrators).
The PlanetLabFdNetDeviceHelper creates TAP devices on PlanetLab nodes using specific PlanetLab mechanisms
(i.e. the vsys system), and associates the TAP device to a FdNetDevice in ns-3. The functionality
provided by this helper is similar to that provided by the FdTapNetDeviceHelper, except that the
underlying mechanisms to create the TAP device are different.
+-------------------------------------+
| PlanetLab host |
+-------------------------------------+
| ns-3 simulation | |
+----------------------+ |
| ns-3 Node | |
| +----------------+ | |
| | ns-3 TCP | | |
| +----------------+ | |
| | ns-3 IP | | |
| +----------------+ | |
| | FdNetDevice | | |
|--+----------------+--+ +------+ |
| | TAP | | eth0 | |
| +------+ +------+ |
| 192.168.0.1 | |
+-------------------------------|-----+
|
|
------------ (Internet) -----
In order to be able to assign private IPv4 addresses to the TAP devices, account holders must request the
vsys_vnet tag to be added to their slice by PlanetLab administrators. The vsys_vnet tag is associated to
private network segment and only addresses from this segment can be used in experiments.
The syntax used to create a TAP device with this helper is similar to that used for the previously
described helpers:
PlanetLabFdNetDeviceHelper helper;
helper.SetTapIpAddress (tapIp);
helper.SetTapMask (tapMask);
...
helper.Install (node);
PlanetLab nodes have a Fedora based distribution, so ns-3 can be installed following the instructions for
ns-3 Linux installation.
Attributes
The FdNetDevice provides a number of attributes:
• Address: The MAC address of the device
• Start: The simulation start time to spin up the device thread
• Stop: The simulation start time to stop the device thread
• EncapsulationMode: Link-layer encapsulation format
•
RxQueueSize: The buffer size of the read queue on the file descriptor
thread (default of 1000 packets)
Start and Stop do not normally need to be specified unless the user wants to limit the time during which
this device is active. Address needs to be set to some kind of unique MAC address if the simulation will
be interacting with other real devices somehow using real MAC addresses. Typical code:
device->SetAttribute ("Address", Mac48AddressValue (Mac48Address::Allocate ()));
Output
Ascii and PCAP tracing is provided similar to the other ns-3 NetDevice types, through the helpers, such
as (e.g.):
:: EmuFdNetDeviceHelper emu; NetDeviceContainer devices = emu.Install (node); … emu.EnablePcap
(“emu-ping”, device, true);
The standard set of Mac-level NetDevice trace sources is provided.
• MaxTx: Trace source triggered when ns-3 provides the device with a new frame to send
• MaxTxDrop: Trace source if write to file descriptor fails
• MaxPromiscRx: Whenever any valid Mac frame is received
• MaxRx: Whenever a valid Mac frame is received for this device
• Sniffer: Non-promiscuous packet sniffer
• PromiscSniffer: Promiscuous packet sniffer (for tcpdump-like traces)
Examples
Several examples are provided:
• dummy-network.cc: This simple example creates two nodes and interconnects them with a Unix pipe by
passing the file descriptors from the socketpair into the FdNetDevice objects of the respective nodes.
• realtime-dummy-network.cc: Same as dummy-network.cc but uses the real time simulator implementnation
instead of the default one.
• fd2fd-onoff.cc: This example is aimed at measuring the throughput of the FdNetDevice in a pure
simulation. For this purpose two FdNetDevices, attached to different nodes but in a same simulation,
are connected using a socket pair. TCP traffic is sent at a saturating data rate.
• fd-emu-onoff.cc: This example is aimed at measuring the throughput of the FdNetDevice when using the
EmuFdNetDeviceHelper to attach the simulated device to a real device in the host machine. This is
achieved by saturating the channel with TCP traffic.
• fd-emu-ping.cc: This example uses the EmuFdNetDeviceHelper to send ICMP traffic over a real channel.
• fd-emu-udp-echo.cc: This example uses the EmuFdNetDeviceHelper to send UDP traffic over a real channel.
• fd-planetlab-ping.cc: This example shows how to set up an experiment to send ICMP traffic from a
PlanetLab node to the Internet.
• fd-tap-ping.cc: This example uses the TapFdNetDeviceHelper to send ICMP traffic over a real channel.
Netmap NetDevice
The fd-net-device module provides the NetmapNetDevice class, a class derived from the FdNetDevice which
is able to read and write traffic using a netmap file descriptor. This netmap file descriptor must be
associated to a real ethernet device in the host machine. The NetmapNetDeviceHelper class supports the
configuration of a NetmapNetDevice.
netmap is a fast packet processing capability that bypasses the host networking stack and gains direct
access to network device. netmap was developed by Luigi Rizzo [Rizzo2012] and is maintained as an open
source project on GitHub at https://github.com/luigirizzo/netmap.
The NetmapNetDevice for ns-3 [Imputato2019] was developed by Pasquale Imputato in the 2017-19 timeframe.
The use of NetmapNetDevice requires that the host system has netmap support (and for best performance,
the drivers must support netmap and must be using a netmap-enabled device driver). Users can expect that
emulation support using Netmap will support higher packets per second than emulation using FdNetDevice
with raw sockets (which pass through the Linux networking kernel).
[Rizzo2012]
Luigi Rizzo, “netmap: A Novel Framework for Fast Packet I/O”, Proceedings of 2012 USENIX Annual
Techincal Conference, June 2012.
[Imputato2019]
Pasquale Imputato, Stefano Avallone, Enhancing the fidelity of network emulation through direct
access to device buffers, Journal of Network and Computer Applications, Volume 130, 2019, Pages
63-75, (http://www.sciencedirect.com/science/article/pii/S1084804519300220)
Model Description
Design
Because netmap uses file descriptor based communication to interact with the real device, the
straightforward approach to design a new NetDevice around netmap is to have it inherit from the existing
FdNetDevice and implement a specialized version of the operations specific to netmap. The operations
that require a specialized implementation are the initialization, because the NIC has to be put in netmap
mode, and the read/write methods, which have to make use of the netmap API to coordinate the exchange of
packets with the netmap rings.
In the initialization stage, the network device is switched to netmap mode, so that ns-3 is able to
send/receive packets to/from the real network device by writing/reading them to/from the netmap rings.
Following the design of the FdNetDevice, a separate reading thread is started during the initialization.
The task of the reading thread is to wait for new incoming packets in the netmap receiver rings, in order
to schedule the events of packet reception. In the initialization of the NetmapNetDevice, an additional
thread, the sync thread, is started. The sync thread is required because, in order to reduce the cost of
the system calls, netmap does not automatically transfer a packet written to a slot of the netmap ring to
the transmission ring or to the installed qdisc. It is up to the user process to periodically request a
synchronization of the netmap ring. Therefore, the purpose of the sync thread is to periodically make a
TXSYNC ioctl request, so that pending packets in the netmap ring are transferred to the transmission
ring, if in native mode, or to the installed qdisc, if in generic mode. Also, as described further below,
the sync thread is exploited to perform flow control and notify the BQL library about the amount of bytes
that have been transferred to the network device.
The read method is called by the reading thread to retrieve new incoming packets stored in the netmap
receiver ring and pass them to the appropriate ns-3 protocol handler for further processing within the
simulator’s network stack. After retrieving packets, the reading thread also synchronizes the netmap
receiver ring, so that the retrieved packets can be removed from the netmap receiver ring.
The NetmapNetDevice also specializes the write method, i.e., the method used to transmit a packet
received from the upper layer (the ns-3 traffic control layer). The write method uses the netmap API to
write the packet to a free slot in the netmap transmission ring. After writing a packet, the write method
checks whether there is enough room in the netmap transmission ring for another packet. If not, the
NetmapNetDevice stops its queue so that the ns-3 traffic control layer does not attempt to send a packet
that could not be stored in the netmap transmission ring.
A stopped NetmapNetDevice queue needs to be restarted as soon as some room is made in the netmap
transmission ring. The sync thread can be exploited for this purpose, given that it periodically
synchronizes the netmap transmission ring. In particular, the sync thread also checks the number of free
slots in the netmap transmission ring in case the NetmapNetDevice queue is stopped. If the number of
free slots exceeds a configurable value, the sync thread restarts the NetmapNetDevice queue and wakes the
associated ns-3 qdisc. The NetmapNetDevice also supports BQL: the write method notifies the BQL library
of the amount of bytes that have been written to the netmap transmission ring, while the sync thread
notifies the BQL library of the amount of bytes that have been removed from the netmap transmission ring
and transferred to the NIC since the previous notification.
Scope and Limitations
The main scope of NetmapNetDevice is to support the flow-control between the physical device and the
upper layer and using at best the computational resources to process packets. However, the (Linux)
system and network device must support netmap to make use of this feature.
Usage
The installation of netmap itself on a host machine is out of scope for this document. Refer to the
netmap GitHub README for instructions.
The ns-3 netmap code has only been tested on Linux; it is not clear whether other operating systems can
be supported.
If ns-3 is able to detect the presence of netmap on the system, it will report that:
Netmap emulation FdNetDevice : not enabled
If not, it will report:
Netmap emulation FdNetDevice : not enabled (needs net/netmap_user.h)
To run FdNetDevice-enabled simulations, one must pass the --enable-sudo option to ./waf configure, or
else run the simulations with root privileges.
Helpers
ns-3 netmap support uses a NetMapNetDeviceHelper helper object to install the NetmapNetDevice. In other
respects, the API and use is similar to that of the EmuFdNetDeviceHelper.
Attributes
There is one attribute specialized to NetmapNetDevice, named SyncAndNotifyQueuePeriod. This value takes
an integer number of microseconds, and is used as the period of time after which the device syncs the
netmap ring and notifies queue status. The value should be close to the interrupt coalescence period of
the real device. Users may want to tune this parameter for their own system; it should be a compromise
between CPU usage and accuracy in the ring sync (if it is too high, the device goes into starvation and
lower throughput occurs).
Output
The NetmapNetDevice does not provide any specialized output, but supports the FdNetDevice output and
traces (such as a promiscuous sniffer trace).
Examples
Several examples are provided:
• fd-emu-onoff.cc: This example is aimed at measuring the throughput of the NetmapNetDevice when using
the NetmapNetDeviceHelper to attach the simulated device to a real device in the host machine. This is
achieved by saturating the channel with TCP or UDP traffic.
• fd-emu-ping.cc: This example uses the NetmapNetDevice to send ICMP traffic over a real device.
•
fd-emu-tc.cc: This example configures a router on a machine with two
interfaces in emulated mode through netmap. The aim is to explore different qdiscs behaviours on
the backlog of a device emulated bottleneck side.
• fd-emu-send.cc: This example builds a node with a device in emulation mode through netmap. The aim is
to measure the maximum transmit rate in packets per second (pps) achievable with NetmapNetDevice on a
specific machine.
Note that all the examples run in emulation mode through netmap (with NetmapNetDevice) and raw socket
(with FdNetDevice).
DPDK NetDevice
Data Plane Development Kit (DPDK) is a library hosted by The Linux Foundation to accelerate packet
processing workloads (https://www.dpdk.org/).
The DpdkNetDevice class provides the implementation of a network device which uses DPDK’s fast packet
processing abilities and bypasses the kernel. This class is included in the src/fd-net-device model. The
DpdkNetDevice class inherits the FdNetDevice class and overrides the functions which are required by ns-3
to interact with DPDK environment.
The DpdkNetDevice for ns-3 [Patel2019] was developed by Harsh Patel, Hrishikesh Hiraskar and Mohit P.
Tahiliani. They were supported by Intel Technology India Pvt. Ltd., Bangalore for this work.
[Patel2019]
Harsh Patel, Hrishikesh Hiraskar, Mohit P. Tahiliani, “Extending Network Emulation Support in ns-3
using DPDK”, Proceedings of the 2019 Workshop on ns-3, ACM, Pages 17-24, (‐
https://dl.acm.org/doi/abs/10.1145/3321349.3321358)
Model Description
DpdkNetDevice is a network device which provides network emulation capabilities i.e. to allow simulated
nodes to interact with real hosts and vice versa. The main feature of the DpdkNetDevice is that is uses
the Environment Abstraction Layer (EAL) provided by DPDK to perform fast packet processing. EAL hides the
device specific attributes from the applications and provides an interface via which the applications can
interact directly with the Network Interface Card (NIC). This allows ns-3 to send/receive packets
directly to/from the NIC without the kernel involvement.
Design
DpdkNetDevice is designed to act as an interface between ns-3 and DPDK environment. There are 3 main
phases in the life cycle of DpdkNetDevice:
• Initialization
• Packet Transfer - Read and Write
• Termination
Initialization
DpdkNetDeviceHelper model is responsible for the initialization of DpdkNetDevice. After this, the EAL is
initialized, a memory pool is allocated, access to the Ethernet port is obtained and it is initialized,
reception (Rx) and transmission (Tx) queues are set up on the port, Rx and Tx buffers are set up and
LaunchCore method is called which will launch the HandleRx method to handle reading of packets in burst.
Packet Transfer
DPDK interacts with packet in the form of mbuf, a data structure provided by it, while ns-3 interacts
with packets in the form of raw buffer. The packet transfer functions take care of converting DPDK mbufs
to ns-3 buffers. The functions are read and write.
• Read: HandleRx method takes care of reading the packets from NIC and transferring them to ns-3 Internet
Stack. This function is called by LaunchCore method which is launched during initialization. It
continuously polls the NIC using DPDK API for packets to read. It reads the mbuf packets in burst from
NIC Rx ring, which are placed into Rx buffer upon read. For each mbuf packet in Rx buffer, it then
converts it to ns-3 raw buffer and then forwards the packet to ns-3 Internet Stack.
• Write: Write method handles transmission of packets. ns-3 provides this packet in the form of a buffer,
which is converted to packet mbuf and then placed in the Tx buffer. These packets are then transferred
to NIC Tx ring when the Tx buffer is full, from where they will be transmitted by the NIC. However,
there might be a scenario where there are not enough packets to fill the Tx buffer. This will lead to
stale packet mbufs in buffer. In such cases, the Write function schedules a manual flush of these stale
packet mbufs to NIC Tx ring, which will occur upon a certain timeout period. The default value of this
timeout is set to 2 ms.
Termination
When ns-3 is done using DpdkNetDevice, the DpdkNetDevice will stop polling for Rx, free the allocated
mbuf packets and then the mbuf pool. Lastly, it will stop the Ethernet device and close the port.
Scope and Limitations
The current implementation supports only one NIC to be bound to DPDK with single Rx and Tx on the NIC.
This can be extended to support multiple NICs and multiple Rx/Tx queues simultaneously. Currently there
is no support for Jumbo frames, which can be added. Offloading, scheduling features can also be added.
Flow control and support for qdisc can be added to provide a more extensive model for network testing.
DPDK Installation
This section contains information on downloading DPDK source code and setting up DPDK for DpdkNetDevice
to work.
Is my NIC supported by DPDK?
Check Supported Devices.
Not supported? Use Virtual Machine instead
Install Oracle VM VirtualBox. Create a new VM and install Ubuntu on it. Open settings, create a network
adapter with following configuration:
• Attached to: Bridged Adapter
• Name: The host network device you want to use
•
In Advanced
• Adapter Type: Intel PRO/1000 MT Server (82545EM) or any other DPDK supported NIC
• Promiscuous Mode: Allow All
• Select Cable Connected
Then rest of the steps are same as follows.
DPDK can be installed in 2 ways:
• Install DPDK on Ubuntu
• Compile DPDK from source
Install DPDK on Ubuntu
To install DPDK on Ubuntu, run the following command:
apt-get install dpdk dpdk-dev libdpdk-dev dpdk-igb-uio-dkms
Ubuntu 20.04 has packaged DPDK v19.11 LTS which is tested with this module and DpdkNetDevice will only be
enabled if this version is available.
Compile from Source
To compile DPDK from source, you need to perform the following 4 steps:
1. Download the source
Visit the DPDK Downloads page to download the latest stable source. (This module has been tested with
version 19.11 LTS and DpdkNetDevice will only be enabled if this version is available.)
2. Configure DPDK as a shared library
In the DPDK directory, edit the config/common_base file to change the following line to compile DPDK as a
shared library:
# Compile to share library
CONFIG_RTE_BUILD_SHARED_LIB=y
3. Install the source
Refer to Installation for detailed instructions.
For a 64 bit linux machine with gcc, run:
make install T=x86_64-native-linuxapp-gcc DESTDIR=install
4. Export DPDK Environment variables
Export the following environment variables:
• RTE_SDK as the your DPDK source folder.
• RTE_TARGET as the build target directory.
For example:
export RTE_SDK=/home/username/dpdk/dpdk-stable-19.11.1
export RTE_TARGET=x86_64-native-linuxapp-gcc
(Note: In case DPDK is moved, ns-3 needs to be reconfigured using ./waf configure [options])
It is advisable that you export these variables in .bashrc or similar for reusability.
Load DPDK Drivers to kernel
Execute the following:
sudo modprobe uio_pci_generic
sudo modprobe uio
sudo modprobe vfio-pci
sudo modprobe igb_uio # for ubuntu package
# OR
sudo insmod $RTE_SDK/$RTE_TARGET/kmod/igb_uio.ko # for dpdk source
These should be done every time you reboot your system.
Configure hugepages
Refer System Requirements for detailed instructions.
To allocate hugepages at runtime, write a value such as ‘256’ to the following:
echo 256 > /sys/kernel/mm/hugepages/hugepages-2048kB/nr_hugepages
To allocate hugepages at boot time, edit /etc/default/grub, and following to GRUB_CMDLINE_LINUX_DEFAULT:
hugepages=256
We suggest minimum of number of 256 to run our applications. (This is to test an application run at 1
Gbps on a 1 Gbps NIC.) You can use any number of hugepages based on your system capacity and application
requirements.
Then update the grub configurations using:
sudo update-grub
OR
sudo update-grub2
You will need to reboot your system in order to see these changes.
To check allocation of hugepages, run:
cat /proc/meminfo | grep HugePages
You will see the number of hugepages allocated, they should be equal to the number you used above.
Once the hugepage memory is reserved (at either runtime or boot time), to make the memory available for
DPDK use, perform the following steps:
sudo mkdir /mnt/huge
sudo mount -t hugetlbfs nodev /mnt/huge
The mount point can be made permanent across reboots, by adding the following line to the /etc/fstab
file:
nodev /mnt/huge hugetlbfs defaults 0 0
Usage
The status of DPDK support is shown in the output of ./waf configure. If it is found, a user should see:
DPDK NetDevice : enabled
DpdkNetDeviceHelper class supports the configuration of DpdkNetDevice.
+----------------------+
| host 1 |
+----------------------+
| ns-3 simulation |
+----------------------+
| ns-3 Node |
| +----------------+ |
| | ns-3 TCP | |
| +----------------+ |
| | ns-3 IP | |
| +----------------+ |
| | DpdkNetDevice | |
| | 10.1.1.1 | |
| +----------------+ |
| | raw socket | |
|--+----------------+--|
| | eth0 | |
+-------+------+-------+
10.1.1.11
|
+-------------- ( Internet ) ----
Initialization of DPDK driver requires initialization of EAL. EAL requires PMD (Poll Mode Driver) Library
for using NIC. DPDK supports multiple Poll Mode Drivers and you can use one that works for your NIC. PMD
Library can be set via DpdkNetDeviceHelper::SetPmdLibrary, as follows:
DpdkNetDeviceHelper* dpdk = new DpdkNetDeviceHelper ();
dpdk->SetPmdLibrary("librte_pmd_e1000.so");
Also, NIC should be bound to DPDK Driver in order to be used with EAL. The default driver used is
uio_pci_generic which supports most of the NICs. You can change it using
DpdkNetDeviceHelper::SetDpdkDriver, as follows:
DpdkNetDeviceHelper* dpdk = new DpdkNetDeviceHelper ();
dpdk->SetDpdkDriver("igb_uio");
Attributes
The DpdkNetDevice provides a number of attributes:
• TxTimeout - The time to wait before transmitting burst from Tx Buffer (in us). (default - 2000) This
attribute is only used to flush out buffer in case it is not filled. This attribute can be decrease for
low data rate traffic. For high data rate traffic, this attribute needs no change.
• MaxRxBurst - Size of Rx Burst. (default - 64) This attribute can be increased for higher data rates.
• MaxTxBurst - Size of Tx Burst. (default - 64) This attribute can be increased for higher data rates.
• MempoolCacheSize - Size of mempool cache. (default - 256) This attribute can be increased for higher
data rates.
• NbRxDesc - Number of Rx descriptors. (default - 1024) This attribute can be increased for higher data
rates.
• NbTxDesc - Number of Tx descriptors. (default - 1024) This attribute can be increased for higher data
rates.
Note: Default values work well with 1Gbps traffic.
Output
As DpdkNetDevice is inherited from FdNetDevice, all the output methods provided by FdNetDevice can be
used directly.
Examples
The following examples are provided:
• fd-emu-ping.cc: This example can be configured to use the DpdkNetDevice to send ICMP traffic bypassing
the kernel over a real channel.
• fd-emu-onoff.cc: This example can be configured to measure the throughput of the DpdkNetDevice by
sending traffic from the simulated node to a real device using the ns3::OnOffApplication while
leveraging DPDK’s fast packet processing abilities. This is achieved by saturating the channel with
TCP/UDP traffic.
Tap NetDevice
The Tap NetDevice can be used to allow a host system or virtual machines to interact with a simulation.
TapBridge Model Overview
The Tap Bridge is designed to integrate “real” internet hosts (or more precisely, hosts that support
Tun/Tap devices) into ns-3 simulations. The goal is to make it appear to a “real” host node in that it
has an ns-3 net device as a local device. The concept of a “real host” is a bit slippery since the “real
host” may actually be virtualized using readily available technologies such as VMware, VirtualBox or
OpenVZ.
Since we are, in essence, connecting the inputs and outputs of an ns-3 net device to the inputs and
outputs of a Linux Tap net device, we call this arrangement a Tap Bridge.
There are three basic operating modes of this device available to users. Basic functionality is
essentially identical, but the modes are different in details regarding how the arrangement is created
and configured; and what devices can live on which side of the bridge.
We call these three modes the ConfigureLocal, UseLocal and UseBridge modes. The first “word” in the
camel case mode identifier indicates who has the responsibility for creating and configuring the taps.
For example, the “Configure” in ConfigureLocal mode indicates that it is the TapBridge that has
responsibility for configuring the tap. In UseLocal mode and UseBridge modes, the “Use” prefix indicates
that the TapBridge is asked to “Use” an existing configuration.
In other words, in ConfigureLocal mode, the TapBridge has the responsibility for creating and configuring
the TAP devices. In UseBridge or UseLocal modes, the user provides a configuration and the TapBridge
adapts to that configuration.
TapBridge ConfigureLocal Mode
In the ConfigureLocal mode, the configuration of the tap device is ns-3 configuration-centric.
Configuration information is taken from a device in the ns-3 simulation and a tap device matching the
ns-3 attributes is automatically created. In this case, a Linux computer is made to appear as if it was
directly connected to a simulated ns-3 network.
This is illustrated below:
+--------+
| Linux |
| host | +----------+
| ------ | | ghost |
| apps | | node |
| ------ | | -------- |
| stack | | IP | +----------+
| ------ | | stack | | node |
| TAP | |==========| | -------- |
| device | <----- IPC ------> | tap | | IP |
+--------+ | bridge | | stack |
| -------- | | -------- |
| ns-3 | | ns-3 |
| net | | net |
| device | | device |
+----------+ +----------+
|| ||
+---------------------------+
| ns-3 channel |
+---------------------------+
In this case, the “ns-3 net device” in the “ghost node” appears as if it were actually replacing the TAP
device in the Linux host. The ns-3 simulation creates the TAP device on the underlying Linux OS and
configures the IP and MAC addresses of the TAP device to match the values assigned to the simulated ns-3
net device. The “IPC” link shown above is the network tap mechanism in the underlying OS. The whole
arrangement acts as a conventional bridge; but a bridge between devices that happen to have the same
shared MAC and IP addresses.
Here, the user is not required to provide any configuration information specific to the tap. A tap
device will be created and configured by ns-3 according to its defaults, and the tap device will have its
name assigned by the underlying operating system according to its defaults.
If the user has a requirement to access the created tap device, he or she may optionally provide a
“DeviceName” attribute. In this case, the created OS tap device will be named accordingly.
The ConfigureLocal mode is the default operating mode of the Tap Bridge.
TapBridge UseLocal Mode
The UseLocal mode is quite similar to the ConfigureLocal mode. The significant difference is, as the
mode name implies, the TapBridge is going to “Use” an existing tap device previously created and
configured by the user. This mode is particularly useful when a virtualization scheme automatically
creates tap devices and ns-3 is used to provide simulated networks for those devices.
+--------+
| Linux |
| host | +----------+
| ------ | | ghost |
| apps | | node |
| ------ | | -------- |
| stack | | IP | +----------+
| ------ | | stack | | node |
| TAP | |==========| | -------- |
| device | <----- IPC ------> | tap | | IP |
| MAC X | | bridge | | stack |
+--------+ | -------- | | -------- |
| ns-3 | | ns-3 |
| net | | net |
| device | | device |
| MAC Y | | MAC Z |
+----------+ +----------+
|| ||
+---------------------------+
| ns-3 channel |
+---------------------------+
In this case, the pre-configured MAC address of the “Tap device” (MAC X) will not be the same as that of
the bridged “ns-3 net device” (MAC Y) shown in the illustration above. In order to bridge to ns-3 net
devices which do not support SendFrom() (especially wireless STA nodes) we impose a requirement that only
one Linux device (with one unique MAC address – here X) generates traffic that flows across the IPC link.
This is because the MAC addresses of traffic across the IPC link will be “spoofed” or changed to make it
appear to Linux and ns-3 that they have the same address. That is, traffic moving from the Linux host to
the ns-3 ghost node will have its MAC address changed from X to Y and traffic from the ghost node to the
Linux host will have its MAC address changed from Y to X. Since there is a one-to-one correspondence
between devices, there may only be one MAC source flowing from the Linux side. This means that Linux
bridges with more than one net device added are incompatible with UseLocal mode.
In UseLocal mode, the user is expected to create and configure a tap device completely outside the scope
of the ns-3 simulation using something like:
$ sudo tunctl -t tap0
$ sudo ifconfig tap0 hw ether 08:00:2e:00:00:01
$ sudo ifconfig tap0 10.1.1.1 netmask 255.255.255.0 up
To tell the TapBridge what is going on, the user will set either directly into the TapBridge or via the
TapBridgeHelper, the “DeviceName” attribute. In the case of the configuration above, the “DeviceName”
attribute would be set to “tap0” and the “Mode” attribute would be set to “UseLocal”.
One particular use case for this mode is in the OpenVZ environment. There it is possible to create a Tap
device on the “Hardware Node” and move it into a Virtual Private Server. If the TapBridge is able to use
an existing tap device it is then possible to avoid the overhead of an OS bridge in that environment.
TapBridge UseBridge Mode
The simplest mode for those familiar with Linux networking is the UseBridge mode. Again, the “Use”
prefix indicates that the TapBridge is going to Use an existing configuration. In this case, the
TapBridge is going to logically extend a Linux bridge into ns-3.
This is illustrated below:
+---------+
| Linux | +----------+
| ------- | | ghost |
| apps | | node |
| ------- | | -------- |
| stack | | IP | +----------+
| ------- | +--------+ | stack | | node |
| Virtual | | TAP | |==========| | -------- |
| Device | | Device | <---- IPC -----> | tap | | IP |
+---------+ +--------+ | bridge | | stack |
|| || | -------- | | -------- |
+--------------------+ | ns-3 | | ns-3 |
| OS (brctl) Bridge | | net | | net |
+--------------------+ | device | | device |
+----------+ +----------+
|| ||
+---------------------------+
| ns-3 channel |
+---------------------------+
In this case, a computer running Linux applications, protocols, etc., is connected to a ns-3 simulated
network in such a way as to make it appear to the Linux host that the TAP device is a real network device
participating in the Linux bridge.
In the ns-3 simulation, a TapBridge is created to match each TAP Device. The name of the TAP Device is
assigned to the Tap Bridge using the “DeviceName” attribute. The TapBridge then logically extends the OS
bridge to encompass the ns-3 net device.
Since this mode logically extends an OS bridge, there may be many Linux net devices on the non-ns-3 side
of the bridge. Therefore, like a net device on any bridge, the ns-3 net device must deal with the
possibly of many source addresses. Thus, ns-3 devices must support SendFrom()
(NetDevice::SupportsSendFrom() must return true) in order to be configured for use in UseBridge mode.
It is expected that the user will do something like the following to configure the bridge and tap
completely outside ns-3:
$ sudo brctl addbr mybridge
$ sudo tunctl -t mytap
$ sudo ifconfig mytap hw ether 00:00:00:00:00:01
$ sudo ifconfig mytap 0.0.0.0 up
$ sudo brctl addif mybridge mytap
$ sudo brctl addif mybridge ...
$ sudo ifconfig mybridge 10.1.1.1 netmask 255.255.255.0 up
To tell the TapBridge what is going on, the user will set either directly into the TapBridge or via the
TapBridgeHelper, the “DeviceName” attribute. In the case of the configuration above, the “DeviceName”
attribute would be set to “mytap” and the “Mode” attribute would be set to “UseBridge”.
This mode is especially useful in the case of virtualization where the configuration of the virtual
hosts may be dictated by another system and not be changeable to suit ns-3. For example, a particular VM
scheme may create virtual “vethx” or “vmnetx” devices that appear local to virtual hosts. In order to
connect to such systems, one would need to manually create TAP devices on the host system and brigde
these TAP devices to the existing (VM) virtual devices. The job of the Tap Bridge in this case is to
extend the bridge to join a ns-3 net device.
TapBridge ConfigureLocal Operation
In ConfigureLocal mode, the TapBridge and therefore its associated ns-3 net device appears to the Linux
host computer as a network device just like any arbitrary “eth0” or “ath0” might appear. The creation
and configuration of the TAP device is done by the ns-3 simulation and no manual configuration is
required by the user. The IP addresses, MAC addresses, gateways, etc., for created TAP devices are
extracted from the simulation itself by querying the configuration of the ns-3 device and the TapBridge
Attributes.
Since the MAC addresses are identical on the Linux side and the ns-3 side, we can use Send() on the ns-3
device which is available on all ns-3 net devices. Since the MAC addresses are identical there is no
requirement to hook the promiscuous callback on the receive side. Therefore there are no restrictions on
the kinds of net device that are usable in ConfigureLocal mode.
The TapBridge appears to an ns-3 simulation as a channel-less net device. This device must not have an
IP address associated with it, but the bridged (ns-3) net device must have an IP address. Be aware that
this is the inverse of an ns-3 BridgeNetDevice (or a conventional bridge in general) which demands that
its bridge ports not have IP addresses, but allows the bridge device itself to have an IP address.
The host computer will appear in a simulation as a “ghost” node that contains one TapBridge for each
NetDevice that is being bridged. From the perspective of a simulation, the only difference between a
ghost node and any other node will be the presence of the TapBridge devices. Note however, that the
presence of the TapBridge does affect the connectivity of the net device to the IP stack of the ghost
node.
Configuration of address information and the ns-3 devices is not changed in any way if a TapBridge is
present. A TapBridge will pick up the addressing information from the ns-3 net device to which it is
connected (its “bridged” net device) and use that information to create and configure the TAP device on
the real host.
The end result of this is a situation where one can, for example, use the standard ping utility on a real
host to ping a simulated ns-3 node. If correct routes are added to the internet host (this is expected
to be done automatically in future ns-3 releases), the routing systems in ns-3 will enable correct
routing of the packets across simulated ns-3 networks. For an example of this, see the example program,
tap-wifi-dumbbell.cc in the ns-3 distribution.
The Tap Bridge lives in a kind of a gray world somewhere between a Linux host and an ns-3 bridge device.
From the Linux perspective, this code appears as the user mode handler for a TAP net device. In
ConfigureLocal mode, this Tap device is automatically created by the ns-3 simulation. When the Linux
host writes to one of these automatically created /dev/tap devices, the write is redirected into the
TapBridge that lives in the ns-3 world; and from this perspective, the packet write on Linux becomes a
packet read in the Tap Bridge. In other words, a Linux process writes a packet to a tap device and this
packet is redirected by the network tap mechanism toan ns-3 process where it is received by the TapBridge
as a result of a read operation there. The TapBridge then writes the packet to the ns-3 net device to
which it is bridged; and therefore it appears as if the Linux host sent a packet directly through an ns-3
net device onto an ns-3 network.
In the other direction, a packet received by the ns-3 net device connected to the Tap Bridge is sent via
a receive callback to the TapBridge. The TapBridge then takes that packet and writes it back to the host
using the network tap mechanism. This write to the device will appear to the Linux host as if a packet
has arrived on a net device; and therefore as if a packet received by the ns-3 net device during a
simulation has appeared on a real Linux net device.
The upshot is that the Tap Bridge appears to bridge a tap device on a Linux host in the “real world” to
an ns-3 net device in the simulation. Because the TAP device and the bridged ns-3 net device have the
same MAC address and the network tap IPC link is not externalized, this particular kind of bridge makes
it appear that a ns-3 net device is actually installed in the Linux host.
In order to implement this on the ns-3 side, we need a “ghost node” in the simulation to hold the bridged
ns-3 net device and the TapBridge. This node should not actually do anything else in the simulation
since its job is simply to make the net device appear in Linux. This is not just arbitrary policy, it is
because:
• Bits sent to the TapBridge from higher layers in the ghost node (using the TapBridge Send method) are
completely ignored. The TapBridge is not, itself, connected to any network, neither in Linux nor in
ns-3. You can never send nor receive data over a TapBridge from the ghost node.
• The bridged ns-3 net device has its receive callback disconnected from the ns-3 node and reconnected to
the Tap Bridge. All data received by a bridged device will then be sent to the Linux host and will not
be received by the node. From the perspective of the ghost node, you can send over this device but you
cannot ever receive.
Of course, if you understand all of the issues you can take control of your own destiny and do whatever
you want – we do not actively prevent you from using the ghost node for anything you decide. You will be
able to perform typical ns-3 operations on the ghost node if you so desire. The internet stack, for
example, must be there and functional on that node in order to participate in IP address assignment and
global routing. However, as mentioned above, interfaces talking to any TapBridge or associated bridged
net devices will not work completely. If you understand exactly what you are doing, you can set up other
interfaces and devices on the ghost node and use them; or take advantage of the operational send side of
the bridged devices to create traffic generators. We generally recommend that you treat this node as a
ghost of the Linux host and leave it to itself, though.
TapBridge UseLocal Mode Operation
As described in above, the TapBridge acts like a bridge from the “real” world into the simulated ns-3
world. In the case of the ConfigureLocal mode, life is easy since the IP address of the Tap device
matches the IP address of the ns-3 device and the MAC address of the Tap device matches the MAC address
of the ns-3 device; and there is a one-to-one relationship between the devices.
Things are slightly complicated when a Tap device is externally configured with a different MAC address
than the ns-3 net device. The conventional way to deal with this kind of difference is to use
promiscuous mode in the bridged device to receive packets destined for the different MAC address and
forward them off to Linux. In order to move packets the other way, the conventional solution is
SendFrom() which allows a caller to “spoof” or change the source MAC address to match the different Linux
MAC address.
We do have a specific requirement to be able to bridge Linux Virtual Machines onto wireless STA nodes.
Unfortunately, the 802.11 spec doesn’t provide a good way to implement SendFrom(), so we have to work
around that problem.
To this end, we provided the UseLocal mode of the Tap Bridge. This mode allows you approach the problem
as if you were creating a bridge with a single net device. A single allowed address on the Linux side is
remembered in the TapBridge, and all packets coming from the Linux side are repeated out the ns-3 side
using the ns-3 device MAC source address. All packets coming in from the ns-3 side are repeated out the
Linux side using the remembered MAC address. This allows us to use Send() on the ns-3 device side which
is available on all ns-3 net devices.
UseLocal mode is identical to the ConfigureLocal mode except for the creation and configuration of the
tap device and the MAC address spoofing.
TapBridge UseBridge Operation
As described in the ConfigureLocal mode section, when the Linux host writes to one of the /dev/tap
devices, the write is redirected into the TapBridge that lives in the ns-3 world. In the case of the
UseBridge mode, these packets will need to be sent out on the ns-3 network as if they were sent on a
device participating in the Linux bridge. This means calling the SendFrom() method on the bridged device
and providing the source MAC address found in the packet.
In the other direction, a packet received by an ns-3 net device is hooked via callback to the TapBridge.
This must be done in promiscuous mode since the goal is to bridge the ns-3 net device onto the OS (brctl)
bridge of which the TAP device is a part.
For these reasons, only ns-3 net devices that support SendFrom() and have a hookable promiscuous receive
callback are allowed to participate in UseBridge mode TapBridge configurations.
Tap Bridge Channel Model
There is no channel model associated with the Tap Bridge. In fact, the intention is make it appear that
the real internet host is connected to the channel of the bridged net device.
Tap Bridge Tracing Model
Unlike most ns-3 devices, the TapBridge does not provide any standard trace sources. This is because the
bridge is an intermediary that is essentially one function call away from the bridged device. We expect
that the trace hooks in the bridged device will be sufficient for most users,
Using the TapBridge
We expect that most users will interact with the TapBridge device through the TapBridgeHelper. Users of
other helper classes, such as CSMA or Wifi, should be comfortable with the idioms used there.
ENERGY FRAMEWORK
Energy consumption is a key issue for wireless devices, and wireless network researchers often need to
investigate the energy consumption at a node or in the overall network while running network simulations
in ns-3. This requires ns-3 to support energy consumption modeling. Further, as concepts such as fuel
cells and energy scavenging are becoming viable for low power wireless devices, incorporating the effect
of these emerging technologies into simulations requires support for modeling diverse energy sources in
ns-3. The ns-3 Energy Framework provides the basis for energy consumption, energy source and energy
harvesting modeling.
Model Description
The source code for the Energy Framework is currently at: src/energy.
Design
The ns-3 Energy Framework is composed of 3 parts: Energy Source, Device Energy Model and Energy
Harvester. The framework is implemented into the src/energy/models folder.
Energy Source
The Energy Source represents the power supply on each node. A node can have one or more energy sources,
and each energy source can be connected to multiple device energy models. Connecting an energy source to
a device energy model implies that the corresponding device draws power from the source. The basic
functionality of the Energy Source is to provide energy for devices on the node. When energy is
completely drained from the Energy Source, it notifies the devices on node such that each device can
react to this event. Further, each node can access the Energy Source Objects for information such as
remaining energy or energy fraction (battery level). This enables the implementation of energy aware
protocols in ns-3.
In order to model a wide range of power supplies such as batteries, the Energy Source must be able to
capture characteristics of these supplies. There are 2 important characteristics or effects related to
practical batteries:
Rate Capacity Effect
Decrease of battery lifetime when the current draw is higher than the rated value of the battery.
Recovery Effect
Increase of battery lifetime when the battery is alternating between discharge and idle states.
In order to incorporate the Rate Capacity Effect, the Energy Source uses current draw from all the
devices on the same node to calculate energy consumption. Moreover, multiple Energy Harvesters can be
connected to the Energy Source in order to replenish its energy. The Energy Source periodically polls all
the devices and energy harvesters on the same node to calculate the total current drain and hence the
energy consumption. When a device changes state, its corresponding Device Energy Model will notify the
Energy Source of this change and new total current draw will be calculated. Similarly, every Energy
Harvester update triggers an update to the connected Energy Source.
The Energy Source base class keeps a list of devices (Device Energy Model objects) and energy harvesters
(Energy Harvester objects) that are using the particular Energy Source as power supply. When energy is
completely drained, the Energy Source will notify all devices on this list. Each device can then handle
this event independently, based on the desired behavior that should be followed in case of power outage.
Device Energy Model
The Device Energy Model is the energy consumption model of a device installed on the node. It is designed
to be a state based model where each device is assumed to have a number of states, and each state is
associated with a power consumption value. Whenever the state of the device changes, the corresponding
Device Energy Model will notify the Energy Source of the new current draw of the device. The Energy
Source will then calculate the new total current draw and update the remaining energy.
The Device Energy Model can also be used for devices that do not have finite number of states. For
example, in an electric vehicle, the current draw of the motor is determined by its speed. Since the
vehicle’s speed can take continuous values within a certain range, it is infeasible to define a set of
discrete states of operation. However, by converting the speed value into current directly, the same set
of Device Energy Model APIs can still be used.
Energy Harvester
The energy harvester represents the elements that harvest energy from the environment and recharge the
Energy Source to which it is connected. The energy harvester includes the complete implementation of the
actual energy harvesting device (e.g., a solar panel) and the environment (e.g., the solar radiation).
This means that in implementing an energy harvester, the energy contribution of the environment and the
additional energy requirements of the energy harvesting device such as the conversion efficiency and the
internal power consumption of the device needs to be jointly modeled.
WiFi Radio Energy Model
The WiFi Radio Energy Model is the energy consumption model of a Wifi net device. It provides a state for
each of the available states of the PHY layer: Idle, CcaBusy, Tx, Rx, ChannelSwitch, Sleep, Off. Each of
such states is associated with a value (in Ampere) of the current draw (see below for the corresponding
attribute names). A Wifi Radio Energy Model PHY Listener is registered to the Wifi PHY in order to be
notified of every Wifi PHY state transition. At every transition, the energy consumed in the previous
state is computed and the energy source is notified in order to update its remaining energy.
The Wifi Tx Current Model gives the possibility to compute the current draw in the transmit state as a
function of the nominal tx power (in dBm), as observed in several experimental measurements. To this
purpose, the Wifi Radio Energy Model PHY Listener is notified of the nominal tx power used to transmit
the current frame and passes such a value to the Wifi Tx Current Model which takes care of updating the
current draw in the Tx state. Hence, the energy consumption is correctly computed even if the Wifi Remote
Station Manager performs per-frame power control. Currently, a Linear Wifi Tx Current Model is
implemented which computes the tx current as a linear function (according to parameters that can be
specified by the user) of the nominal tx power in dBm.
The Wifi Radio Energy Model offers the possibility to specify a callback that is invoked when the energy
source is depleted. If such a callback is not specified when the Wifi Radio Energy Model Helper is used
to install the model on a device, a callback is implicitly made so that the Wifi PHY is put in the OFF
mode (hence no frame is transmitted nor received afterwards) when the energy source is depleted.
Likewise, it is possible to specify a callback that is invoked when the energy source is recharged (which
might occur in case an energy harvester is connected to the energy source). If such a callback is not
specified when the Wifi Radio Energy Model Helper is used to install the model on a device, a callback is
implicitly made so that the Wifi PHY is resumed from the OFF mode when the energy source is recharged.
Future Work
For Device Energy Models, we are planning to include support for other PHY layer models provided in ns-3
such as WiMAX, and to model the energy consumptions of other non communicating devices, like a generic
sensor and a CPU. For Energy Sources, we are planning to included new types of Energy Sources such as
Supercapacitor and Nickel-Metal Hydride (Ni-MH) battery. For the Energy Harvesters, we are planning to
implement an energy harvester that recharges the energy sources according to the power levels defined in
a user customizable dataset of real measurements.
References
[1] ns-2 Energy model: http://www.cubinlab.ee.unimelb.edu.au/~jrid/Docs/Manuel-NS2/node204.html
[2] H. Wu, S. Nabar and R. Poovendran. An Energy Framework for the Network Simulator 3 (ns-3).
[3] M. Handy and D. Timmermann. Simulation of mobile wireless networks with accurate modelling of
non-linear battery effects. In Proc. of Applied simulation and Modeling (ASM), 2003.
[4] D. N. Rakhmatov and S. B. Vrudhula. An analytical high-level battery model for use in energy
management of portable electronic systems. In Proc. of IEEE/ACM International Conference on Computer
Aided Design (ICCAD’01), pages 488-493, November 2001.
[5] D. N. Rakhmatov, S. B. Vrudhula, and D. A. Wallach. Battery lifetime prediction for energy-aware
computing. In Proc. of the 2002 International Symposium on Low Power Electronics and Design
(ISLPED’02), pages 154-159, 2002.
[6] C. Tapparello, H. Ayatollahi and W. Heinzelman. Extending the Energy Framework for Network Simulator
3 (ns-3). Workshop on ns-3 (WNS3), Poster Session, Atlanta, GA, USA. May, 2014.
[7] C. Tapparello, H. Ayatollahi and W. Heinzelman. Energy Harvesting Framework for Network Simulator 3
(ns-3). 2nd International Workshop on Energy Neutral Sensing Systems (ENSsys), Memphis, TN, USA.
November 6, 2014.
Usage
The main way that ns-3 users will typically interact with the Energy Framework is through the helper API
and through the publicly visible attributes of the framework. The helper API is defined in
src/energy/helper/*.h.
In order to use the energy framework, the user must install an Energy Source for the node of interest,
the corresponding Device Energy Model for the network devices and, if necessary, the one or more Energy
Harvester. Energy Source (objects) are aggregated onto each node by the Energy Source Helper. In order to
allow multiple energy sources per node, we aggregate an Energy Source Container rather than directly
aggregating a source object.
The Energy Source object keeps a list of Device Energy Model and Energy Harvester objects using the
source as power supply. Device Energy Model objects are installed onto the Energy Source by the Device
Energy Model Helper, while Energy Harvester object are installed by the Energy Harvester Helper. User can
access the Device Energy Model objects through the Energy Source object to obtain energy consumption
information of individual devices. Moreover, the user can access to the Energy Harvester objects in order
to gather information regarding the current harvestable power and the total energy harvested by the
harvester.
Examples
The example directories, src/examples/energy and examples/energy, contain some basic code that shows how
to set up the framework.
Helpers
Energy Source Helper
Base helper class for Energy Source objects, this helper Aggregates Energy Source object onto a node.
Child implementation of this class creates the actual Energy Source object.
Device Energy Model Helper
Base helper class for Device Energy Model objects, this helper attaches Device Energy Model objects onto
Energy Source objects. Child implementation of this class creates the actual Device Energy Model object.
Energy Harvesting Helper
Base helper class for Energy Harvester objects, this helper attaches Energy Harvester objects onto Energy
Source objects. Child implementation of this class creates the actual Energy Harvester object.
Attributes
Attributes differ between Energy Sources, Devices Energy Models and Energy Harvesters implementations,
please look at the specific child class for details.
Basic Energy Source
• BasicEnergySourceInitialEnergyJ: Initial energy stored in basic energy source.
• BasicEnergySupplyVoltageV: Initial supply voltage for basic energy source.
• PeriodicEnergyUpdateInterval: Time between two consecutive periodic energy updates.
RV Battery Model
• RvBatteryModelPeriodicEnergyUpdateInterval: RV battery model sampling interval.
• RvBatteryModelOpenCircuitVoltage: RV battery model open circuit voltage.
• RvBatteryModelCutoffVoltage: RV battery model cutoff voltage.
• RvBatteryModelAlphaValue: RV battery model alpha value.
• RvBatteryModelBetaValue: RV battery model beta value.
• RvBatteryModelNumOfTerms: The number of terms of the infinite sum for estimating battery level.
WiFi Radio Energy Model
• IdleCurrentA: The default radio Idle current in Ampere.
• CcaBusyCurrentA: The default radio CCA Busy State current in Ampere.
• TxCurrentA: The radio Tx current in Ampere.
• RxCurrentA: The radio Rx current in Ampere.
• SwitchingCurrentA: The default radio Channel Switch current in Ampere.
• SleepCurrentA: The radio Sleep current in Ampere.
• TxCurrentModel: A pointer to the attached tx current model.
Basic Energy Harvester
• PeriodicHarvestedPowerUpdateInterval: Time between two consecutive periodic updates of the harvested
power.
• HarvestablePower: Random variables that represents the amount of power that is provided by the energy
harvester.
Tracing
Traced values differ between Energy Sources, Devices Energy Models and Energy Harvesters implementations,
please look at the specific child class for details.
Basic Energy Source
• RemainingEnergy: Remaining energy at BasicEnergySource.
RV Battery Model
• RvBatteryModelBatteryLevel: RV battery model battery level.
• RvBatteryModelBatteryLifetime: RV battery model battery lifetime.
WiFi Radio Energy Model
• TotalEnergyConsumption: Total energy consumption of the radio device.
Basic Energy Harvester
• HarvestedPower: Current power provided by the BasicEnergyHarvester.
• TotalEnergyHarvested: Total energy harvested by the BasicEnergyHarvester.
Validation
Comparison of the Energy Framework against actual devices have not been performed. Current implementation
of the Energy Framework is checked numerically for computation errors. The RV battery model is validated
by comparing results with what was presented in the original RV battery model paper.
FLOW MONITOR
Model Description
The source code for the new module lives in the directory src/flow-monitor.
The Flow Monitor module goal is to provide a flexible system to measure the performance of network
protocols. The module uses probes, installed in network nodes, to track the packets exchanged by the
nodes, and it will measure a number of parameters. Packets are divided according to the flow they belong
to, where each flow is defined according to the probe’s characteristics (e.g., for IP, a flow is defined
as the packets with the same {protocol, source (IP, port), destination (IP, port)} tuple.
The statistics are collected for each flow can be exported in XML format. Moreover, the user can access
the probes directly to request specific stats about each flow.
Design
Flow Monitor module is designed in a modular way. It can be extended by subclassing ns3::FlowProbe and
ns3::FlowClassifier. Typically, a subclass of ns3::FlowProbe works by listening to the appropriate class
Traces, and then uses its own ns3::FlowClassifier subclass to classify the packets passing though each
node.
Each Probe can try to listen to other classes traces (e.g., ns3::Ipv4FlowProbe will try to use any
ns3::NetDevice trace named TxQueue/Drop) but this is something that the user should not rely into
blindly, because the trace is not guaranteed to be in every type of ns3::NetDevice. As an example,
CsmaNetDevice and PointToPointNetDevice have a TxQueue/Drop trace, while WiFiNetDevice does not.
The full module design is described in [FlowMonitor]
Scope and Limitations
At the moment, probes and classifiers are available only for IPv4 and IPv6.
IPv4 and IPv6 probes will classify packets in four points:
• When a packet is sent (SendOutgoing IPv[4,6] traces)
• When a packet is forwarded (UnicastForward IPv[4,6] traces)
• When a packet is received (LocalDeliver IPv[4,6] traces)
• When a packet is dropped (Drop IPv[4,6] traces)
Since the packets are tracked at IP level, any retransmission caused by L4 protocols (e.g., TCP) will be
seen by the probe as a new packet.
A Tag will be added to the packet (ns3::Ipv[4,6]FlowProbeTag). The tag will carry basic packet’s data,
useful for the packet’s classification.
It must be underlined that only L4 (TCP, UDP) packets are, so far, classified. Moreover, only unicast
packets will be classified. These limitations may be removed in the future.
The data collected for each flow are:
• timeFirstTxPacket: when the first packet in the flow was transmitted;
• timeLastTxPacket: when the last packet in the flow was transmitted;
• timeFirstRxPacket: when the first packet in the flow was received by an end node;
• timeLastRxPacket: when the last packet in the flow was received;
• delaySum: the sum of all end-to-end delays for all received packets of the flow;
• jitterSum: the sum of all end-to-end delay jitter (delay variation) values for all received packets of
the flow, as defined in RFC 3393;
• txBytes, txPackets: total number of transmitted bytes / packets for the flow;
• rxBytes, rxPackets: total number of received bytes / packets for the flow;
• lostPackets: total number of packets that are assumed to be lost (not reported over 10 seconds);
• timesForwarded: the number of times a packet has been reportedly forwarded;
• delayHistogram, jitterHistogram, packetSizeHistogram: histogram versions for the delay, jitter, and
packet sizes, respectively;
• packetsDropped, bytesDropped: the number of lost packets and bytes, divided according to the loss
reason code (defined in the probe).
It is worth pointing out that the probes measure the packet bytes including IP headers. The L2 headers
are not included in the measure.
These stats will be written in XML form upon request (see the Usage section).
The “lost” packets problem
At the end of a simulation, Flow Monitor could report about “lost” packets, i.e., packets that Flow
Monitor have lost track of.
It is important to keep in mind that Flow Monitor records the packets statistics by intercepting them at
a given network level - let’s say at IP level. When the simulation ends, any packet queued for
transmission below the IP level will be considered as lost.
It is strongly suggested to consider this point when using Flow Monitor. The user can choose to:
• Ignore the lost packets (if their number is a statistically irrelevant quantity), or
• Stop the Applications before the actual Simulation End time, leaving enough time between the two for
the queued packets to be processed.
The second method is the suggested one. Usually a few seconds are enough (the exact value depends on the
network type).
It is important to stress that “lost” packets could be anywhere in the network, and could count toward
the received packets or the dropped ones. Ideally, their number should be zero or a minimal fraction of
the other ones, i.e., they should be “statistically irrelevant”.
References
[FlowMonitor]
G. Carneiro, P. Fortuna, and M. Ricardo. 2009. FlowMonitor: a network monitoring framework for the
network simulator 3 (NS-3). In Proceedings of the Fourth International ICST Conference on Performance
Evaluation Methodologies and Tools (VALUETOOLS ‘09).
http://dx.doi.org/10.4108/ICST.VALUETOOLS2009.7493 (Full text:
https://dl.acm.org/doi/abs/10.4108/ICST.VALUETOOLS2009.7493)
Usage
The module usage is extremely simple. The helper will take care of about everything.
The typical use is:
// Flow monitor
Ptr<FlowMonitor> flowMonitor;
FlowMonitorHelper flowHelper;
flowMonitor = flowHelper.InstallAll();
-yourApplicationsContainer-.Stop (Seconds (stop_time));;
Simulator::Stop (Seconds(stop_time+cleanup_time));
Simulator::Run ();
flowMonitor->SerializeToXmlFile("NameOfFile.xml", true, true);
the SerializeToXmlFile () function 2nd and 3rd parameters are used respectively to activate/deactivate
the histograms and the per-probe detailed stats. Other possible alternatives can be found in the Doxygen
documentation, while cleanup_time is the time needed by in-flight packets to reach their destinations.
Helpers
The helper API follows the pattern usage of normal helpers. Through the helper you can install the
monitor in the nodes, set the monitor attributes, and print the statistics.
One important thing is: the ns3::FlowMonitorHelper must be instantiated only once in the main.
Attributes
The module provides the following attributes in ns3::FlowMonitor:
• MaxPerHopDelay (Time, default 10s): The maximum per-hop delay that should be considered;
• StartTime (Time, default 0s): The time when the monitoring starts;
• DelayBinWidth (double, default 0.001): The width used in the delay histogram;
• JitterBinWidth (double, default 0.001): The width used in the jitter histogram;
• PacketSizeBinWidth (double, default 20.0): The width used in the packetSize histogram;
• FlowInterruptionsBinWidth (double, default 0.25): The width used in the flowInterruptions histogram;
• FlowInterruptionsMinTime (double, default 0.5): The minimum inter-arrival time that is considered a
flow interruption.
Output
The main model output is an XML formatted report about flow statistics. An example is:
<?xml version="1.0" ?>
<FlowMonitor>
<FlowStats>
<Flow flowId="1" timeFirstTxPacket="+0.0ns" timeFirstRxPacket="+20067198.0ns" timeLastTxPacket="+2235764408.0ns" timeLastRxPacket="+2255831606.0ns" delaySum="+138731526300.0ns" jitterSum="+1849692150.0ns" lastDelay="+20067198.0ns" txBytes="2149400" rxBytes="2149400" txPackets="3735" rxPackets="3735" lostPackets="0" timesForwarded="7466">
</Flow>
</FlowStats>
<Ipv4FlowClassifier>
<Flow flowId="1" sourceAddress="10.1.3.1" destinationAddress="10.1.2.2" protocol="6" sourcePort="49153" destinationPort="50000" />
</Ipv4FlowClassifier>
<Ipv6FlowClassifier>
</Ipv6FlowClassifier>
<FlowProbes>
<FlowProbe index="0">
<FlowStats flowId="1" packets="3735" bytes="2149400" delayFromFirstProbeSum="+0.0ns" >
</FlowStats>
</FlowProbe>
<FlowProbe index="2">
<FlowStats flowId="1" packets="7466" bytes="2224020" delayFromFirstProbeSum="+199415389258.0ns" >
</FlowStats>
</FlowProbe>
<FlowProbe index="4">
<FlowStats flowId="1" packets="3735" bytes="2149400" delayFromFirstProbeSum="+138731526300.0ns" >
</FlowStats>
</FlowProbe>
</FlowProbes>
</FlowMonitor>
The output was generated by a TCP flow from 10.1.3.1 to 10.1.2.2.
It is worth noticing that the index 2 probe is reporting more packets and more bytes than the other
probes. That’s a perfectly normal behaviour, as packets are fragmented at IP level in that node.
It should also be observed that the receiving node’s probe (index 4) doesn’t count the fragments, as the
reassembly is done before the probing point.
Examples
The examples are located in src/flow-monitor/examples.
Moreover, the following examples use the flow-monitor module:
• examples/matrix-topology/matrix-topology.cc
• examples/routing/manet-routing-compare.cc
• examples/routing/simple-global-routing.cc
• examples/tcp/tcp-variants-comparison.cc
• examples/wireless/multirate.cc
• examples/wireless/wifi-hidden-terminal.cc
Troubleshooting
Do not define more than one ns3::FlowMonitorHelper in the simulation.
Validation
The paper in the references contains a full description of the module validation against a test network.
Tests are provided to ensure the Histogram correct functionality.
INTERNET MODELS (IP, TCP, ROUTING, UDP, INTERNET APPLICATIONS)
Internet Stack
Internet stack aggregation
A bare class Node is not very useful as-is; other objects must be aggregated to it to provide useful node
functionality.
The ns-3 source code directory src/internet provides implementation of TCP/IPv4- and IPv6-related
components. These include IPv4, ARP, UDP, TCP, IPv6, Neighbor Discovery, and other related protocols.
Internet Nodes are not subclasses of class Node; they are simply Nodes that have had a bunch of
IP-related objects aggregated to them. They can be put together by hand, or via a helper function
InternetStackHelper::Install () which does the following to all nodes passed in as arguments:
void
InternetStackHelper::Install (Ptr<Node> node) const
{
if (m_ipv4Enabled)
{
/* IPv4 stack */
if (node->GetObject<Ipv4> () != 0)
{
NS_FATAL_ERROR ("InternetStackHelper::Install (): Aggregating "
"an InternetStack to a node with an existing Ipv4 object");
return;
}
CreateAndAggregateObjectFromTypeId (node, "ns3::ArpL3Protocol");
CreateAndAggregateObjectFromTypeId (node, "ns3::Ipv4L3Protocol");
CreateAndAggregateObjectFromTypeId (node, "ns3::Icmpv4L4Protocol");
// Set routing
Ptr<Ipv4> ipv4 = node->GetObject<Ipv4> ();
Ptr<Ipv4RoutingProtocol> ipv4Routing = m_routing->Create (node);
ipv4->SetRoutingProtocol (ipv4Routing);
}
if (m_ipv6Enabled)
{
/* IPv6 stack */
if (node->GetObject<Ipv6> () != 0)
{
NS_FATAL_ERROR ("InternetStackHelper::Install (): Aggregating "
"an InternetStack to a node with an existing Ipv6 object");
return;
}
CreateAndAggregateObjectFromTypeId (node, "ns3::Ipv6L3Protocol");
CreateAndAggregateObjectFromTypeId (node, "ns3::Icmpv6L4Protocol");
// Set routing
Ptr<Ipv6> ipv6 = node->GetObject<Ipv6> ();
Ptr<Ipv6RoutingProtocol> ipv6Routing = m_routingv6->Create (node);
ipv6->SetRoutingProtocol (ipv6Routing);
/* register IPv6 extensions and options */
ipv6->RegisterExtensions ();
ipv6->RegisterOptions ();
}
if (m_ipv4Enabled || m_ipv6Enabled)
{
/* UDP and TCP stacks */
CreateAndAggregateObjectFromTypeId (node, "ns3::UdpL4Protocol");
node->AggregateObject (m_tcpFactory.Create<Object> ());
Ptr<PacketSocketFactory> factory = CreateObject<PacketSocketFactory> ();
node->AggregateObject (factory);
}
}
Where multiple implementations exist in ns-3 (TCP, IP routing), these objects are added by a factory
object (TCP) or by a routing helper (m_routing).
Note that the routing protocol is configured and set outside this function. By default, the following
protocols are added:
void InternetStackHelper::Initialize ()
{
SetTcp ("ns3::TcpL4Protocol");
Ipv4StaticRoutingHelper staticRouting;
Ipv4GlobalRoutingHelper globalRouting;
Ipv4ListRoutingHelper listRouting;
Ipv6ListRoutingHelper listRoutingv6;
Ipv6StaticRoutingHelper staticRoutingv6;
listRouting.Add (staticRouting, 0);
listRouting.Add (globalRouting, -10);
listRoutingv6.Add (staticRoutingv6, 0);
SetRoutingHelper (listRouting);
SetRoutingHelper (listRoutingv6);
}
By default, IPv4 and IPv6 are enabled.
Internet Node structure
An IP-capable Node (an ns-3 Node augmented by aggregation to have one or more IP stacks) has the
following internal structure.
Layer-3 protocols
At the lowest layer, sitting above the NetDevices, are the “layer 3” protocols, including IPv4, IPv6, ARP
and so on. The class Ipv4L3Protocol is an implementation class whose public interface is typically class
Ipv4, but the Ipv4L3Protocol public API is also used internally at present.
In class Ipv4L3Protocol, one method described below is Receive ():
/**
* Lower layer calls this method after calling L3Demux::Lookup
* The ARP subclass needs to know from which NetDevice this
* packet is coming to:
* - implement a per-NetDevice ARP cache
* - send back arp replies on the right device
*/
void Receive( Ptr<NetDevice> device, Ptr<const Packet> p, uint16_t protocol,
const Address &from, const Address &to, NetDevice::PacketType packetType);
First, note that the Receive () function has a matching signature to the ReceiveCallback in the class
Node. This function pointer is inserted into the Node’s protocol handler when AddInterface () is called.
The actual registration is done with a statement such as follows:
RegisterProtocolHandler ( MakeCallback (&Ipv4Protocol::Receive, ipv4),
Ipv4L3Protocol::PROT_NUMBER, 0);
The Ipv4L3Protocol object is aggregated to the Node; there is only one such Ipv4L3Protocol object.
Higher-layer protocols that have a packet to send down to the Ipv4L3Protocol object can call
GetObject<Ipv4L3Protocol> () to obtain a pointer, as follows:
Ptr<Ipv4L3Protocol> ipv4 = m_node->GetObject<Ipv4L3Protocol> ();
if (ipv4 != 0)
{
ipv4->Send (packet, saddr, daddr, PROT_NUMBER);
}
This class nicely demonstrates two techniques we exploit in ns-3 to bind objects together: callbacks,
and object aggregation.
Once IPv4 routing has determined that a packet is for the local node, it forwards it up the stack. This
is done with the following function:
void
Ipv4L3Protocol::LocalDeliver (Ptr<const Packet> packet, Ipv4Header const&ip, uint32_t iif)
The first step is to find the right Ipv4L4Protocol object, based on IP protocol number. For instance, TCP
is registered in the demux as protocol number 6. Finally, the Receive() function on the Ipv4L4Protocol
(such as TcpL4Protocol::Receive is called.
We have not yet introduced the class Ipv4Interface. Basically, each NetDevice is paired with an IPv4
representation of such device. In Linux, this class Ipv4Interface roughly corresponds to the struct
in_device; the main purpose is to provide address-family specific information (addresses) about an
interface.
All the classes have appropriate traces in order to track sent, received and lost packets. The users is
encouraged to use them so to find out if (and where) a packet is dropped. A common mistake is to forget
the effects of local queues when sending packets, e.g., the ARP queue. This can be particularly puzzling
when sending jumbo packets or packet bursts using UDP. The ARP cache pending queue is limited (3
datagrams) and IP packets might be fragmented, easily overfilling the ARP cache queue size. In those
cases it is useful to increase the ARP cache pending size to a proper value, e.g.:
Config::SetDefault ("ns3::ArpCache::PendingQueueSize", UintegerValue (MAX_BURST_SIZE/L2MTU*3));
The IPv6 implementation follows a similar architecture. Dual-stacked nodes (one with support for both
IPv4 and IPv6) will allow an IPv6 socket to receive IPv4 connections as a standard dual-stacked system
does. A socket bound and listening to an IPv6 endpoint can receive an IPv4 connection and will return
the remote address as an IPv4-mapped address. Support for the IPV6_V6ONLY socket option does not
currently exist.
Layer-4 protocols and sockets
We next describe how the transport protocols, sockets, and applications tie together. In summary, each
transport protocol implementation is a socket factory. An application that needs a new socket
For instance, to create a UDP socket, an application would use a code snippet such as the following:
Ptr<Udp> udpSocketFactory = GetNode ()->GetObject<Udp> ();
Ptr<Socket> m_socket = socketFactory->CreateSocket ();
m_socket->Bind (m_local_address);
...
The above will query the node to get a pointer to its UDP socket factory, will create one such socket,
and will use the socket with an API similar to the C-based sockets API, such as Connect () and Send ().
The address passed to the Bind (), Connect (), or Send () functions may be a Ipv4Address, Ipv6Address, or
Address. If a Address is passed in and contains anything other than a Ipv4Address or Ipv6Address, these
functions will return an error. The Bind (void) and Bind6 (void) functions bind to “0.0.0.0” and “::”
respectively.
The socket can also be bound to a specific NetDevice though the BindToNetDevice (Ptr<NetDevice>
netdevice) function. BindToNetDevice (Ptr<NetDevice> netdevice) will bind the socket to “0.0.0.0” and
“::” (equivalent to calling Bind () and Bind6 (), unless the socket has been already bound to a specific
address. Summarizing, the correct sequence is:
Ptr<Udp> udpSocketFactory = GetNode ()->GetObject<Udp> ();
Ptr<Socket> m_socket = socketFactory->CreateSocket ();
m_socket->BindToNetDevice (n_netDevice);
...
or:
Ptr<Udp> udpSocketFactory = GetNode ()->GetObject<Udp> ();
Ptr<Socket> m_socket = socketFactory->CreateSocket ();
m_socket->Bind (m_local_address);
m_socket->BindToNetDevice (n_netDevice);
...
The following raises an error:
Ptr<Udp> udpSocketFactory = GetNode ()->GetObject<Udp> ();
Ptr<Socket> m_socket = socketFactory->CreateSocket ();
m_socket->BindToNetDevice (n_netDevice);
m_socket->Bind (m_local_address);
...
See the chapter on ns-3 sockets for more information.
We have described so far a socket factory (e.g. class Udp) and a socket, which may be specialized (e.g.,
class UdpSocket). There are a few more key objects that relate to the specialized task of demultiplexing
a packet to one or more receiving sockets. The key object in this task is class Ipv4EndPointDemux. This
demultiplexer stores objects of class Ipv4EndPoint. This class holds the addressing/port tuple (local
port, local address, destination port, destination address) associated with the socket, and a receive
callback. This receive callback has a receive function registered by the socket. The Lookup () function
to Ipv4EndPointDemux returns a list of Ipv4EndPoint objects (there may be a list since more than one
socket may match the packet). The layer-4 protocol copies the packet to each Ipv4EndPoint and calls its
ForwardUp () method, which then calls the Receive () function registered by the socket.
An issue that arises when working with the sockets API on real systems is the need to manage the reading
from a socket, using some type of I/O (e.g., blocking, non-blocking, asynchronous, …). ns-3 implements
an asynchronous model for socket I/O; the application sets a callback to be notified of received data
ready to be read, and the callback is invoked by the transport protocol when data is available. This
callback is specified as follows:
void Socket::SetRecvCallback (Callback<void, Ptr<Socket>,
Ptr<Packet>,
const Address&> receivedData);
The data being received is conveyed in the Packet data buffer. An example usage is in class PacketSink:
m_socket->SetRecvCallback (MakeCallback(&PacketSink::HandleRead, this));
To summarize, internally, the UDP implementation is organized as follows:
• a UdpImpl class that implements the UDP socket factory functionality
• a UdpL4Protocol class that implements the protocol logic that is socket-independent
• a UdpSocketImpl class that implements socket-specific aspects of UDP
• a class called Ipv4EndPoint that stores the addressing tuple (local port, local address, destination
port, destination address) associated with the socket, and a receive callback for the socket.
IP-capable node interfaces
Many of the implementation details, or internal objects themselves, of IP-capable Node objects are not
exposed at the simulator public API. This allows for different implementations; for instance, replacing
the native ns-3 models with ported TCP/IP stack code.
The C++ public APIs of all of these objects is found in the src/network directory, including principally:
• address.h
• socket.h
• node.h
• packet.h
These are typically base class objects that implement the default values used in the implementation,
implement access methods to get/set state variables, host attributes, and implement publicly-available
methods exposed to clients such as CreateSocket.
Example path of a packet
These two figures show an example stack trace of how packets flow through the Internet Node objects.
[image] Send path of a packet..UNINDENT
[image] Receive path of a packet..UNINDENT
IPv4
This chapter describes the ns-3 IPv4 address assignment and basic components tracking.
IPv4 addresses assignment
In order to use IPv4 on a network, the first thing to do is assigning IPv4 addresses.
Any IPv4-enabled ns-3 node will have at least one NetDevice: the ns3::LoopbackNetDevice. The loopback
device address is 127.0.0.1. All the other NetDevices will have one (or more) IPv4 addresses.
Note that, as today, ns-3 does not have a NAT module, and it does not follows the rules about filtering
private addresses (RFC 1918): 10.0.0.0/8, 172.16.0.0/12, and 192.168.0.0/16. These addresses are routed
as any other address. This behaviour could change in the future.
IPv4 global addresses can be:
• manually assigned
• assigned though DHCP
ns-3 can use both methods, and it’s quite important to understand the implications of both.
Manually assigned IPv4 addresses
This is probably the easiest and most used method. As an example:
Ptr<Node> n0 = CreateObject<Node> ();
Ptr<Node> n1 = CreateObject<Node> ();
NodeContainer net (n0, n1);
CsmaHelper csma;
NetDeviceContainer ndc = csma.Install (net);
NS_LOG_INFO ("Assign IPv4 Addresses.");
Ipv4AddressHelper ipv4;
ipv4.SetBase (Ipv4Address ("192.168.1.0"), NetMask ("/24"));
Ipv4InterfaceContainer ic = ipv4.Assign (ndc);
This method will add two global IPv4 addresses to the nodes.
Note that the addresses are assigned in sequence. As a consequence, the first Node / NetDevice will have
“192.168.1.1”, the second “192.168.1.2” and so on.
It is possible to repeat the above to assign more than one address to a node. However, due to the
Ipv4AddressHelper singleton nature, one should first assign all the addresses of a network, then change
the network base (SetBase), then do a new assignment.
Alternatively, it is possible to assign a specific address to a node:
Ptr<Node> n0 = CreateObject<Node> ();
NodeContainer net (n0);
CsmaHelper csma;
NetDeviceContainer ndc = csma.Install (net);
NS_LOG_INFO ("Specifically Assign an IPv4 Address.");
Ipv4AddressHelper ipv4;
Ptr<NetDevice> device = ndc.Get (0);
Ptr<Node> node = device->GetNode ();
Ptr<Ipv4> ipv4proto = node->GetObject<Ipv4> ();
int32_t ifIndex = 0;
ifIndex = ipv4proto->GetInterfaceForDevice (device);
Ipv4InterfaceAddress ipv4Addr = Ipv4InterfaceAddress (Ipv4Address ("192.168.1.42"), NetMask ("/24"));
ipv4proto->AddAddress (ifIndex, ipv4Addr);
DHCP assigned IPv4 addresses
DHCP is available in the internet-apps module. In order to use DHCP you have to have a DhcpServer
application in a node (the DHC server node) and a DhcpClient application in each of the nodes. Note that
it not necessary that all the nodes in a subnet use DHCP. Some nodes can have static addresses.
All the DHCP setup is performed though the DhcpHelper class. A complete example is in
src/internet-apps/examples/dhcp-example.cc.
Further info about the DHCP functionalities can be found in the internet-apps model documentation.
Tracing in the IPv4 Stack
The internet stack provides a number of trace sources in its various protocol implementations. These
trace sources can be hooked using your own custom trace code, or you can use our helper functions in some
cases to arrange for tracing to be enabled.
Tracing in ARP
ARP provides two trace hooks, one in the cache, and one in the layer three protocol. The trace accessor
in the cache is given the name “Drop.” When a packet is transmitted over an interface that requires ARP,
it is first queued for transmission in the ARP cache until the required MAC address is resolved. There
are a number of retries that may be done trying to get the address, and if the maximum retry count is
exceeded the packet in question is dropped by ARP. The single trace hook in the ARP cache is called,
• If an outbound packet is placed in the ARP cache pending address resolution and no resolution can be
made within the maximum retry count, the outbound packet is dropped and this trace is fired;
A second trace hook lives in the ARP L3 protocol (also named “Drop”) and may be called for a number of
reasons.
• If an ARP reply is received for an entry that is not waiting for a reply, the ARP reply packet is
dropped and this trace is fired;
• If an ARP reply is received for a non-existent entry, the ARP reply packet is dropped and this trace is
fired;
• If an ARP cache entry is in the DEAD state (has timed out) and an ARP reply packet is received, the
reply packet is dropped and this trace is fired.
• Each ARP cache entry has a queue of pending packets. If the size of the queue is exceeded, the
outbound packet is dropped and this trace is fired.
Tracing in IPv4
The IPv4 layer three protocol provides three trace hooks. These are the “Tx”
(ns3::Ipv4L3Protocol::m_txTrace), “Rx” (ns3::Ipv4L3Protocol::m_rxTrace) and “Drop”
(ns3::Ipv4L3Protocol::m_dropTrace) trace sources.
The “Tx” trace is fired in a number of situations, all of which indicate that a given packet is about to
be sent down to a given ns3::Ipv4Interface.
• In the case of a packet destined for the broadcast address, the Ipv4InterfaceList is iterated and for
every interface that is up and can fragment the packet or has a large enough MTU to transmit the
packet, the trace is hit. See ns3::Ipv4L3Protocol::Send.
• In the case of a packet that needs routing, the “Tx” trace may be fired just before a packet is sent to
the interface appropriate to the default gateway. See ns3::Ipv4L3Protocol::SendRealOut.
• Also in the case of a packet that needs routing, the “Tx” trace may be fired just before a packet is
sent to the outgoing interface appropriate to the discovered route. See
ns3::Ipv4L3Protocol::SendRealOut.
The “Rx” trace is fired when a packet is passed from the device up to the ns3::Ipv4L3Protocol::Receive
function.
• In the receive function, the Ipv4InterfaceList is iterated, and if the Ipv4Interface corresponding to
the receiving device is fount to be in the UP state, the trace is fired.
The “Drop” trace is fired in any case where the packet is dropped (in both the transmit and receive
paths).
• In the ns3::Ipv4Interface::Receive function, the packet is dropped and the drop trace is hit if the
interface corresponding to the receiving device is in the DOWN state.
• Also in the ns3::Ipv4Interface::Receive function, the packet is dropped and the drop trace is hit if
the checksum is found to be bad.
• In ns3::Ipv4L3Protocol::Send, an outgoing packet bound for the broadcast address is dropped and the
“Drop” trace is fired if the “don’t fragment” bit is set and fragmentation is available and required.
• Also in ns3::Ipv4L3Protocol::Send, an outgoing packet destined for the broadcast address is dropped and
the “Drop” trace is hit if fragmentation is not available and is required (MTU < packet size).
• In the case of a broadcast address, an outgoing packet is cloned for each outgoing interface. If any
of the interfaces is in the DOWN state, the “Drop” trace event fires with a reference to the copied
packet.
• In the case of a packet requiring a route, an outgoing packet is dropped and the “Drop” trace event
fires if no route to the remote host is found.
• In ns3::Ipv4L3Protocol::SendRealOut, an outgoing packet being routed is dropped and the “Drop” trace is
fired if the “don’t fragment” bit is set and fragmentation is available and required.
• Also in ns3::Ipv4L3Protocol::SendRealOut, an outgoing packet being routed is dropped and the “Drop”
trace is hit if fragmentation is not available and is required (MTU < packet size).
• An outgoing packet being routed is dropped and the “Drop” trace event fires if the required
Ipv4Interface is in the DOWN state.
• If a packet is being forwarded, and the TTL is exceeded (see ns3::Ipv4L3Protocol::DoForward), the
packet is dropped and the “Drop” trace event is fired.
Explicit Congestion Notification (ECN) bits
• In IPv4, ECN bits are the last 2 bits in TOS field and occupy 14th and 15th bits in the header.
• The IPv4 header class defines an EcnType enum with all four ECN codepoints (ECN_NotECT, ECN_ECT1,
ECN_ECT0, ECN_CE) mentioned in RFC 3168, and also a setter and getter method to handle ECN values in
the TOS field.
Ipv4QueueDiscItem
The traffic control sublayer in ns-3 handles objects of class QueueDiscItem which are used to hold an
ns3::Packet and an ns3::Header. This is done to facilitate the marking of packets for Explicit
Congestion Notification. The Mark () method is implemented in Ipv4QueueDiscItem. It returns true if
marking the packet is successful, i.e., it successfully sets the CE bit in the IPv4 header. The Mark ()
method will return false, however, if the IPv4 header indicates the ECN_NotECT codepoint.
RFC 6621 duplicate packet detection
To support mesh network protocols over broadcast-capable networks (e.g. Wi-Fi), it is useful to have
support for duplicate packet detection and filtering, since nodes in a network may receive multiple
copies of flooded multicast packets arriving on different paths. The Ipv4L3Protocol model in ns-3 has a
model for hash-based duplicate packet detection (DPD) based on Section 6.2.2 of (RFC 6621). The model,
disabled by default, must be enabled by setting EnableRFC6621 to true. A second attribute,
DuplicateExpire, sets the expiration delay for erasing the cache entry of a packet in the duplicate
cache; the delay value defaults to 1ms.
IPv6
This chapter describes the ns-3 IPv6 model capabilities and limitations along with its usage and
examples.
IPv6 model description
The IPv6 model is loosely patterned after the Linux implementation; the implementation is not complete as
some features of IPv6 are not of much interest to simulation studies, and some features of IPv6 are
simply not modeled yet in ns-3.
The base class Ipv6 defines a generic API, while the class Ipv6L3Protocol is the actual class
implementing the protocol. The actual classes used by the IPv6 stack are located mainly in the directory
src/internet.
The implementation of IPv6 is contained in the following files:
src/internet/model/icmpv6-header.{cc,h}
src/internet/model/icmpv6-l4-protocol.{cc,h}
src/internet/model/ipv6.{cc,h}
src/internet/model/ipv6-address-generator.{cc,h}
src/internet/model/ipv6-autoconfigured-prefix.{cc,h}
src/internet/model/ipv6-end-point.{cc,h}
src/internet/model/ipv6-end-point-demux.{cc,h}
src/internet/model/ipv6-extension.{cc,h}
src/internet/model/ipv6-extension-demux.{cc,h}
src/internet/model/ipv6-extension-header.{cc,h}
src/internet/model/ipv6-header.{cc,h}
src/internet/model/ipv6-interface.{cc,h}
src/internet/model/ipv6-interface-address.{cc,h}
src/internet/model/ipv6-l3-protocol.{cc,h}
src/internet/model/ipv6-list-routing.{cc,h}
src/internet/model/ipv6-option.{cc,h}
src/internet/model/ipv6-option-demux.{cc,h}
src/internet/model/ipv6-option-header.{cc,h}
src/internet/model/ipv6-packet-info-tag.{cc,h}
src/internet/model/ipv6-pmtu-cache.{cc,h}
src/internet/model/ipv6-raw-socket-factory.{cc,h}
src/internet/model/ipv6-raw-socket-factory-impl.{cc,h}
src/internet/model/ipv6-raw-socket-impl.{cc,h}
src/internet/model/ipv6-route.{cc,h}
src/internet/model/ipv6-routing-protocol.{cc,h}
src/internet/model/ipv6-routing-table-entry.{cc,h}
src/internet/model/ipv6-static-routing.{cc,h}
src/internet/model/ndisc-cache.{cc,h}
src/network/utils/inet6-socket-address.{cc,h}
src/network/utils/ipv6-address.{cc,h}
Also some helpers are involved with IPv6:
src/internet/helper/internet-stack-helper.{cc,h}
src/internet/helper/ipv6-address-helper.{cc,h}
src/internet/helper/ipv6-interface-container.{cc,h}
src/internet/helper/ipv6-list-routing-helper.{cc,h}
src/internet/helper/ipv6-routing-helper.{cc,h}
src/internet/helper/ipv6-static-routing-helper.{cc,h}
The model files can be roughly divided into:
• protocol models (e.g., ipv6, ipv6-l3-protocol, icmpv6-l4-protocol, etc.)
• routing models (i.e., anything with ‘routing’ in its name)
• sockets and interfaces (e.g., ipv6-raw-socket, ipv6-interface, ipv6-end-point, etc.)
• address-related things
• headers, option headers, extension headers, etc.
• accessory classes (e.g., ndisc-cache)
Usage
The following description is based on using the typical helpers found in the example code.
IPv6 does not need to be activated in a node. it is automatically added to the available protocols once
the Internet Stack is installed.
In order to not install IPv6 along with IPv4, it is possible to use ns3::InternetStackHelper method
SetIpv6StackInstall (bool enable) before installing the InternetStack in the nodes.
Note that to have an IPv6-only network (i.e., to not install the IPv4 stack in a node) one should use
ns3::InternetStackHelper method SetIpv4StackInstall (bool enable) before the stack installation.
As an example, in the following code node 0 will have both IPv4 and IPv6, node 1 only only IPv6 and node
2 only IPv4:
NodeContainer n;
n.Create (3);
InternetStackHelper internet;
InternetStackHelper internetV4only;
InternetStackHelper internetV6only;
internetV4only.SetIpv6StackInstall (false);
internetV6only.SetIpv4StackInstall (false);
internet.Install (n.Get (0));
internetV6only.Install (n.Get (1));
internetV4only.Install (n.Get (2));
IPv6 addresses assignment
In order to use IPv6 on a network, the first thing to do is assigning IPv6 addresses.
Any IPv6-enabled ns-3 node will have at least one NetDevice: the ns3::LoopbackNetDevice. The loopback
device address is ::1. All the other NetDevices will have one or more IPv6 addresses:
• One link-local address: fe80::interface ID, where interface ID is derived from the NetDevice MAC
address.
• Zero or more global addresses, e.g., 2001:db8::1.
Typically the first address on an interface will be the link-local one, with the global address(es) being
the following ones.
IPv6 global addresses might be:
• manually assigned
• auto-generated
ns-3 can use both methods, and it’s quite important to understand the implications of both.
Manually assigned IPv6 addresses
This is probably the easiest and most used method. As an example:
Ptr<Node> n0 = CreateObject<Node> ();
Ptr<Node> n1 = CreateObject<Node> ();
NodeContainer net (n0, n1);
CsmaHelper csma;
NetDeviceContainer ndc = csma.Install (net);
NS_LOG_INFO ("Assign IPv6 Addresses.");
Ipv6AddressHelper ipv6;
ipv6.SetBase (Ipv6Address ("2001:db8::"), Ipv6Prefix (64));
Ipv6InterfaceContainer ic = ipv6.Assign (ndc);
This method will add two global IPv6 addresses to the nodes. Note that, as usual for IPv6, all the nodes
will also have a link-local address. Typically the first address on an interface will be the link-local
one, with the global address(es) being the following ones.
Note that the global addresses will be derived from the MAC address. As a consequence, expect to have
addresses similar to 2001:db8::200:ff:fe00:1.
It is possible to repeat the above to assign more than one global address to a node. However, due to the
Ipv6AddressHelper singleton nature, one should first assign all the addresses of a network, then change
the network base (SetBase), then do a new assignment.
Alternatively, it is possible to assign a specific address to a node:
Ptr<Node> n0 = CreateObject<Node> ();
NodeContainer net (n0);
CsmaHelper csma;
NetDeviceContainer ndc = csma.Install (net);
NS_LOG_INFO ("Specifically Assign an IPv6 Address.");
Ipv6AddressHelper ipv6;
Ptr<NetDevice> device = ndc.Get (0);
Ptr<Node> node = device->GetNode ();
Ptr<Ipv6> ipv6proto = node->GetObject<Ipv6> ();
int32_t ifIndex = 0;
ifIndex = ipv6proto->GetInterfaceForDevice (device);
Ipv6InterfaceAddress ipv6Addr = Ipv6InterfaceAddress (Ipv6Address ("2001:db8:f00d:cafe::42"), Ipv6Prefix (64));
ipv6proto->AddAddress (ifIndex, ipv6Addr);
Auto-generated IPv6 addresses
This is accomplished by relying on the RADVD protocol, implemented by the class Radvd. A helper class is
available, which can be used to ease the most common tasks, e.g., setting up a prefix on an interface, if
it is announced periodically, and if the router is the default router for that interface.
A fine grain configuration is possible though the RadvdInterface class, which allows to setup every
parameter of the announced router advertisement on a given interface.
It is worth mentioning that the configurations must be set up before installing the application in the
node.
Upon using this method, the nodes will acquire dynamically (i.e., during the simulation) one (or more)
global address(es) according to the RADVD configuration. These addresses will be bases on the RADVD
announced prefix and the node’s EUI-64.
Examples of RADVD use are shown in examples/ipv6/radvd.cc and examples/ipv6/radvd-two-prefix.cc.
Note that the router (i.e., the node with Radvd) will have to have a global address, while the nodes
using the auto-generated addresses (SLAAC) will have to have a link-local address. This is accomplished
using Ipv6AddressHelper::AssignWithoutAddress, e.g.:
Ipv6AddressHelper ipv6;
NetDeviceContainer tmp;
tmp.Add (d1.Get (0)); /* n0 */
Ipv6InterfaceContainer iic1 = ipv6.AssignWithoutAddress (tmp); /* n0 interface */
Random-generated IPv6 addresses
While IPv6 real nodes will use randomly generated addresses to protect privacy, ns-3 does NOT have this
capability. This feature haven’t been so far considered as interesting for simulation.
Networks with and without the onlink property
IPv6 adds the concept of “on-link” for addresses and prefixes. Simplifying the concept, a network with
the on-link property behaves roughly as IPv4: a node will assume that any address belonging to the
on-link prefix is reachable on the link, so it uses Neighbor Discovery Protocol (NDP) to find the MAC
address corresponding to the IPv6 address. If the prefix is not marked as “on-link”, then any packet is
sent to the default router.
Radvd can announce prefixes that have the on-link flag not set. Moreover, it is possible to add an
address to a node without setting the on-link property for the prefix used in the address. The function
to use is Ipv6AddressHelper::AssignWithoutOnLink.
Duplicate Address Detection (DAD)
Nodes will perform DAD (it can be disabled using an Icmpv6L4Protocol attribute). Upon receiving a DAD,
however, nodes will not react to it. As is: DAD reaction is incomplete so far. The main reason relies on
the missing random-generated address capability. Moreover, since ns-3 nodes will usually be
well-behaving, there shouldn’t be any Duplicate Address. This might be changed in the future, so as to
avoid issues with real-world integrated simulations.
Explicit Congestion Notification (ECN) bits in IPv6
• In IPv6, ECN bits are the last 2 bits of the Traffic class and occupy 10th and 11th bit in the header.
• The IPv6 header class defines an EcnType enum with all four ECN codepoints (ECN_NotECT, ECN_ECT1,
ECN_ECT0, ECN_CE) mentioned in RFC 3168, and also a setter and getter method to handle ECN values in
the Traffic Class field.
Ipv6QueueDiscItem
The traffic control sublayer in ns-3 handles objects of class QueueDiscItem which are used to hold an
ns3::Packet and an ns3::Header. This is done to facilitate the marking of packets for Explicit
Congestion Notification. The Mark () method is implemented in Ipv6QueueDiscItem. It returns true if
marking the packet is successful, i.e., it successfully sets the CE bit in the IPv6 header. The Mark ()
method will return false, however, if the IPv6 header indicates the ECN_NotECT codepoint.
Host and Router behaviour in IPv6 and ns-3
In IPv6 there is a clear distinction between routers and hosts. As one might expect, routers can forward
packets from an interface to another interface, while hosts drop packets not directed to them.
Unfortunately, forwarding is not the only thing affected by this distinction, and forwarding itself might
be fine-tuned, e.g., to forward packets incoming from an interface and drop packets from another
interface.
In ns-3 a node is configured to be an host by default. There are two main ways to change this behaviour:
• Using ns3::Ipv6InterfaceContainer SetForwarding(uint32_t i, bool router) where i is the interface index
in the container.
• Changing the ns3::Ipv6 attribute IpForward.
Either one can be used during the simulation.
A fine-grained setup can be accomplished by using ns3::Ipv6Interface SetForwarding (bool forward); which
allows to change the behaviour on a per-interface-basis.
Note that the node-wide configuration only serves as a convenient method to enable/disable the
ns3::Ipv6Interface specific setting. An Ipv6Interface added to a node with forwarding enabled will be set
to be forwarding as well. This is really important when a node has interfaces added during the
simulation.
According to the ns3::Ipv6Interface forwarding state, the following happens:
• Forwarding OFF
• The node will NOT reply to Router Solicitation
• The node will react to Router Advertisement
• The node will periodically send Router Solicitation
• Routing protocols MUST DROP packets not directed to the node
• Forwarding ON
• The node will reply to Router Solicitation
• The node will NOT react to Router Advertisement
• The node will NOT send Router Solicitation
• Routing protocols MUST forward packets
The behaviour is matching ip-sysctl.txt (‐
http://www.kernel.org/doc/Documentation/networking/ip-sysctl.txt) with the difference that it’s not
possible to override the behaviour using esoteric settings (e.g., forwarding but accept router
advertisements, accept_ra=2, or forwarding but send router solicitations forwarding=2).
Consider carefully the implications of packet forwarding. As an example, a node will NOT send ICMPv6
PACKET_TOO_BIG messages from an interface with forwarding off. This is completely normal, as the Routing
protocol will drop the packet before attempting to forward it.
Helpers
Typically the helpers used in IPv6 setup are:
• ns3::InternetStackHelper
• ns3::Ipv6AddressHelper
• ns3::Ipv6InterfaceContainer
The use is almost identical to the corresponding IPv4 case, e.g.:
NodeContainer n;
n.Create (4);
NS_LOG_INFO ("Create IPv6 Internet Stack");
InternetStackHelper internetv6;
internetv6.Install (n);
NS_LOG_INFO ("Create channels.");
CsmaHelper csma;
NetDeviceContainer d = csma.Install (n);
NS_LOG_INFO ("Create networks and assign IPv6 Addresses.");
Ipv6AddressHelper ipv6;
ipv6.SetBase (Ipv6Address ("2001:db8::"), Ipv6Prefix (64));
Ipv6InterfaceContainer iic = ipv6.Assign (d);
Additionally, a common task is to enable forwarding on one of the nodes and to setup a default route
toward it in the other nodes, e.g.:
iic.SetForwarding (0, true);
iic.SetDefaultRouteInAllNodes (0);
This will enable forwarding on the node 0 and will setup a default route in ns3::Ipv6StaticRouting on all
the other nodes. Note that this requires that Ipv6StaticRouting is present in the nodes.
The IPv6 routing helpers enable the user to perform specific tasks on the particular routing algorithm
and to print the routing tables.
Attributes
Many classes in the ns-3 IPv6 implementation contain attributes. The most useful ones are:
• ns3::Ipv6
• IpForward, boolean, default false. Globally enable or disable IP forwarding for all current and
future IPv6 devices.
• MtuDiscover, boolean, default true. If disabled, every interface will have its MTU set to 1280
bytes.
• ns3::Ipv6L3Protocol
• DefaultTtl, uint8_t, default 64. The TTL value set by default on all outgoing packets generated on
this node.
• SendIcmpv6Redirect, boolean, default true. Send the ICMPv6 Redirect when appropriate.
• ns3::Icmpv6L4Protocol
• DAD, boolean, default true. Always do DAD (Duplicate Address Detection) check.
• ns3::NdiscCache
• UnresolvedQueueSize, uint32_t, default 3. Size of the queue for packets pending an NA reply.
Output
The IPv6 stack provides some useful trace sources:
• ns3::Ipv6L3Protocol
• Tx, Send IPv6 packet to outgoing interface.
• Rx, Receive IPv6 packet from incoming interface.
• Drop, Drop IPv6 packet.
• ns3::Ipv6Extension
• Drop, Drop IPv6 packet.
The latest trace source is generated when a packet contains an unknown option blocking its processing.
Mind that ns3::NdiscCache could drop packets as well, and they are not logged in a trace source (yet).
This might generate some confusion in the sent/received packets counters.
Advanced Usage
IPv6 maximum transmission unit (MTU) and fragmentation
ns-3 NetDevices define the MTU according to the L2 simulated Device. IPv6 requires that the minimum MTU
is 1280 bytes, so all NetDevices are required to support at least this MTU. This is the link-MTU.
In order to support different MTUs in a source-destination path, ns-3 IPv6 model can perform
fragmentation. This can be either triggered by receiving a packet bigger than the link-MTU from the L4
protocols (UDP, TCP, etc.), or by receiving an ICMPv6 PACKET_TOO_BIG message. The model mimics RFC 1981,
with the following notable exceptions:
• L4 protocols are not informed of the Path MTU change
• TCP can not change its Segment Size according to the Path-MTU.
Both limitations are going to be removed in due time.
The Path-MTU cache is currently based on the source-destination IPv6 addresses. Further classifications
(e.g., flow label) are possible but not yet implemented.
The Path-MTU default validity time is 10 minutes. After the cache entry expiration, the Path-MTU
information is removed and the next packet will (eventually) trigger a new ICMPv6 PACKET_TOO_BIG message.
Note that 1) this is consistent with the RFC specification and 2) L4 protocols are responsible for
retransmitting the packets.
Examples
The examples for IPv6 are in the directory examples/ipv6. These examples focus on the most interesting
IPv6 peculiarities, such as fragmentation, redirect and so on.
Moreover, most TCP and UDP examples located in examples/udp, examples/tcp, etc. have a command-line
option to use IPv6 instead of IPv4.
Troubleshooting
There are just a few pitfalls to avoid while using ns-3 IPv6.
Routing loops
Since the only (so far) routing scheme available for IPv6 is ns3::Ipv6StaticRouting, default router have
to be setup manually. When there are two or more routers in a network (e.g., node A and node B), avoid
using the helper function SetDefaultRouteInAllNodes for more than one router.
The consequence would be to install a default route to B in A and a default route pointing to A in B,
generating a loop.
Global address leakage
Remember that addresses in IPv6 are global by definition. When using IPv6 with an emulation ns-3
capability, avoid at all costs address leakage toward the global Internet. It is advisable to setup an
external firewall to prevent leakage.
2001:DB8::/32 addresses
IPv6 standard (RFC 3849) defines the 2001:DB8::/32 address class for the documentation. This manual uses
this convention. The addresses in this class are, however, only usable in a document, and routers should
discard them.
Validation
The IPv6 protocols has not yet been extensively validated against real implementations. The actual tests
involve mainly performing checks of the .pcap trace files with Wireshark, and the results are positive.
Routing overview
ns-3 is intended to support traditional routing approaches and protocols, support ports of open source
routing implementations, and facilitate research into unorthodox routing techniques. The overall routing
architecture is described below in Routing architecture. Users who wish to just read about how to
configure global routing for wired topologies can read Global centralized routing. Unicast routing
protocols are described in Unicast routing. Multicast routing is documented in Multicast routing.
Routing architecture
[image] Overview of routing.UNINDENT
Overview of routing shows the overall routing architecture for Ipv4. The key objects are
Ipv4L3Protocol, Ipv4RoutingProtocol(s) (a class to which all routing/forwarding has been delegated from
Ipv4L3Protocol), and Ipv4Route(s).
Ipv4L3Protocol must have at least one Ipv4RoutingProtocol added to it at simulation setup time. This is
done explicitly by calling Ipv4::SetRoutingProtocol ().
The abstract base class Ipv4RoutingProtocol () declares a minimal interface, consisting of two methods:
RouteOutput () and RouteInput (). For packets traveling outbound from a host, the transport protocol
will query Ipv4 for the Ipv4RoutingProtocol object interface, and will request a route via
Ipv4RoutingProtocol::RouteOutput (). A Ptr to Ipv4Route object is returned. This is analogous to a
dst_cache entry in Linux. The Ipv4Route is carried down to the Ipv4L3Protocol to avoid a second lookup
there. However, some cases (e.g. Ipv4 raw sockets) will require a call to RouteOutput() directly from
Ipv4L3Protocol.
For packets received inbound for forwarding or delivery, the following steps occur.
Ipv4L3Protocol::Receive() calls Ipv4RoutingProtocol::RouteInput(). This passes the packet ownership to
the Ipv4RoutingProtocol object. There are four callbacks associated with this call:
• LocalDeliver
• UnicastForward
• MulticastForward
• Error
The Ipv4RoutingProtocol must eventually call one of these callbacks for each packet that it takes
responsibility for. This is basically how the input routing process works in Linux.
[image] Ipv4Routing specialization..UNINDENT
This overall architecture is designed to support different routing approaches, including (in the
future) a Linux-like policy-based routing implementation, proactive and on-demand routing protocols,
and simple routing protocols for when the simulation user does not really care about routing.
Ipv4Routing specialization. illustrates how multiple routing protocols derive from this base class. A
class Ipv4ListRouting (implementation class Ipv4ListRoutingImpl) provides the existing list routing
approach in ns-3. Its API is the same as base class Ipv4Routing except for the ability to add multiple
prioritized routing protocols (Ipv4ListRouting::AddRoutingProtocol(),
Ipv4ListRouting::GetRoutingProtocol()).
The details of these routing protocols are described below in Unicast routing. For now, we will first
start with a basic unicast routing capability that is intended to globally build routing tables at
simulation time t=0 for simulation users who do not care about dynamic routing.
Unicast routing
The following unicast routing protocols are defined for IPv4 and IPv6:
• classes Ipv4ListRouting and Ipv6ListRouting (used to store a prioritized list of routing protocols)
• classes Ipv4StaticRouting and Ipv6StaticRouting (covering both unicast and multicast)
• class Ipv4GlobalRouting (used to store routes computed by the global route manager, if that is used)
• class Ipv4NixVectorRouting (a more efficient version of global routing that stores source routes in a
packet header field)
• class Rip - the IPv4 RIPv2 protocol (RFC 2453)
• class RipNg - the IPv6 RIPng protocol (RFC 2080)
• IPv4 Optimized Link State Routing (OLSR) (a MANET protocol defined in RFC 3626)
• IPv4 Ad Hoc On Demand Distance Vector (AODV) (a MANET protocol defined in RFC 3561)
• IPv4 Destination Sequenced Distance Vector (DSDV) (a MANET protocol)
• IPv4 Dynamic Source Routing (DSR) (a MANET protocol)
In the future, this architecture should also allow someone to implement a Linux-like implementation with
routing cache, or a Click modular router, but those are out of scope for now.
Ipv[4,6]ListRouting
This section describes the current default ns-3 Ipv[4,6]RoutingProtocol. Typically, multiple routing
protocols are supported in user space and coordinate to write a single forwarding table in the kernel.
Presently in ns-3, the implementation instead allows for multiple routing protocols to build/keep their
own routing state, and the IP implementation will query each one of these routing protocols (in some
order determined by the simulation author) until a route is found.
We chose this approach because it may better facilitate the integration of disparate routing approaches
that may be difficult to coordinate the writing to a single table, approaches where more information than
destination IP address (e.g., source routing) is used to determine the next hop, and on-demand routing
approaches where packets must be cached.
Ipv[4,6]ListRouting::AddRoutingProtocol
Classes Ipv4ListRouting and Ipv6ListRouting provides a pure virtual function declaration for the method
that allows one to add a routing protocol:
void AddRoutingProtocol (Ptr<Ipv4RoutingProtocol> routingProtocol,
int16_t priority);
void AddRoutingProtocol (Ptr<Ipv6RoutingProtocol> routingProtocol,
int16_t priority);
These methods are implemented respectively by class Ipv4ListRoutingImpl and by class Ipv6ListRoutingImpl
in the internet module.
The priority variable above governs the priority in which the routing protocols are inserted. Notice that
it is a signed int. By default in ns-3, the helper classes will instantiate a Ipv[4,6]ListRoutingImpl
object, and add to it an Ipv[4,6]StaticRoutingImpl object at priority zero. Internally, a list of
Ipv[4,6]RoutingProtocols is stored, and and the routing protocols are each consulted in decreasing order
of priority to see whether a match is found. Therefore, if you want your Ipv4RoutingProtocol to have
priority lower than the static routing, insert it with priority less than 0; e.g.:
Ptr<MyRoutingProtocol> myRoutingProto = CreateObject<MyRoutingProtocol> ();
listRoutingPtr->AddRoutingProtocol (myRoutingProto, -10);
Upon calls to RouteOutput() or RouteInput(), the list routing object will search the list of routing
protocols, in priority order, until a route is found. Such routing protocol will invoke the appropriate
callback and no further routing protocols will be searched.
Global centralized routing
Global centralized routing is sometimes called “God” routing; it is a special implementation that walks
the simulation topology and runs a shortest path algorithm, and populates each node’s routing tables. No
actual protocol overhead (on the simulated links) is incurred with this approach. It does have a few
constraints:
• Wired only: It is not intended for use in wireless networks.
• Unicast only: It does not do multicast.
• Scalability: Some users of this on large topologies (e.g. 1000 nodes) have noticed that the current
implementation is not very scalable. The global centralized routing will be modified in the future to
reduce computations and runtime performance.
Presently, global centralized IPv4 unicast routing over both point-to-point and shared (CSMA) links is
supported.
By default, when using the ns-3 helper API and the default InternetStackHelper, global routing capability
will be added to the node, and global routing will be inserted as a routing protocol with lower priority
than the static routes (i.e., users can insert routes via Ipv4StaticRouting API and they will take
precedence over routes found by global routing).
Global Unicast Routing API
The public API is very minimal. User scripts include the following:
#include "ns3/internet-module.h"
If the default InternetStackHelper is used, then an instance of global routing will be aggregated to each
node. After IP addresses are configured, the following function call will cause all of the nodes that
have an Ipv4 interface to receive forwarding tables entered automatically by the GlobalRouteManager:
Ipv4GlobalRoutingHelper::PopulateRoutingTables ();
Note: A reminder that the wifi NetDevice will work but does not take any wireless effects into account.
For wireless, we recommend OLSR dynamic routing described below.
It is possible to call this function again in the midst of a simulation using the following additional
public function:
Ipv4GlobalRoutingHelper::RecomputeRoutingTables ();
which flushes the old tables, queries the nodes for new interface information, and rebuilds the routes.
For instance, this scheduling call will cause the tables to be rebuilt at time 5 seconds:
Simulator::Schedule (Seconds (5),
&Ipv4GlobalRoutingHelper::RecomputeRoutingTables);
There are two attributes that govern the behavior. The first is Ipv4GlobalRouting::RandomEcmpRouting. If
set to true, packets are randomly routed across equal-cost multipath routes. If set to false (default),
only one route is consistently used. The second is Ipv4GlobalRouting::RespondToInterfaceEvents. If set to
true, dynamically recompute the global routes upon Interface notification events (up/down, or add/remove
address). If set to false (default), routing may break unless the user manually calls
RecomputeRoutingTables() after such events. The default is set to false to preserve legacy ns-3 program
behavior.
Global Routing Implementation
This section is for those readers who care about how this is implemented. A singleton object
(GlobalRouteManager) is responsible for populating the static routes on each node, using the public Ipv4
API of that node. It queries each node in the topology for a “globalRouter” interface. If found, it
uses the API of that interface to obtain a “link state advertisement (LSA)” for the router. Link State
Advertisements are used in OSPF routing, and we follow their formatting.
It is important to note that all of these computations are done before packets are flowing in the
network. In particular, there are no overhead or control packets being exchanged when using this
implementation. Instead, this global route manager just walks the list of nodes to build the necessary
information and configure each node’s routing table.
The GlobalRouteManager populates a link state database with LSAs gathered from the entire topology. Then,
for each router in the topology, the GlobalRouteManager executes the OSPF shortest path first (SPF)
computation on the database, and populates the routing tables on each node.
The quagga (http://www.quagga.net) OSPF implementation was used as the basis for the routing computation
logic. One benefit of following an existing OSPF SPF implementation is that OSPF already has defined link
state advertisements for all common types of network links:
• point-to-point (serial links)
• point-to-multipoint (Frame Relay, ad hoc wireless)
• non-broadcast multiple access (ATM)
• broadcast (Ethernet)
Therefore, we think that enabling these other link types will be more straightforward now that the
underlying OSPF SPF framework is in place.
Presently, we can handle IPv4 point-to-point, numbered links, as well as shared broadcast (CSMA) links.
Equal-cost multipath is also supported. Although wireless link types are supported by the
implementation, note that due to the nature of this implementation, any channel effects will not be
considered and the routing tables will assume that every node on the same shared channel is reachable
from every other node (i.e. it will be treated like a broadcast CSMA link).
The GlobalRouteManager first walks the list of nodes and aggregates a GlobalRouter interface to each one
as follows:
typedef std::vector < Ptr<Node> >::iterator Iterator;
for (Iterator i = NodeList::Begin (); i != NodeList::End (); i++)
{
Ptr<Node> node = *i;
Ptr<GlobalRouter> globalRouter = CreateObject<GlobalRouter> (node);
node->AggregateObject (globalRouter);
}
This interface is later queried and used to generate a Link State Advertisement for each router, and this
link state database is fed into the OSPF shortest path computation logic. The Ipv4 API is finally used to
populate the routes themselves.
RIP and RIPng
The RIPv2 protocol for IPv4 is described in the RFC 2453, and it consolidates a number of improvements
over the base protocol defined in RFC 1058.
This IPv6 routing protocol (RFC 2080) is the evolution of the well-known RIPv1 (see RFC 1058 and RFC
1723) routing protocol for IPv4.
The protocols are very simple, and are normally suitable for flat, simple network topologies.
RIPv1, RIPv2, and RIPng have the very same goals and limitations. In particular, RIP considers any route
with a metric equal or greater than 16 as unreachable. As a consequence, the maximum number of hops is
the network must be less than 15 (the number of routers is not set). Users are encouraged to read RFC
2080 and RFC 1058 to fully understand RIP behaviour and limitations.
Routing convergence
RIP uses a Distance-Vector algorithm, and routes are updated according to the Bellman-Ford algorithm
(sometimes known as Ford-Fulkerson algorithm). The algorithm has a convergence time of O(|V|*|E|) where
|V| and |E| are the number of vertices (routers) and edges (links) respectively. It should be stressed
that the convergence time is the number of steps in the algorithm, and each step is triggered by a
message. Since Triggered Updates (i.e., when a route is changed) have a 1-5 seconds cooldown, the
topology can require some time to be stabilized.
Users should be aware that, during routing tables construction, the routers might drop packets. Data
traffic should be sent only after a time long enough to allow RIP to build the network topology. Usually
80 seconds should be enough to have a suboptimal (but working) routing setup. This includes the time
needed to propagate the routes to the most distant router (16 hops) with Triggered Updates.
If the network topology is changed (e.g., a link is broken), the recovery time might be quite high, and
it might be even higher than the initial setup time. Moreover, the network topology recovery is affected
by the Split Horizoning strategy.
The examples examples/routing/ripng-simple-network.cc and examples/routing/rip-simple-network.cc shows
both the network setup and network recovery phases.
Split Horizoning
Split Horizon is a strategy to prevent routing instability. Three options are possible:
• No Split Horizon
• Split Horizon
• Poison Reverse
In the first case, routes are advertised on all the router’s interfaces. In the second case, routers
will not advertise a route on the interface from which it was learned. Poison Reverse will advertise the
route on the interface from which it was learned, but with a metric of 16 (infinity). For a full
analysis of the three techniques, see RFC 1058, section 2.2.
The examples are based on the network topology described in the RFC, but it does not show the effect
described there.
The reason are the Triggered Updates, together with the fact that when a router invalidates a route, it
will immediately propagate the route unreachability, thus preventing most of the issues described in the
RFC.
However, with complex topologies, it is still possible to have route instability phenomena similar to the
one described in the RFC after a link failure. As a consequence, all the considerations about Split
Horizon remains valid.
Default routes
RIP protocol should be installed only on routers. As a consequence, nodes will not know what is the
default router.
To overcome this limitation, users should either install the default route manually (e.g., by resorting
to Ipv4StaticRouting or Ipv6StaticRouting), or by using RADVd (in case of IPv6). RADVd is available in
ns-3 in the Applications module, and it is strongly suggested.
Protocol parameters and options
The RIP ns-3 implementations allow to change all the timers associated with route updates and routes
lifetime.
Moreover, users can change the interface metrics on a per-node basis.
The type of Split Horizoning (to avoid routes back-propagation) can be selected on a per-node basis, with
the choices being “no split horizon”, “split horizon” and “poison reverse”. See RFC 2080 for further
details, and RFC 1058 for a complete discussion on the split horizoning strategies.
Moreover, it is possible to use a non-standard value for Link Down Value (i.e., the value after which a
link is considered down). The default is value is 16.
Limitations
There is no support for the Next Hop option (RFC 2080, Section 2.1.1). The Next Hop option is useful
when RIP is not being run on all of the routers on a network. Support for this option may be considered
in the future.
There is no support for CIDR prefix aggregation. As a result, both routing tables and route
advertisements may be larger than necessary. Prefix aggregation may be added in the future.
Other routing protocols
Other routing protocols documentation can be found under the respective modules sections, e.g.:
• AODV
• Click
• DSDV
• DSR
• NixVectorRouting
• OLSR
• etc.
Multicast routing
The following function is used to add a static multicast route to a node:
void
Ipv4StaticRouting::AddMulticastRoute (Ipv4Address origin,
Ipv4Address group,
uint32_t inputInterface,
std::vector<uint32_t> outputInterfaces);
A multicast route must specify an origin IP address, a multicast group and an input network interface
index as conditions and provide a vector of output network interface indices over which packets matching
the conditions are sent.
Typically there are two main types of multicast routes:
• Routes used during forwarding, and
• Routes used in the originator node.
In the first case all the conditions must be explicitly provided.
In the second case, the route is equivalent to a unicast route, and must be added through
Ipv4StaticRouting::AddHostRouteTo.
Another command sets the default multicast route:
void
Ipv4StaticRouting::SetDefaultMulticastRoute (uint32_t outputInterface);
This is the multicast equivalent of the unicast version SetDefaultRoute. We tell the routing system what
to do in the case where a specific route to a destination multicast group is not found. The system
forwards packets out the specified interface in the hope that “something out there” knows better how to
route the packet. This method is only used in initially sending packets off of a host. The default
multicast route is not consulted during forwarding – exact routes must be specified using
AddMulticastRoute for that case.
Since we’re basically sending packets to some entity we think may know better what to do, we don’t pay
attention to “subtleties” like origin address, nor do we worry about forwarding out multiple interfaces.
If the default multicast route is set, it is returned as the selected route from LookupStatic
irrespective of origin or multicast group if another specific route is not found.
Finally, a number of additional functions are provided to fetch and remove multicast routes:
uint32_t GetNMulticastRoutes (void) const;
Ipv4MulticastRoute *GetMulticastRoute (uint32_t i) const;
Ipv4MulticastRoute *GetDefaultMulticastRoute (void) const;
bool RemoveMulticastRoute (Ipv4Address origin,
Ipv4Address group,
uint32_t inputInterface);
void RemoveMulticastRoute (uint32_t index);
TCP models in ns-3
This chapter describes the TCP models available in ns-3.
Overview of support for TCP
ns-3 was written to support multiple TCP implementations. The implementations inherit from a few common
header classes in the src/network directory, so that user code can swap out implementations with minimal
changes to the scripts.
There are three important abstract base classes:
• class TcpSocket: This is defined in src/internet/model/tcp-socket.{cc,h}. This class exists for hosting
TcpSocket attributes that can be reused across different implementations. For instance, the attribute
InitialCwnd can be used for any of the implementations that derive from class TcpSocket.
• class TcpSocketFactory: This is used by the layer-4 protocol instance to create TCP sockets of the
right type.
• class TcpCongestionOps: This supports different variants of congestion control– a key topic of
simulation-based TCP research.
There are presently two active and one legacy implementations of TCP available for ns-3.
• a natively implemented TCP for ns-3
• support for kernel implementations via Direct Code Execution (DCE)
• (legacy) support for kernel implementations for the Network Simulation Cradle (NSC)
NSC is no longer actively supported; it requires use of gcc-5 or gcc-4.9, and only covers up to Linux
kernel version 2.6.29.
Direct Code Execution is also limited in its support for newer kernels; at present, only Linux kernel 4.4
is supported. However, the TCP implementations in kernel 4.4 can still be used for ns-3 validation or
for specialized simulation use cases.
It should also be mentioned that various ways of combining virtual machines with ns-3 makes available
also some additional TCP implementations, but those are out of scope for this chapter.
ns-3 TCP
In brief, the native ns-3 TCP model supports a full bidirectional TCP with connection setup and close
logic. Several congestion control algorithms are supported, with CUBIC the default, and NewReno,
Westwood, Hybla, HighSpeed, Vegas, Scalable, Veno, Binary Increase Congestion Control (BIC), Yet Another
HighSpeed TCP (YeAH), Illinois, H-TCP, Low Extra Delay Background Transport (LEDBAT), TCP Low Priority
(TCP-LP), Data Center TCP (DCTCP) and Bottleneck Bandwidth and RTT (BBR) also supported. The model also
supports Selective Acknowledgements (SACK), Proportional Rate Reduction (PRR) and Explicit Congestion
Notification (ECN). Multipath-TCP is not yet supported in the ns-3 releases.
Model history
Until the ns-3.10 release, ns-3 contained a port of the TCP model from GTNetS, developed initially by
George Riley and ported to ns-3 by Raj Bhattacharjea. This implementation was substantially rewritten by
Adriam Tam for ns-3.10. In 2015, the TCP module was redesigned in order to create a better environment
for creating and carrying out automated tests. One of the main changes involves congestion control
algorithms, and how they are implemented.
Before the ns-3.25 release, a congestion control was considered as a stand-alone TCP through an
inheritance relation: each congestion control (e.g. TcpNewReno) was a subclass of TcpSocketBase,
reimplementing some inherited methods. The architecture was redone to avoid this inheritance, by making
each congestion control a separate class, and defining an interface to exchange important data between
TcpSocketBase and the congestion modules. The Linux tcp_congestion_ops interface was used as the design
reference.
Along with congestion control, Fast Retransmit and Fast Recovery algorithms have been modified; in
previous releases, these algorithms were delegated to TcpSocketBase subclasses. Starting from ns-3.25,
they have been merged inside TcpSocketBase. In future releases, they can be extracted as separate
modules, following the congestion control design.
As of the ns-3.31 release, the default initial window was set to 10 segments (in previous releases, it
was set to 1 segment). This aligns with current Linux default, and is discussed further in RFC 6928.
In the ns-3.32 release, the default recovery algorithm was set to Proportional Rate Reduction (PRR) from
the classic ack-clocked Fast Recovery algorithm.
In the ns-3.34 release, the default congestion control algorithm was set to CUBIC from NewReno.
Acknowledgments
As mentioned above, ns-3 TCP has had multiple authors and maintainers over the years. Several
publications exist on aspects of ns-3 TCP, and users of ns-3 TCP are requested to cite one of the
applicable papers when publishing new work.
A general reference on the current architecture is found in the following paper:
• Maurizio Casoni, Natale Patriciello, Next-generation TCP for ns-3 simulator, Simulation Modelling
Practice and Theory, Volume 66, 2016, Pages 81-93. (‐
http://www.sciencedirect.com/science/article/pii/S1569190X15300939)
For an academic peer-reviewed paper on the SACK implementation in ns-3, please refer to:
• Natale Patriciello. 2017. A SACK-based Conservative Loss Recovery Algorithm for ns-3 TCP: a
Linux-inspired Proposal. In Proceedings of the Workshop on ns-3 (WNS3 ‘17). ACM, New York, NY, USA,
1-8. (https://dl.acm.org/citation.cfm?id=3067666)
Usage
In many cases, usage of TCP is set at the application layer by telling the ns-3 application which kind of
socket factory to use.
Using the helper functions defined in src/applications/helper and src/network/helper, here is how one
would create a TCP receiver:
// Create a packet sink on the star "hub" to receive these packets
uint16_t port = 50000;
Address sinkLocalAddress(InetSocketAddress (Ipv4Address::GetAny (), port));
PacketSinkHelper sinkHelper ("ns3::TcpSocketFactory", sinkLocalAddress);
ApplicationContainer sinkApp = sinkHelper.Install (serverNode);
sinkApp.Start (Seconds (1.0));
sinkApp.Stop (Seconds (10.0));
Similarly, the below snippet configures OnOffApplication traffic source to use TCP:
// Create the OnOff applications to send TCP to the server
OnOffHelper clientHelper ("ns3::TcpSocketFactory", Address ());
The careful reader will note above that we have specified the TypeId of an abstract base class
TcpSocketFactory. How does the script tell ns-3 that it wants the native ns-3 TCP vs. some other one?
Well, when internet stacks are added to the node, the default TCP implementation that is aggregated to
the node is the ns-3 TCP. This can be overridden as we show below when using Network Simulation Cradle.
So, by default, when using the ns-3 helper API, the TCP that is aggregated to nodes with an Internet
stack is the native ns-3 TCP.
To configure behavior of TCP, a number of parameters are exported through the ns-3 attribute system.
These are documented in the Doxygen for class TcpSocket. For example, the maximum segment size is a
settable attribute.
To set the default socket type before any internet stack-related objects are created, one may put the
following statement at the top of the simulation program:
Config::SetDefault ("ns3::TcpL4Protocol::SocketType", StringValue ("ns3::TcpNewReno"));
For users who wish to have a pointer to the actual socket (so that socket operations like Bind(), setting
socket options, etc. can be done on a per-socket basis), Tcp sockets can be created by using the
Socket::CreateSocket() method. The TypeId passed to CreateSocket() must be of type ns3::SocketFactory, so
configuring the underlying socket type must be done by twiddling the attribute associated with the
underlying TcpL4Protocol object. The easiest way to get at this would be through the attribute
configuration system. In the below example, the Node container “n0n1” is accessed to get the zeroth
element, and a socket is created on this node:
// Create and bind the socket...
TypeId tid = TypeId::LookupByName ("ns3::TcpNewReno");
Config::Set ("/NodeList/*/$ns3::TcpL4Protocol/SocketType", TypeIdValue (tid));
Ptr<Socket> localSocket =
Socket::CreateSocket (n0n1.Get (0), TcpSocketFactory::GetTypeId ());
Above, the “*” wild card for node number is passed to the attribute configuration system, so that all
future sockets on all nodes are set to NewReno, not just on node ‘n0n1.Get (0)’. If one wants to limit it
to just the specified node, one would have to do something like:
// Create and bind the socket...
TypeId tid = TypeId::LookupByName ("ns3::TcpNewReno");
std::stringstream nodeId;
nodeId << n0n1.Get (0)->GetId ();
std::string specificNode = "/NodeList/" + nodeId.str () + "/$ns3::TcpL4Protocol/SocketType";
Config::Set (specificNode, TypeIdValue (tid));
Ptr<Socket> localSocket =
Socket::CreateSocket (n0n1.Get (0), TcpSocketFactory::GetTypeId ());
Once a TCP socket is created, one will want to follow conventional socket logic and either connect() and
send() (for a TCP client) or bind(), listen(), and accept() (for a TCP server). Please note that
applications usually create the sockets they use automatically, and so is not straightforward to connect
directly to them using pointers. Please refer to the source code of your preferred application to
discover how and when it creates the socket.
TCP Socket interaction and interface with Application layer
In the following there is an analysis on the public interface of the TCP socket, and how it can be used
to interact with the socket itself. An analysis of the callback fired by the socket is also carried out.
Please note that, for the sake of clarity, we will use the terminology “Sender” and “Receiver” to clearly
divide the functionality of the socket. However, in TCP these two roles can be applied at the same time
(i.e. a socket could be a sender and a receiver at the same time): our distinction does not lose
generality, since the following definition can be applied to both sockets in case of full-duplex mode.
----
TCP state machine (for commodity use)
[image] TCP State machine.UNINDENT
In ns-3 we are fully compliant with the state machine depicted in Figure TCP State machine.
----
Public interface for receivers (e.g. servers receiving data)
Bind() Bind the socket to an address, or to a general endpoint. A general endpoint is an endpoint with an
ephemeral port allocation (that is, a random port allocation) on the 0.0.0.0 IP address. For
instance, in current applications, data senders usually binds automatically after a Connect() over
a random port. Consequently, the connection will start from this random port towards the
well-defined port of the receiver. The IP 0.0.0.0 is then translated by lower layers into the real
IP of the device.
Bind6()
Same as Bind(), but for IPv6.
BindToNetDevice()
Bind the socket to the specified NetDevice, creating a general endpoint.
Listen()
Listen on the endpoint for an incoming connection. Please note that this function can be called
only in the TCP CLOSED state, and transit in the LISTEN state. When an incoming request for
connection is detected (i.e. the other peer invoked Connect()) the application will be signaled
with the callback NotifyConnectionRequest (set in SetAcceptCallback() beforehand). If the
connection is accepted (the default behavior, when the associated callback is a null one) the
Socket will fork itself, i.e. a new socket is created to handle the incoming data/connection, in
the state SYN_RCVD. Please note that this newly created socket is not connected anymore to the
callbacks on the “father” socket (e.g. DataSent, Recv); the pointer of the newly created socket is
provided in the Callback NotifyNewConnectionCreated (set beforehand in SetAcceptCallback), and
should be used to connect new callbacks to interesting events (e.g. Recv callback). After
receiving the ACK of the SYN-ACK, the socket will set the congestion control, move into
ESTABLISHED state, and then notify the application with NotifyNewConnectionCreated.
ShutdownSend()
Signal a termination of send, or in other words prevents data from being added to the buffer.
After this call, if buffer is already empty, the socket will send a FIN, otherwise FIN will go
when buffer empties. Please note that this is useful only for modeling “Sink” applications. If you
have data to transmit, please refer to the Send() / Close() combination of API.
GetRxAvailable()
Get the amount of data that could be returned by the Socket in one or multiple call to Recv or
RecvFrom. Please use the Attribute system to configure the maximum available space on the receiver
buffer (property “RcvBufSize”).
Recv() Grab data from the TCP socket. Please remember that TCP is a stream socket, and it is allowed to
concatenate multiple packets into bigger ones. If no data is present (i.e. GetRxAvailable returns
0) an empty packet is returned. Set the callback RecvCallback through SetRecvCallback() in order
to have the application automatically notified when some data is ready to be read. It’s important
to connect that callback to the newly created socket in case of forks.
RecvFrom()
Same as Recv, but with the source address as parameter.
----
Public interface for senders (e.g. clients uploading data)
Connect()
Set the remote endpoint, and try to connect to it. The local endpoint should be set before this
call, or otherwise an ephemeral one will be created. The TCP then will be in the SYN_SENT state.
If a SYN-ACK is received, the TCP will setup the congestion control, and then call the callback
ConnectionSucceeded.
GetTxAvailable()
Return the amount of data that can be stored in the TCP Tx buffer. Set this property through the
Attribute system (“SndBufSize”).
Send() Send the data into the TCP Tx buffer. From there, the TCP rules will decide if, and when, this
data will be transmitted. Please note that, if the tx buffer has enough data to fill the
congestion (or the receiver) window, dynamically varying the rate at which data is injected in the
TCP buffer does not have any noticeable effect on the amount of data transmitted on the wire, that
will continue to be decided by the TCP rules.
SendTo()
Same as Send().
Close()
Terminate the local side of the connection, by sending a FIN (after all data in the tx buffer has
been transmitted). This does not prevent the socket in receiving data, and employing retransmit
mechanism if losses are detected. If the application calls Close() with unread data in its rx
buffer, the socket will send a reset. If the socket is in the state SYN_SENT, CLOSING, LISTEN,
FIN_WAIT_2, or LAST_ACK, after that call the application will be notified with
NotifyNormalClose(). In other cases, the notification is delayed (see NotifyNormalClose()).
----
Public callbacks
These callbacks are called by the TCP socket to notify the application of interesting events. We will
refer to these with the protected name used in socket.h, but we will provide the API function to set the
pointers to these callback as well.
NotifyConnectionSucceeded: SetConnectCallback, 1st argument
Called in the SYN_SENT state, before moving to ESTABLISHED. In other words, we have sent the SYN,
and we received the SYN-ACK: the socket prepares the sequence numbers, sends the ACK for the
SYN-ACK, tries to send out more data (in another segment) and then invokes this callback. After
this callback, it invokes the NotifySend callback.
NotifyConnectionFailed: SetConnectCallback, 2nd argument
Called after the SYN retransmission count goes to 0. SYN packet is lost multiple times, and the
socket gives up.
NotifyNormalClose: SetCloseCallbacks, 1st argument
A normal close is invoked. A rare case is when we receive an RST segment (or a segment with bad
flags) in normal states. All other cases are: - The application tries to Connect() over an already
connected socket - Received an ACK for the FIN sent, with or without the FIN bit set (we are in
LAST_ACK) - The socket reaches the maximum amount of retries in retransmitting the SYN (*) - We
receive a timeout in the LAST_ACK state - Upon entering the TIME_WAIT state, before waiting the
2*Maximum Segment Lifetime seconds to finally deallocate the socket.
NotifyErrorClose: SetCloseCallbacks, 2nd argument
Invoked when we send an RST segment (for whatever reason) or we reached the maximum amount of data
retries.
NotifyConnectionRequest: SetAcceptCallback, 1st argument
Invoked in the LISTEN state, when we receive a SYN. The return value indicates if the socket
should accept the connection (return true) or should ignore it (return false).
NotifyNewConnectionCreated: SetAcceptCallback, 2nd argument
Invoked when from SYN_RCVD the socket passes to ESTABLISHED, and after setting up the congestion
control, the sequence numbers, and processing the incoming ACK. If there is some space in the
buffer, NotifySend is called shortly after this callback. The Socket pointer, passed with this
callback, is the newly created socket, after a Fork().
NotifyDataSent: SetDataSentCallback
The Socket notifies the application that some bytes have been transmitted on the IP level. These
bytes could still be lost in the node (traffic control layer) or in the network.
NotifySend: SetSendCallback
Invoked if there is some space in the tx buffer when entering the ESTABLISHED state (e.g. after
the ACK for SYN-ACK is received), after the connection succeeds (e.g. after the SYN-ACK is
received) and after each new ACK (i.e. that advances SND.UNA).
NotifyDataRecv: SetRecvCallback
Called when in the receiver buffer there are in-order bytes, and when in FIN_WAIT_1 or FIN_WAIT_2
the socket receive a in-sequence FIN (that can carry data).
Congestion Control Algorithms
Here follows a list of supported TCP congestion control algorithms. For an academic paper on many of
these congestion control algorithms, see http://dl.acm.org/citation.cfm?id=2756518 .
NewReno
NewReno algorithm introduces partial ACKs inside the well-established Reno algorithm. This and other
modifications are described in RFC 6582. We have two possible congestion window increment strategy: slow
start and congestion avoidance. Taken from RFC 5681:
During slow start, a TCP increments cwnd by at most SMSS bytes for each ACK received that cumulatively
acknowledges new data. Slow start ends when cwnd exceeds ssthresh (or, optionally, when it reaches it,
as noted above) or when congestion is observed. While traditionally TCP implementations have increased
cwnd by precisely SMSS bytes upon receipt of an ACK covering new data, we RECOMMEND that TCP
implementations increase cwnd, per Equation (1), where N is the number of previously unacknowledged
bytes acknowledged in the incoming ACK.
cwnd += min (N, SMSS)
During congestion avoidance, cwnd is incremented by roughly 1 full-sized segment per round-trip time
(RTT), and for each congestion event, the slow start threshold is halved.
CUBIC
CUBIC (class TcpCubic) is the default TCP congestion control in Linux, macOS (since 2014), and Microsoft
Windows (since 2017). CUBIC has two main differences with respect to a more classic TCP congestion
control such as NewReno. First, during the congestion avoidance phase, the window size grows according
to a cubic function (concave, then convex) with the latter convex portion designed to allow for bandwidth
probing. Second, a hybrid slow start (HyStart) algorithm uses observations of delay increases in the
slow start phase of window growth to try to exit slow start before window growth causes queue overflow.
CUBIC is documented in RFC 8312, and the ns-3 implementation is based on the RFC more so than the Linux
implementation, although the Linux 4.4 kernel implementation (through the Direct Code Execution
environment) has been used to validate the behavior and is fairly well aligned (see below section on
validation).
Linux Reno
TCP Linux Reno (class TcpLinuxReno) is designed to provide a Linux-like implementation of TCP NewReno.
The implementation of class TcpNewReno in ns-3 follows RFC standards, and increases cwnd more
conservatively than does Linux Reno. Linux Reno modifies slow start and congestion avoidance algorithms
to increase cwnd based on the number of bytes being acknowledged by each arriving ACK, rather than by the
number of ACKs that arrive. Another major difference in implementation is that Linux maintains the
congestion window in units of segments, while the RFCs define the congestion window in units of bytes.
In slow start phase, on each incoming ACK at the TCP sender side cwnd is increased by the number of
previously unacknowledged bytes ACKed by the incoming acknowledgment. In contrast, in ns-3 NewReno, cwnd
is increased by one segment per acknowledgment. In standards terminology, this difference is referred to
as Appropriate Byte Counting (RFC 3465); Linux follows Appropriate Byte Counting while ns-3 NewReno does
not.
cwnd += segAcked * segmentSize
cwnd += segmentSize
In congestion avoidance phase, the number of bytes that have been ACKed at the TCP sender side are stored
in a ‘bytes_acked’ variable in the TCP control block. When ‘bytes_acked’ becomes greater than or equal to
the value of the cwnd, ‘bytes_acked’ is reduced by the value of cwnd. Next, cwnd is incremented by a
full-sized segment (SMSS). In contrast, in ns-3 NewReno, cwnd is increased by (1/cwnd) with a rounding
off due to type casting into int.
if (m_cWndCnt >= w)
{
uint32_t delta = m_cWndCnt / w;
m_cWndCnt -= delta * w;
tcb->m_cWnd += delta * tcb->m_segmentSize;
NS_LOG_DEBUG ("Subtracting delta * w from m_cWndCnt " << delta * w);
}
:label: linuxrenocongavoid
if (segmentsAcked > 0)
{
double adder = static_cast<double> (tcb->m_segmentSize * tcb->m_segmentSize) / tcb->m_cWnd.Get ();
adder = std::max (1.0, adder);
tcb->m_cWnd += static_cast<uint32_t> (adder);
NS_LOG_INFO ("In CongAvoid, updated to cwnd " << tcb->m_cWnd <<
" ssthresh " << tcb->m_ssThresh);
}
:label: newrenocongavoid
So, there are two main difference between the TCP Linux Reno and TCP NewReno in ns-3: 1) In TCP Linux
Reno, delayed acknowledgement configuration does not affect congestion window growth, while in TCP
NewReno, delayed acknowledgments cause a slower congestion window growth. 2) In congestion avoidance
phase, the arithmetic for counting the number of segments acked and deciding when to increment the cwnd
is different for TCP Linux Reno and TCP NewReno.
Following graphs shows the behavior of window growth in TCP Linux Reno and TCP NewReno with delayed
acknowledgement of 2 segments:
[image] ns-3 TCP NewReno vs. ns-3 TCP Linux Reno.UNINDENT
HighSpeed
TCP HighSpeed is designed for high-capacity channels or, in general, for TCP connections with large
congestion windows. Conceptually, with respect to the standard TCP, HighSpeed makes the cWnd grow faster
during the probing phases and accelerates the cWnd recovery from losses. This behavior is executed only
when the window grows beyond a certain threshold, which allows TCP HighSpeed to be friendly with standard
TCP in environments with heavy congestion, without introducing new dangers of congestion collapse.
Mathematically:
cWnd = cWnd + \frac{a(cWnd)}{cWnd}
The function a() is calculated using a fixed RTT the value 100 ms (the lookup table for this function is
taken from RFC 3649). For each congestion event, the slow start threshold is decreased by a value that
depends on the size of the slow start threshold itself. Then, the congestion window is set to such value.
cWnd = (1-b(cWnd)) \cdot cWnd
The lookup table for the function b() is taken from the same RFC. More information at:
http://dl.acm.org/citation.cfm?id=2756518
Hybla
The key idea behind TCP Hybla is to obtain for long RTT connections the same instantaneous transmission
rate of a reference TCP connection with lower RTT. With analytical steps, it is shown that this goal can
be achieved by modifying the time scale, in order for the throughput to be independent from the RTT. This
independence is obtained through the use of a coefficient rho.
This coefficient is used to calculate both the slow start threshold and the congestion window when in
slow start and in congestion avoidance, respectively.
More information at: http://dl.acm.org/citation.cfm?id=2756518
Westwood
Westwood and Westwood+ employ the AIAD (Additive Increase/Adaptive Decrease)· congestion control
paradigm. When a congestion episode happens,· instead of halving the cwnd, these protocols try to
estimate the network’s bandwidth and use the estimated value to adjust the cwnd.· While Westwood performs
the bandwidth sampling every ACK reception,· Westwood+ samples the bandwidth every RTT.
More information at: http://dl.acm.org/citation.cfm?id=381704 and
http://dl.acm.org/citation.cfm?id=2512757
Vegas
TCP Vegas is a pure delay-based congestion control algorithm implementing a proactive scheme that tries
to prevent packet drops by maintaining a small backlog at the bottleneck queue. Vegas continuously
samples the RTT and computes the actual throughput a connection achieves using Equation (6) and compares
it with the expected throughput calculated in Equation (7). The difference between these 2 sending rates
in Equation (8) reflects the amount of extra packets being queued at the bottleneck.
actual &= \frac{cWnd}{RTT}
expected &= \frac{cWnd}{BaseRTT}
diff &= expected - actual
To avoid congestion, Vegas linearly increases/decreases its congestion window to ensure the diff value
falls between the two predefined thresholds, alpha and beta. diff and another threshold, gamma, are used
to determine when Vegas should change from its slow-start mode to linear increase/decrease mode.
Following the implementation of Vegas in Linux, we use 2, 4, and 1 as the default values of alpha, beta,
and gamma, respectively, but they can be modified through the Attribute system.
More information at: http://dx.doi.org/10.1109/49.464716
Scalable
Scalable improves TCP performance to better utilize the available bandwidth of a highspeed wide area
network by altering NewReno congestion window adjustment algorithm. When congestion has not been
detected, for each ACK received in an RTT, Scalable increases its cwnd per:
cwnd = cwnd + 0.01
Following Linux implementation of Scalable, we use 50 instead of 100 to account for delayed ACK.
On the first detection of congestion in a given RTT, cwnd is reduced based on the following equation:
cwnd = cwnd - ceil(0.125 \cdot cwnd)
More information at: http://dl.acm.org/citation.cfm?id=956989
Veno
TCP Veno enhances Reno algorithm for more effectively dealing with random packet loss in wireless access
networks by employing Vegas’s method in estimating the backlog at the bottleneck queue to distinguish
between congestive and non-congestive states.
The backlog (the number of packets accumulated at the bottleneck queue) is calculated using Equation
(11):
N &= Actual \cdot (RTT - BaseRTT) \\
&= Diff \cdot BaseRTT
where:
Diff &= Expected - Actual \\
&= \frac{cWnd}{BaseRTT} - \frac{cWnd}{RTT}
Veno makes decision on cwnd modification based on the calculated N and its predefined threshold beta.
Specifically, it refines the additive increase algorithm of Reno so that the connection can stay longer
in the stable state by incrementing cwnd by 1/cwnd for every other new ACK received after the available
bandwidth has been fully utilized, i.e. when N exceeds beta. Otherwise, Veno increases its cwnd by 1/cwnd
upon every new ACK receipt as in Reno.
In the multiplicative decrease algorithm, when Veno is in the non-congestive state, i.e. when N is less
than beta, Veno decrements its cwnd by only 1/5 because the loss encountered is more likely a
corruption-based loss than a congestion-based. Only when N is greater than beta, Veno halves its sending
rate as in Reno.
More information at: http://dx.doi.org/10.1109/JSAC.2002.807336
BIC
BIC (class TcpBic) is a predecessor of TCP CUBIC. In TCP BIC the congestion control problem is viewed as
a search problem. Taking as a starting point the current window value and as a target point the last
maximum window value (i.e. the cWnd value just before the loss event) a binary search technique can be
used to update the cWnd value at the midpoint between the two, directly or using an additive increase
strategy if the distance from the current window is too large.
This way, assuming a no-loss period, the congestion window logarithmically approaches the maximum value
of cWnd until the difference between it and cWnd falls below a preset threshold. After reaching such a
value (or the maximum window is unknown, i.e. the binary search does not start at all) the algorithm
switches to probing the new maximum window with a ‘slow start’ strategy.
If a loss occur in either these phases, the current window (before the loss) can be treated as the new
maximum, and the reduced (with a multiplicative decrease factor Beta) window size can be used as the new
minimum.
More information at: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1354672
YeAH
YeAH-TCP (Yet Another HighSpeed TCP) is a heuristic designed to balance various requirements of a
state-of-the-art congestion control algorithm:
1. fully exploit the link capacity of high BDP networks while inducing a small number of congestion
events
2. compete friendly with Reno flows
3. achieve intra and RTT fairness
4. robust to random losses
5. achieve high performance regardless of buffer size
YeAH operates between 2 modes: Fast and Slow mode. In the Fast mode when the queue occupancy is small and
the network congestion level is low, YeAH increments its congestion window according to the aggressive
HSTCP rule. When the number of packets in the queue grows beyond a threshold and the network congestion
level is high, YeAH enters its Slow mode, acting as Reno with a decongestion algorithm. YeAH employs
Vegas’ mechanism for calculating the backlog as in Equation (13). The estimation of the network
congestion level is shown in Equation (14).
Q = (RTT - BaseRTT) \cdot \frac{cWnd}{RTT}
L = \frac{RTT - BaseRTT}{BaseRTT}
To ensure TCP friendliness, YeAH also implements an algorithm to detect the presence of legacy Reno
flows. Upon the receipt of 3 duplicate ACKs, YeAH decreases its slow start threshold according to
Equation (15) if it’s not competing with Reno flows. Otherwise, the ssthresh is halved as in Reno:
ssthresh = min(max(\frac{cWnd}{8}, Q), \frac{cWnd}{2})
More information: http://www.csc.lsu.edu/~sjpark/cs7601/4-YeAH_TCP.pdf
Illinois
TCP Illinois is a hybrid congestion control algorithm designed for high-speed networks. Illinois
implements a Concave-AIMD (or C-AIMD) algorithm that uses packet loss as the primary congestion signal to
determine the direction of window update and queueing delay as the secondary congestion signal to
determine the amount of change.
The additive increase and multiplicative decrease factors (denoted as alpha and beta, respectively) are
functions of the current average queueing delay da as shown in Equations (16) and (17). To improve the
protocol robustness against sudden fluctuations in its delay sampling, Illinois allows the increment of
alpha to alphaMax only if da stays below d1 for a some (theta) amount of time.
alpha &=
\begin{cases}
\quad alphaMax & \quad \text{if } da <= d1 \\
\quad k1 / (k2 + da) & \quad \text{otherwise} \\ \end{cases}
beta &=
\begin{cases}
\quad betaMin & \quad \text{if } da <= d2 \\
\quad k3 + k4 \, da & \quad \text{if } d2 < da < d3 \\
\quad betaMax & \quad \text{otherwise} \end{cases}
where the calculations of k1, k2, k3, and k4 are shown in the following:
k1 &= \frac{(dm - d1) \cdot alphaMin \cdot alphaMax}{alphaMax - alphaMin}
k2 &= \frac{(dm - d1) \cdot alphaMin}{alphaMax - alphaMin} - d1
k3 &= \frac{alphaMin \cdot d3 - alphaMax \cdot d2}{d3 - d2}
k4 &= \frac{alphaMax - alphaMin}{d3 - d2}
Other parameters include da (the current average queueing delay), and Ta (the average RTT, calculated as
sumRtt / cntRtt in the implementation) and Tmin (baseRtt in the implementation) which is the minimum RTT
ever seen. dm is the maximum (average) queueing delay, and Tmax (maxRtt in the implementation) is the
maximum RTT ever seen.
da &= Ta - Tmin
dm &= Tmax - Tmin
d_i &= eta_i \cdot dm
Illinois only executes its adaptation of alpha and beta when cwnd exceeds a threshold called winThresh.
Otherwise, it sets alpha and beta to the base values of 1 and 0.5, respectively.
Following the implementation of Illinois in the Linux kernel, we use the following default parameter
settings:
• alphaMin = 0.3 (0.1 in the Illinois paper)
• alphaMax = 10.0
• betaMin = 0.125
• betaMax = 0.5
• winThresh = 15 (10 in the Illinois paper)
• theta = 5
• eta1 = 0.01
• eta2 = 0.1
• eta3 = 0.8
More information: http://www.doi.org/10.1145/1190095.1190166
H-TCP
H-TCP has been designed for high BDP (Bandwidth-Delay Product) paths. It is a dual mode protocol. In
normal conditions, it works like traditional TCP with the same rate of increment and decrement for the
congestion window. However, in high BDP networks, when it finds no congestion on the path after deltal
seconds, it increases the window size based on the alpha function in the following:
alpha(delta)=1+10(delta-deltal)+0.5(delta-deltal)^2
where deltal is a threshold in seconds for switching between the modes and delta is the elapsed time from
the last congestion. During congestion, it reduces the window size by multiplying by beta function
provided in the reference paper. The calculated throughput between the last two consecutive congestion
events is considered for beta calculation.
The transport TcpHtcp can be selected in the program examples/tcp/tcp-variants-comparison.cc to perform
an experiment with H-TCP, although it is useful to increase the bandwidth in this example (e.g. to 20
Mb/s) to create a higher BDP link, such as
./waf --run "tcp-variants-comparison --transport_prot=TcpHtcp --bandwidth=20Mbps --duration=10"
More information (paper): http://www.hamilton.ie/net/htcp3.pdf
More information (Internet Draft): https://tools.ietf.org/html/draft-leith-tcp-htcp-06
LEDBAT
Low Extra Delay Background Transport (LEDBAT) is an experimental delay-based congestion control algorithm
that seeks to utilize the available bandwidth on an end-to-end path while limiting the consequent
increase in queueing delay on that path. LEDBAT uses changes in one-way delay measurements to limit
congestion that the flow itself induces in the network.
As a first approximation, the LEDBAT sender operates as shown below:
On receipt of an ACK:
:: currentdelay = acknowledgement.delay basedelay = min (basedelay, currentdelay) queuingdelay =
currentdelay - basedelay offtarget = (TARGET - queuingdelay) / TARGET cWnd += GAIN * offtarget *
bytesnewlyacked * MSS / cWnd
TARGET is the maximum queueing delay that LEDBAT itself may introduce in the network, and GAIN determines
the rate at which the cwnd responds to changes in queueing delay; offtarget is a normalized value
representing the difference between the measured current queueing delay and the predetermined TARGET
delay. offtarget can be positive or negative; consequently, cwnd increases or decreases in proportion to
offtarget.
Following the recommendation of RFC 6817, the default values of the parameters are:
• TargetDelay = 100
• baseHistoryLen = 10
• noiseFilterLen = 4
• Gain = 1
To enable LEDBAT on all TCP sockets, the following configuration can be used:
Config::SetDefault ("ns3::TcpL4Protocol::SocketType", TypeIdValue (TcpLedbat::GetTypeId ()));
To enable LEDBAT on a chosen TCP socket, the following configuration can be used:
Config::Set ("$ns3::NodeListPriv/NodeList/1/$ns3::TcpL4Protocol/SocketType", TypeIdValue (TcpLedbat::GetTypeId ()));
The following unit tests have been written to validate the implementation of LEDBAT:
• LEDBAT should operate same as NewReno during slow start
• LEDBAT should operate same as NewReno if timestamps are disabled
• Test to validate cwnd increment in LEDBAT
In comparison to RFC 6817, the scope and limitations of the current LEDBAT implementation are:
• It assumes that the clocks on the sender side and receiver side are synchronised
• In line with Linux implementation, the one-way delay is calculated at the sender side by using the
timestamps option in TCP header
• Only the MIN function is used for noise filtering
More information about LEDBAT is available in RFC 6817: https://tools.ietf.org/html/rfc6817
TCP-LP
TCP-Low Priority (TCP-LP) is a delay based congestion control protocol in which the low priority data
utilizes only the excess bandwidth available on an end-to-end path. TCP-LP uses one way delay
measurements as an indicator of congestion as it does not influence cross-traffic in the reverse
direction.
On receipt of an ACK:
One way delay = Receiver timestamp - Receiver timestamp echo reply
Smoothed one way delay = 7/8 * Old Smoothed one way delay + 1/8 * one way delay If smoothed one way delay
> owdMin + 15 * (owdMax - owdMin) / 100
if LP_WITHIN_INF
cwnd = 1
else
cwnd = cwnd / 2
Inference timer is set
where owdMin and owdMax are the minimum and maximum one way delays experienced throughout the connection,
LP_WITHIN_INF indicates if TCP-LP is in inference phase or not
More information (paper): http://cs.northwestern.edu/~akuzma/rice/doc/TCP-LP.pdf
Data Center TCP (DCTCP)
DCTCP, specified in RFC 8257 and implemented in Linux, is a TCP congestion control algorithm for data
center networks. It leverages Explicit Congestion Notification (ECN) to provide more fine-grained
congestion feedback to the end hosts, and is intended to work with routers that implement a shallow
congestion marking threshold (on the order of a few milliseconds) to achieve high throughput and low
latency in the datacenter. However, because DCTCP does not react in the same way to notification of
congestion experienced, there are coexistence (fairness) issues between it and legacy TCP congestion
controllers, which is why it is recommended to only be used in controlled networking environments such as
within data centers.
DCTCP extends the Explicit Congestion Notification signal to estimate the fraction of bytes that
encounter congestion, rather than simply detecting that the congestion has occurred. DCTCP then scales
the congestion window based on this estimate. This approach achieves high burst tolerance, low latency,
and high throughput with shallow-buffered switches.
• Receiver functionality: If CE is observed in the IP header of an incoming packet at the TCP receiver,
the receiver sends congestion notification to the sender by setting ECE in TCP header. This processing
is different from standard receiver ECN processing which sets and holds the ECE bit for every ACK until
it observes a CWR signal from the TCP sender.
• Sender functionality: The sender makes use of the modified receiver ECE semantics to maintain an
estimate of the fraction of packets marked (\alpha) by using the exponential weighted moving average
(EWMA) as shown below:
\alpha = (1 - g) * \alpha + g * F
In the above EWMA:
• g is the estimation gain (between 0 and 1)
• F is the fraction of packets marked in current RTT.
For send windows in which at least one ACK was received with ECE set, the sender should respond by
reducing the congestion window as follows, once for every window of data:
cwnd = cwnd * (1 - \alpha / 2)
Following the recommendation of RFC 8257, the default values of the parameters are:
g = 0.0625 (i.e., 1/16)
initial alpha (\alpha) = 1
To enable DCTCP on all TCP sockets, the following configuration can be used:
Config::SetDefault ("ns3::TcpL4Protocol::SocketType", TypeIdValue (TcpDctcp::GetTypeId ()));
To enable DCTCP on a selected node, one can set the “SocketType” attribute on the TcpL4Protocol object of
that node to the TcpDctcp TypeId.
The ECN is enabled automatically when DCTCP is used, even if the user has not explicitly enabled it.
DCTCP depends on a simple queue management algorithm in routers / switches to mark packets. The current
implementation of DCTCP in ns-3 can use RED with a simple configuration to achieve the behavior of
desired queue management algorithm.
To configure RED router for DCTCP:
Config::SetDefault ("ns3::RedQueueDisc::UseEcn", BooleanValue (true));
Config::SetDefault ("ns3::RedQueueDisc::QW", DoubleValue (1.0));
Config::SetDefault ("ns3::RedQueueDisc::MinTh", DoubleValue (16));
Config::SetDefault ("ns3::RedQueueDisc::MaxTh", DoubleValue (16));
There is also the option, when running CoDel or FqCoDel, to enable ECN on the queue and to set the
“CeThreshold” value to a low value such as 1ms. The following example uses CoDel:
Config::SetDefault ("ns3::CoDelQueueDisc::UseEcn", BooleanValue (true));
Config::SetDefault ("ns3::CoDelQueueDisc::CeThreshold", TimeValue (MilliSeconds (1)));
The following unit tests have been written to validate the implementation of DCTCP:
• ECT flags should be set for SYN, SYN+ACK, ACK and data packets for DCTCP traffic
• ECT flags should not be set for SYN, SYN+ACK and pure ACK packets, but should be set on data packets
for ECN enabled traditional TCP flows
• ECE should be set only when CE flags are received at receiver and even if sender doesn’t send CWR,
receiver should not send ECE if it doesn’t receive packets with CE flags
An example program, examples/tcp/tcp-validation.cc, can be used to experiment with DCTCP for long-running
flows with different bottleneck link bandwidth, base RTTs, and queuing disciplines. A variant of this
program has also been run using the ns-3 Direct Code Execution environment using DCTCP from Linux kernel
4.4, and the results were compared against ns-3 results.
An example program based on an experimental topology found in the original DCTCP SIGCOMM paper is
provided in examples/tcp/dctcp-example.cc. This example uses a simple topology consisting of forty DCTCP
senders and receivers and two ECN-enabled switches to examine throughput, fairness, and queue delay
properties of the network.
This implementation was tested extensively against a version of DCTCP in the Linux kernel version 4.4
using the ns-3 direct code execution (DCE) environment. Some differences were noted:
• Linux maintains its congestion window in segments and not bytes, and the arithmetic is not floating
point, so small differences in the evolution of congestion window have been observed.
• Linux uses pacing, where packets to be sent are paced out at regular intervals. However, if at any
instant the number of segments that can be sent are less than two, Linux does not pace them and instead
sends them back-to-back. Currently, ns-3 paces out all packets eligible to be sent in the same manner.
More information about DCTCP is available in the RFC 8257: https://tools.ietf.org/html/rfc8257
BBR
BBR (class TcpBbr) is a congestion control algorithm that regulates the sending rate by deriving an
estimate of the bottleneck’s available bandwidth and RTT of the path. It seeks to operate at an optimal
point where sender experiences maximum delivery rate with minimum RTT. It creates a network model
comprising maximum delivery rate with minimum RTT observed so far, and then estimates BDP (maximum
bandwidth * minimum RTT) to control the maximum amount of inflight data. BBR controls congestion by
limiting the rate at which packets are sent. It caps the cwnd to one BDP and paces out packets at a rate
which is adjusted based on the latest estimate of delivery rate. BBR algorithm is agnostic to packet
losses and ECN marks.
pacing_gain controls the rate of sending data and cwnd_gain controls the amount of data to send.
The following is a high level overview of BBR congestion control algorithm:
On receiving an ACK:
rtt = now - packet.sent_time update_minimum_rtt (rtt) delivery_rate = estimate_delivery_rate
(packet) update_maximum_bandwidth (delivery_rate)
After transmitting a data packet:
bdp = max_bandwidth * min_rtt if (cwnd * bdp < inflight)
return
if (now > nextSendTime)
{ transmit (packet) nextSendTime = now + packet.size / (pacing_gain * max_bandwidth)
}
else return
Schedule (nextSendTime, Send)
To enable BBR on all TCP sockets, the following configuration can be used:
Config::SetDefault ("ns3::TcpL4Protocol::SocketType", TypeIdValue (TcpBbr::GetTypeId ()));
To enable BBR on a chosen TCP socket, the following configuration can be used (note that an appropriate
Node ID must be used instead of 1):
Config::Set ("$ns3::NodeListPriv/NodeList/1/$ns3::TcpL4Protocol/SocketType", TypeIdValue (TcpBbr::GetTypeId ()));
The ns-3 implementation of BBR is based on its Linux implementation. Linux 5.4 kernel implementation has
been used to validate the behavior of ns-3 implementation of BBR (See below section on Validation).
In addition, the following unit tests have been written to validate the implementation of BBR in ns-3:
• BBR should enable (if not already done) TCP pacing feature.
• Test to validate the values of pacing_gain and cwnd_gain in different phases
of BBR.
An example program, examples/tcp/tcp-bbr-example.cc, is provided to experiment with BBR for one long
running flow. This example uses a simple topology consisting of one sender, one receiver and two routers
to examine congestion window, throughput and queue control. A program similar to this has been run using
the Network Stack Tester (NeST) using BBR from Linux kernel 5.4, and the results were compared against
ns-3 results.
More information about BBR is available in the following Internet Draft:
https://tools.ietf.org/html/draft-cardwell-iccrg-bbr-congestion-control-00
More information about Delivery Rate Estimation is in the following draft:
https://tools.ietf.org/html/draft-cheng-iccrg-delivery-rate-estimation-00
For an academic peer-reviewed paper on the BBR implementation in ns-3, please refer to:
• Vivek Jain, Viyom Mittal and Mohit P. Tahiliani. “Design and Implementation of TCP BBR in ns-3.” In
Proceedings of the 10th Workshop on ns-3, pp. 16-22. 2018. (‐
https://dl.acm.org/doi/abs/10.1145/3199902.3199911)
Support for Explicit Congestion Notification (ECN)
ECN provides end-to-end notification of network congestion without dropping packets. It uses two bits in
the IP header: ECN Capable Transport (ECT bit) and Congestion Experienced (CE bit), and two bits in the
TCP header: Congestion Window Reduced (CWR) and ECN Echo (ECE).
More information is available in RFC 3168: https://tools.ietf.org/html/rfc3168
The following ECN states are declared in src/internet/model/tcp-socket-state.h
typedef enum
{
ECN_DISABLED = 0, //!< ECN disabled traffic
ECN_IDLE, //!< ECN is enabled but currently there is no action pertaining to ECE or CWR to be taken
ECN_CE_RCVD, //!< Last packet received had CE bit set in IP header
ECN_SENDING_ECE, //!< Receiver sends an ACK with ECE bit set in TCP header
ECN_ECE_RCVD, //!< Last ACK received had ECE bit set in TCP header
ECN_CWR_SENT //!< Sender has reduced the congestion window, and sent a packet with CWR bit set in TCP header. This is used for tracing.
} EcnStates_t;
Current implementation of ECN is based on RFC 3168 and is referred as Classic ECN.
The following enum represents the mode of ECN:
typedef enum
{
ClassicEcn, //!< ECN functionality as described in RFC 3168.
DctcpEcn, //!< ECN functionality as described in RFC 8257. Note: this mode is specific to DCTCP.
} EcnMode_t;
The following are some important ECN parameters:
// ECN parameters
EcnMode_t m_ecnMode {ClassicEcn}; //!< ECN mode
UseEcn_t m_useEcn {Off}; //!< Socket ECN capability
Enabling ECN
By default, support for ECN is disabled in TCP sockets. To enable, change the value of the attribute
ns3::TcpSocketBase::UseEcn to On. Following are supported values for the same, this functionality is
aligned with Linux: https://www.kernel.org/doc/Documentation/networking/ip-sysctl.txt
typedef enum
{
Off = 0, //!< Disable
On = 1, //!< Enable
AcceptOnly = 2, //!< Enable only when the peer endpoint is ECN capable
} UseEcn_t;
For example:
Config::SetDefault ("ns3::TcpSocketBase::UseEcn", StringValue ("On"))
ECN negotiation
ECN capability is negotiated during the three-way TCP handshake:
1. Sender sends SYN + CWR + ECE
if (m_useEcn == UseEcn_t::On)
{
SendEmptyPacket (TcpHeader::SYN | TcpHeader::ECE | TcpHeader::CWR);
}
else
{
SendEmptyPacket (TcpHeader::SYN);
}
m_ecnState = ECN_DISABLED;
2. Receiver sends SYN + ACK + ECE
if (m_useEcn != UseEcn_t::Off && (tcpHeader.GetFlags () & (TcpHeader::CWR | TcpHeader::ECE)) == (TcpHeader::CWR | TcpHeader::ECE))
{
SendEmptyPacket (TcpHeader::SYN | TcpHeader::ACK |TcpHeader::ECE);
m_ecnState = ECN_IDLE;
}
else
{
SendEmptyPacket (TcpHeader::SYN | TcpHeader::ACK);
m_ecnState = ECN_DISABLED;
}
3. Sender sends ACK
if (m_useEcn != UseEcn_t::Off && (tcpHeader.GetFlags () & (TcpHeader::CWR | TcpHeader::ECE)) == (TcpHeader::ECE))
{
m_ecnState = ECN_IDLE;
}
else
{
m_ecnState = ECN_DISABLED;
}
Once the ECN-negotiation is successful, the sender sends data packets with ECT bits set in the IP header.
Note: As mentioned in Section 6.1.1 of RFC 3168, ECT bits should not be set during ECN negotiation. The
ECN negotiation implemented in ns-3 follows this guideline.
ECN State Transitions
1. Initially both sender and receiver have their m_ecnState set as ECN_DISABLED
2. Once the ECN negotiation is successful, their states are set to ECN_IDLE
3. The receiver’s state changes to ECN_CE_RCVD when it receives a packet with CE bit set. The state then
moves to ECN_SENDING_ECE when the receiver sends an ACK with ECE set. This state is retained until a
CWR is received , following which, the state changes to ECN_IDLE.
4. When the sender receives an ACK with ECE bit set from receiver, its state is set as ECN_ECE_RCVD
5. The sender’s state changes to ECN_CWR_SENT when it sends a packet with CWR bit set. It remains in this
state until an ACK with valid ECE is received (i.e., ECE is received for a packet that belongs to a
new window), following which, its state changes to ECN_ECE_RCVD.
RFC 3168 compliance
Based on the suggestions provided in RFC 3168, the following behavior has been implemented:
1. Pure ACK packets should not have the ECT bit set (Section 6.1.4).
2. In the current implementation, the sender only sends ECT(0) in the IP header.
3. The sender should should reduce the congestion window only once in each window (Section 6.1.2).
4. The receiver should ignore the CE bits set in a packet arriving out of window (Section 6.1.5).
5. The sender should ignore the ECE bits set in the packet arriving out of window (Section 6.1.2).
Open issues
The following issues are yet to be addressed:
1. Retransmitted packets should not have the CWR bit set (Section 6.1.5).
2. Despite the congestion window size being 1 MSS, the sender should reduce its congestion window by half
when it receives a packet with the ECE bit set. The sender must reset the retransmit timer on
receiving the ECN-Echo packet when the congestion window is one. The sending TCP will then be able to
send a new packet only when the retransmit timer expires (Section 6.1.2).
3. Support for separately handling the enabling of ECN on the incoming and outgoing TCP sessions (e.g. a
TCP may perform ECN echoing but not set the ECT codepoints on its outbound data segments).
Support for Dynamic Pacing
TCP pacing refers to the sender-side practice of scheduling the transmission of a burst of eligible TCP
segments across a time interval such as a TCP RTT, to avoid or reduce bursts. Historically, TCP used the
natural ACK clocking mechanism to pace segments, but some network paths introduce aggregation (bursts of
ACKs arriving) or ACK thinning, either of which disrupts ACK clocking. Some latency-sensitive congestion
controls under development (Prague, BBR) require pacing to operate effectively.
Until recently, the state of the art in Linux was to support pacing in one of two ways:
1. fq/pacing with sch_fq
2. TCP internal pacing
The presentation by Dumazet and Cheng at IETF 88 summarizes:
https://www.ietf.org/proceedings/88/slides/slides-88-tcpm-9.pdf
The first option was most often used when offloading (TSO) was enabled and when the sch_fq scheduler was
used at the traffic control (qdisc) sublayer. In this case, TCP was responsible for setting the socket
pacing rate, but the qdisc sublayer would enforce it. When TSO was enabled, the kernel would break a
large burst into smaller chunks, with dynamic sizing based on the pacing rate, and hand off the segments
to the fq qdisc for pacing.
The second option was used if sch_fq was not enabled; TCP would be responsible for internally pacing.
In 2018, Linux switched to an Early Departure Model (EDM): https://lwn.net/Articles/766564/.
TCP pacing in Linux was added in kernel 3.12, and authors chose to allow a pacing rate of 200% against
the current rate, to allow probing for optimal throughput even during slow start phase. Some refinements
were added in https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/commit/?id=43e122b014c9,
in which Google reported that it was better to apply a different ratio (120%) in Congestion Avoidance
phase. Furthermore, authors found that after cwnd reduction, it was helpful to become more conservative
and switch to the conservative ratio (120%) as soon as cwnd >= ssthresh/2, as the initial ramp up (when
ssthresh is infinite) still allows doubling cwnd every other RTT. Linux also does not pace the initial
window (IW), typically 10 segments in practice.
Linux has also been observed to not pace if the number of eligible segments to be sent is exactly two;
they will be sent back to back. If three or more, the first two are sent immediately, and additional
segments are paced at the current pacing rate.
In ns-3, the model is as follows. There is no TSO/sch_fq model; only internal pacing according to
current Linux policy.
Pacing may be enabled for any TCP congestion control, and a maximum pacing rate can be set. Furthermore,
dynamic pacing is enabled for all TCP variants, according to the following guidelines.
• Pacing of the initial window (IW) is not done by default but can be separately enabled.
• Pacing of the initial slow start, after IW, is done according to the pacing rate of 200% of the current
rate, to allow for window growth This pacing rate can be configured to a different value than 200%.
• Pacing of congestion avoidance phase is done at a pacing rate of 120% of current rate. This can be
configured to a different value than 120%.
• Pacing of subsequent slow start is done according to the following heuristic. If cwnd < ssthresh/2,
such as after a timeout or idle period, pace at the slow start rate (200%). Otherwise, pace at the
congestion avoidance rate.
Dynamic pacing is demonstrated by the example program examples/tcp/tcp-pacing.cc.
Validation
The following tests are found in the src/internet/test directory. In general, TCP tests inherit from a
class called TcpGeneralTest, which provides common operations to set up test scenarios involving TCP
objects. For more information on how to write new tests, see the section below on Writing TCP tests.
• tcp: Basic transmission of string of data from client to server
• tcp-bytes-in-flight-test: TCP correctly estimates bytes in flight under loss conditions
• tcp-cong-avoid-test: TCP congestion avoidance for different packet sizes
• tcp-datasentcb: Check TCP’s ‘data sent’ callback
• tcp-endpoint-bug2211-test: A test for an issue that was causing stack overflow
• tcp-fast-retr-test: Fast Retransmit testing
• tcp-header: Unit tests on the TCP header
• tcp-highspeed-test: Unit tests on the HighSpeed congestion control
• tcp-htcp-test: Unit tests on the H-TCP congestion control
• tcp-hybla-test: Unit tests on the Hybla congestion control
• tcp-vegas-test: Unit tests on the Vegas congestion control
• tcp-veno-test: Unit tests on the Veno congestion control
• tcp-scalable-test: Unit tests on the Scalable congestion control
• tcp-bic-test: Unit tests on the BIC congestion control
• tcp-yeah-test: Unit tests on the YeAH congestion control
• tcp-illinois-test: Unit tests on the Illinois congestion control
• tcp-ledbat-test: Unit tests on the LEDBAT congestion control
• tcp-lp-test: Unit tests on the TCP-LP congestion control
• tcp-dctcp-test: Unit tests on the DCTCP congestion control
• tcp-bbr-test: Unit tests on the BBR congestion control
• tcp-option: Unit tests on TCP options
• tcp-pkts-acked-test: Unit test the number of time that PktsAcked is called
• tcp-rto-test: Unit test behavior after a RTO occurs
• tcp-rtt-estimation-test: Check RTT calculations, including retransmission cases
• tcp-slow-start-test: Check behavior of slow start
• tcp-timestamp: Unit test on the timestamp option
• tcp-wscaling: Unit test on the window scaling option
• tcp-zero-window-test: Unit test persist behavior for zero window conditions
• tcp-close-test: Unit test on the socket closing: both receiver and sender have to close their socket
when all bytes are transferred
• tcp-ecn-test: Unit tests on Explicit Congestion Notification
• tcp-pacing-test: Unit tests on dynamic TCP pacing rate
Several tests have dependencies outside of the internet module, so they are located in a system test
directory called src/test/ns3tcp. Three of these six tests involve use of the Network Simulation Cradle,
and are disabled if NSC is not enabled in the build.
• ns3-tcp-cwnd: Check to see that ns-3 TCP congestion control works against liblinux2.6.26.so
implementation
• ns3-tcp-interoperability: Check to see that ns-3 TCP interoperates with liblinux2.6.26.so
implementation
• ns3-tcp-loss: Check behavior of ns-3 TCP upon packet losses
• nsc-tcp-loss: Check behavior of NSC TCP upon packet losses
• ns3-tcp-no-delay: Check that ns-3 TCP Nagle’s algorithm works correctly and that it can be disabled
• ns3-tcp-socket: Check that ns-3 TCP successfully transfers an application data write of various sizes
• ns3-tcp-state: Check the operation of the TCP state machine for several cases
Several TCP validation test results can also be found in the wiki page describing this implementation.
The ns-3 implementation of TCP Linux Reno was validated against the NewReno implementation of Linux
kernel 4.4.0 using ns-3 Direct Code Execution (DCE). DCE is a framework which allows the users to run
kernel space protocol inside ns-3 without changing the source code.
In this validation, cwnd traces of DCE Linux reno were compared to those of ns-3 Linux Reno and NewReno
for a delayed acknowledgement configuration of 1 segment (in the ns-3 implementation; Linux does not
allow direct configuration of this setting). It can be observed that cwnd traces for ns-3 Linux Reno are
closely overlapping with DCE reno, while for ns-3 NewReno there was deviation in the congestion avoidance
phase.
[image] DCE Linux Reno vs. ns-3 Linux Reno.UNINDENT
[image] DCE Linux Reno vs. ns-3 NewReno.UNINDENT
The difference in the cwnd in the early stage of this flow is because of the way cwnd is plotted. As
ns-3 provides a trace source for cwnd, an ns-3 Linux Reno cwnd simple is obtained every time the cwnd
value changes, whereas for DCE Linux Reno, the kernel does not have a corresponding trace source.
Instead, we use the “ss” command of the Linux kernel to obtain cwnd values. The “ss” samples cwnd at an
interval of 0.5 seconds.
Figure DCTCP throughput for 10ms/50Mbps bottleneck, 1ms CE threshold shows a long-running file transfer
using DCTCP over a 50 Mbps bottleneck (running CoDel queue disc with a 1ms CE threshold setting) with a
10 ms base RTT. The figure shows that DCTCP reaches link capacity very quickly and stays there for the
duration with minimal change in throughput. In contrast, Figure DCTCP throughput for 80ms/50Mbps
bottleneck, 1ms CE threshold plots the throughput for the same configuration except with an 80 ms base
RTT. In this case, the DCTCP exits slow start early and takes a long time to build the flow throughput
to the bottleneck link capacity. DCTCP is not intended to be used at such a large base RTT, but this
figure highlights the sensitivity to RTT (and can be reproduced using the Linux implementation).
[image] DCTCP throughput for 10ms/50Mbps bottleneck, 1ms CE threshold.UNINDENT
[image] DCTCP throughput for 80ms/50Mbps bottleneck, 1ms CE threshold.UNINDENT
Similar to DCTCP, TCP CUBIC has been tested against the Linux kernel version 4.4 implementation.
Figure CUBIC cwnd evolution for 50ms/50Mbps bottleneck, no ECN compares the congestion window evolution
between ns-3 and Linux for a single flow operating over a 50 Mbps link with 50 ms base RTT and the
CoDel AQM. Some differences can be observed between the peak of slow start window growth (ns-3 exits
slow start earlier due to its HyStart implementation), and the window growth is a bit out-of-sync
(likely due to different implementations of the algorithm), but the cubic concave/convex window
pattern, and the signs of TCP CUBIC fast convergence algorithm (alternating patterns of cubic and
concave window growth) can be observed. The ns-3 congestion window is maintained in bytes (unlike
Linux which uses segments) but has been normalized to segments for these plots. Figure CUBIC cwnd
evolution for 50ms/50Mbps bottleneck, with ECN displays the outcome of a similar scenario but with ECN
enabled throughout.
[image] CUBIC cwnd evolution for 50ms/50Mbps bottleneck, no ECN.UNINDENT
[image] CUBIC cwnd evolution for 50ms/50Mbps bottleneck, with ECN.UNINDENT
TCP ECN operation is tested in the ARED and RED tests that are documented in the traffic-control module
documentation.
Like DCTCP and TCP CUBIC, the ns-3 implementation of TCP BBR was validated against the BBR
implementation of Linux kernel 5.4 using Network Stack Tester (NeST). NeST is a python package which
allows the users to emulate kernel space protocols using Linux network namespaces. Figure Congestion
window evolution: ns-3 BBR vs. Linux BBR (using NeST) compares the congestion window evolution between
ns-3 and Linux for a single flow operating over a 10 Mbps link with 10 ms base RTT and FIFO queue
discipline.
[image] Congestion window evolution: ns-3 BBR vs. Linux BBR (using NeST).UNINDENT
It can be observed that the congestion window traces for ns-3 BBR closely overlap with Linux BBR. The
periodic drops in congestion window every 10 seconds depict the PROBE_RTT phase of the BBR algorithm.
In this phase, BBR algorithm keeps the congestion window fixed to 4 segments.
The example program, examples/tcp-bbr-example.cc has been used to obtain the congestion window curve
shown in Figure Congestion window evolution: ns-3 BBR vs. Linux BBR (using NeST). The detailed
instructions to reproduce ns-3 plot and NeST plot can be found at:
https://github.com/mohittahiliani/BBR-Validation
Writing a new congestion control algorithm
Writing (or porting) a congestion control algorithms from scratch (or from other systems) is a process
completely separated from the internals of TcpSocketBase.
All operations that are delegated to a congestion control are contained in the class TcpCongestionOps. It
mimics the structure tcp_congestion_ops of Linux, and the following operations are defined:
virtual std::string GetName () const;
virtual uint32_t GetSsThresh (Ptr<const TcpSocketState> tcb, uint32_t bytesInFlight);
virtual void IncreaseWindow (Ptr<TcpSocketState> tcb, uint32_t segmentsAcked);
virtual void PktsAcked (Ptr<TcpSocketState> tcb, uint32_t segmentsAcked,const Time& rtt);
virtual Ptr<TcpCongestionOps> Fork ();
virtual void CwndEvent (Ptr<TcpSocketState> tcb, const TcpSocketState::TcpCaEvent_t event);
The most interesting methods to write are GetSsThresh and IncreaseWindow. The latter is called when
TcpSocketBase decides that it is time to increase the congestion window. Much information is available in
the Transmission Control Block, and the method should increase cWnd and/or ssThresh based on the number
of segments acked.
GetSsThresh is called whenever the socket needs an updated value of the slow start threshold. This
happens after a loss; congestion control algorithms are then asked to lower such value, and to return it.
PktsAcked is used in case the algorithm needs timing information (such as RTT), and it is called each
time an ACK is received.
CwndEvent is used in case the algorithm needs the state of socket during different congestion window
event.
TCP SACK and non-SACK
To avoid code duplication and the effort of maintaining two different versions of the TCP core, namely
RFC 6675 (TCP-SACK) and RFC 5681 (TCP congestion control), we have merged RFC 6675 in the current code
base. If the receiver supports the option, the sender bases its retransmissions over the received SACK
information. However, in the absence of that option, the best it can do is to follow the RFC 5681
specification (on Fast Retransmit/Recovery) and employing NewReno modifications in case of partial ACKs.
A similar concept is used in Linux with the function tcp_add_reno_sack. Our implementation resides in
the TcpTxBuffer class that implements a scoreboard through two different lists of segments. TcpSocketBase
actively uses the API provided by TcpTxBuffer to query the scoreboard; please refer to the Doxygen
documentation (and to in-code comments) if you want to learn more about this implementation.
For an academic peer-reviewed paper on the SACK implementation in ns-3, please refer to
https://dl.acm.org/citation.cfm?id=3067666.
Loss Recovery Algorithms
The following loss recovery algorithms are supported in ns-3 TCP. The current default (as of ns-3.32
release) is Proportional Rate Reduction (PRR), while the default for ns-3.31 and earlier was Classic
Recovery.
Classic Recovery
Classic Recovery refers to the combination of NewReno algorithm described in RFC 6582 along with SACK
based loss recovery algorithm mentioned in RFC 6675. SACK based loss recovery is used when sender and
receiver support SACK options. In the case when SACK options are disabled, the NewReno modification
handles the recovery.
At the start of recovery phase the congestion window is reduced diffently for NewReno and SACK based
recovery. For NewReno the reduction is done as given below:
cWnd = ssThresh
For SACK based recovery, this is done as follows:
cWnd = ssThresh + (dupAckCount * segmentSize)
While in the recovery phase, the congestion window is inflated by segmentSize on arrival of every ACK
when NewReno is used. The congestion window is kept same when SACK based loss recovery is used.
Proportional Rate Reduction
Proportional Rate Reduction (PRR) is a loss recovery algorithm described in RFC 6937 and currently used
in Linux. The design of PRR helps in avoiding excess window adjustments and aims to keep the congestion
window as close as possible to ssThresh.
PRR updates the congestion window by comparing the values of bytesInFlight and ssThresh. If the value of
bytesInFlight is greater than ssThresh, congestion window is updated as shown below:
sndcnt = CEIL(prrDelivered * ssThresh / RecoverFS) - prrOut
cWnd = pipe + sndcnt
where RecoverFS is the value of bytesInFlight at the start of recovery phase, prrDelivered is the total
bytes delivered during recovery phase, prrOut is the total bytes sent during recovery phase and sndcnt
represents the number of bytes to be sent in response to each ACK.
Otherwise, the congestion window is updated by either using Conservative Reduction Bound (CRB) or Slow
Start Reduction Bound (SSRB) with SSRB being the default Reduction Bound. Each Reduction Bound calculates
a maximum data sending limit. For CRB, the limit is calculated as shown below:
limit = prrDelivered - prr out
For SSRB, it is calculated as:
limit = MAX(prrDelivered - prrOut, DeliveredData) + MSS
where DeliveredData represets the total number of bytes delivered to the receiver as indicated by the
current ACK and MSS is the maximum segment size.
After limit calculation, the cWnd is updated as given below:
sndcnt = MIN (ssThresh - pipe, limit)
cWnd = pipe + sndcnt
More information (paper): https://dl.acm.org/citation.cfm?id=2068832
More information (RFC): https://tools.ietf.org/html/rfc6937
Adding a new loss recovery algorithm in ns-3
Writing (or porting) a loss recovery algorithms from scratch (or from other systems) is a process
completely separated from the internals of TcpSocketBase.
All operations that are delegated to a loss recovery are contained in the class TcpRecoveryOps and are
given below:
virtual std::string GetName () const;
virtual void EnterRecovery (Ptr<const TcpSocketState> tcb, uint32_t unAckDataCount,
bool isSackEnabled, uint32_t dupAckCount,
uint32_t bytesInFlight, uint32_t lastDeliveredBytes);
virtual void DoRecovery (Ptr<const TcpSocketState> tcb, uint32_t unAckDataCount,
bool isSackEnabled, uint32_t dupAckCount,
uint32_t bytesInFlight, uint32_t lastDeliveredBytes);
virtual void ExitRecovery (Ptr<TcpSocketState> tcb, uint32_t bytesInFlight);
virtual void UpdateBytesSent (uint32_t bytesSent);
virtual Ptr<TcpRecoveryOps> Fork ();
EnterRecovery is called when packet loss is detected and recovery is triggered. While in recovery phase,
each time when an ACK arrives, DoRecovery is called which performs the necessary congestion window
changes as per the recovery algorithm. ExitRecovery is called just prior to exiting recovery phase in
order to perform the required congestion window ajustments. UpdateBytesSent is used to keep track of
bytes sent and is called whenever a data packet is sent during recovery phase.
Delivery Rate Estimation
Current TCP implementation measures the approximate value of the delivery rate of inflight data based on
Delivery Rate Estimation.
As high level idea, keep in mind that the algorithm keeps track of 2 variables:
1. delivered: Total amount of data delivered so far.
2. deliveredStamp: Last time delivered was updated.
When a packet is transmitted, the value of delivered (d0) and deliveredStamp (t0) is stored in its
respective TcpTxItem.
When an acknowledgement comes for this packet, the value of delivered and deliveredStamp is updated to d1
and t1 in the same TcpTxItem.
After processing the acknowledgement, the rate sample is calculated and then passed to a congestion
avoidance algorithm:
delivery_rate = (d1 - d0)/(t1 - t0)
The implementation to estimate delivery rate is a joint work between TcpTxBuffer and TcpRateOps. For
more information, please take a look at their Doxygen documentation.
The implementation follows the Internet draft (Delivery Rate Estimation):
https://tools.ietf.org/html/draft-cheng-iccrg-delivery-rate-estimation-00
Current limitations
• TcpCongestionOps interface does not contain every possible Linux operation
Writing TCP tests
The TCP subsystem supports automated test cases on both socket functions and congestion control
algorithms. To show how to write tests for TCP, here we explain the process of creating a test case that
reproduces the Bug #1571.
The bug concerns the zero window situation, which happens when the receiver cannot handle more data. In
this case, it advertises a zero window, which causes the sender to pause transmission and wait for the
receiver to increase the window.
The sender has a timer to periodically check the receiver’s window: however, in modern TCP
implementations, when the receiver has freed a “significant” amount of data, the receiver itself sends an
“active” window update, meaning that the transmission could be resumed. Nevertheless, the sender timer is
still necessary because window updates can be lost.
NOTE:
During the text, we will assume some knowledge about the general design of the TCP test
infrastructure, which is explained in detail into the Doxygen documentation. As a brief summary, the
strategy is to have a class that sets up a TCP connection, and that calls protected members of itself.
In this way, subclasses can implement the necessary members, which will be called by the main
TcpGeneralTest class when events occur. For example, after processing an ACK, the method ProcessedAck
will be invoked. Subclasses interested in checking some particular things which must have happened
during an ACK processing, should implement the ProcessedAck method and check the interesting values
inside the method. To get a list of available methods, please check the Doxygen documentation.
We describe the writing of two test cases, covering both situations: the sender’s zero-window probing and
the receiver “active” window update. Our focus will be on dealing with the reported problems, which are:
• an ns-3 receiver does not send “active” window update when its receive buffer is being freed;
• even if the window update is artificially crafted, the transmission does not resume.
However, other things should be checked in the test:
• Persistent timer setup
• Persistent timer teardown if rWnd increases
To construct the test case, one first derives from the TcpGeneralTest class:
The code is the following:
TcpZeroWindowTest::TcpZeroWindowTest (const std::string &desc)
: TcpGeneralTest (desc)
{
}
Then, one should define the general parameters for the TCP connection, which will be one-sided (one node
is acting as SENDER, while the other is acting as RECEIVER):
• Application packet size set to 500, and 20 packets in total (meaning a stream of 10k bytes)
• Segment size for both SENDER and RECEIVER set to 500 bytes
• Initial slow start threshold set to UINT32_MAX
• Initial congestion window for the SENDER set to 10 segments (5000 bytes)
• Congestion control: NewReno
We have also to define the link properties, because the above definition does not work for every
combination of propagation delay and sender application behavior.
• Link one-way propagation delay: 50 ms
• Application packet generation interval: 10 ms
• Application starting time: 20 s after the starting point
To define the properties of the environment (e.g. properties which should be set before the object
creation, such as propagation delay) one next implements the method ConfigureEnvironment:
void
TcpZeroWindowTest::ConfigureEnvironment ()
{
TcpGeneralTest::ConfigureEnvironment ();
SetAppPktCount (20);
SetMTU (500);
SetTransmitStart (Seconds (2.0));
SetPropagationDelay (MilliSeconds (50));
}
For other properties, set after the object creation, one can use ConfigureProperties (). The difference
is that some values, such as initial congestion window or initial slow start threshold, are applicable
only to a single instance, not to every instance we have. Usually, methods that requires an id and a
value are meant to be called inside ConfigureProperties (). Please see the Doxygen documentation for an
exhaustive list of the tunable properties.
void
TcpZeroWindowTest::ConfigureProperties ()
{
TcpGeneralTest::ConfigureProperties ();
SetInitialCwnd (SENDER, 10);
}
To see the default value for the experiment, please see the implementation of both methods inside
TcpGeneralTest class.
NOTE:
If some configuration parameters are missing, add a method called “SetSomeValue” which takes as input
the value only (if it is meant to be called inside ConfigureEnvironment) or the socket and the value
(if it is meant to be called inside ConfigureProperties).
To define a zero-window situation, we choose (by design) to initiate the connection with a 0-byte rx
buffer. This implies that the RECEIVER, in its first SYN-ACK, advertises a zero window. This can be
accomplished by implementing the method CreateReceiverSocket, setting an Rx buffer value of 0 bytes (at
line 6 of the following code):
Ptr<TcpSocketMsgBase>
TcpZeroWindowTest::CreateReceiverSocket (Ptr<Node> node)
{
Ptr<TcpSocketMsgBase> socket = TcpGeneralTest::CreateReceiverSocket (node);
socket->SetAttribute("RcvBufSize", UintegerValue (0));
Simulator::Schedule (Seconds (10.0),
&TcpZeroWindowTest::IncreaseBufSize, this);
return socket;
}
Even so, to check the active window update, we should schedule an increase of the buffer size. We do this
at line 7 and 8, scheduling the function IncreaseBufSize.
void
TcpZeroWindowTest::IncreaseBufSize ()
{
SetRcvBufSize (RECEIVER, 2500);
}
Which utilizes the SetRcvBufSize method to edit the RxBuffer object of the RECEIVER. As said before,
check the Doxygen documentation for class TcpGeneralTest to be aware of the various possibilities that it
offers.
NOTE:
By design, we choose to maintain a close relationship between TcpSocketBase and TcpGeneralTest: they
are connected by a friendship relation. Since friendship is not passed through inheritance, if one
discovers that one needs to access or to modify a private (or protected) member of TcpSocketBase, one
can do so by adding a method in the class TcpGeneralSocket. An example of such method is
SetRcvBufSize, which allows TcpGeneralSocket subclasses to forcefully set the RxBuffer size.
void
TcpGeneralTest::SetRcvBufSize (SocketWho who, uint32_t size)
{
if (who == SENDER)
{
m_senderSocket->SetRcvBufSize (size);
}
else if (who == RECEIVER)
{
m_receiverSocket->SetRcvBufSize (size);
}
else
{
NS_FATAL_ERROR ("Not defined");
}
}
Next, we can start to follow the TCP connection:
1. At time 0.0 s the connection is opened sender side, with a SYN packet sent from SENDER to RECEIVER
2. At time 0.05 s the RECEIVER gets the SYN and replies with a SYN-ACK
3. At time 0.10 s the SENDER gets the SYN-ACK and replies with a SYN.
While the general structure is defined, and the connection is started, we need to define a way to check
the rWnd field on the segments. To this aim, we can implement the methods Rx and Tx in the TcpGeneralTest
subclass, checking each time the actions of the RECEIVER and the SENDER. These methods are defined in
TcpGeneralTest, and they are attached to the Rx and Tx traces in the TcpSocketBase. One should write
small tests for every detail that one wants to ensure during the connection (it will prevent the test
from changing over the time, and it ensures that the behavior will stay consistent through releases). We
start by ensuring that the first SYN-ACK has 0 as advertised window size:
void
TcpZeroWindowTest::Tx(const Ptr<const Packet> p, const TcpHeader &h, SocketWho who)
{
...
else if (who == RECEIVER)
{
NS_LOG_INFO ("\tRECEIVER TX " << h << " size " << p->GetSize());
if (h.GetFlags () & TcpHeader::SYN)
{
NS_TEST_ASSERT_MSG_EQ (h.GetWindowSize(), 0,
"RECEIVER window size is not 0 in the SYN-ACK");
}
}
....
}
Pratically, we are checking that every SYN packet sent by the RECEIVER has the advertised window set to
0. The same thing is done also by checking, in the Rx method, that each SYN received by SENDER has the
advertised window set to 0. Thanks to the log subsystem, we can print what is happening through
messages. If we run the experiment, enabling the logging, we can see the following:
./waf shell
gdb --args ./build/utils/ns3-dev-test-runner-debug --test-name=tcp-zero-window-test --stop-on-failure --fullness=QUICK --assert-on-failure --verbose
(gdb) run
0.00s TcpZeroWindowTestSuite:Tx(): 0.00 SENDER TX 49153 > 4477 [SYN] Seq=0 Ack=0 Win=32768 ns3::TcpOptionWinScale(2) ns3::TcpOptionTS(0;0) size 36
0.05s TcpZeroWindowTestSuite:Rx(): 0.05 RECEIVER RX 49153 > 4477 [SYN] Seq=0 Ack=0 Win=32768 ns3::TcpOptionWinScale(2) ns3::TcpOptionTS(0;0) ns3::TcpOptionEnd(EOL) size 0
0.05s TcpZeroWindowTestSuite:Tx(): 0.05 RECEIVER TX 4477 > 49153 [SYN|ACK] Seq=0 Ack=1 Win=0 ns3::TcpOptionWinScale(0) ns3::TcpOptionTS(50;0) size 36
0.10s TcpZeroWindowTestSuite:Rx(): 0.10 SENDER RX 4477 > 49153 [SYN|ACK] Seq=0 Ack=1 Win=0 ns3::TcpOptionWinScale(0) ns3::TcpOptionTS(50;0) ns3::TcpOptionEnd(EOL) size 0
0.10s TcpZeroWindowTestSuite:Tx(): 0.10 SENDER TX 49153 > 4477 [ACK] Seq=1 Ack=1 Win=32768 ns3::TcpOptionTS(100;50) size 32
0.15s TcpZeroWindowTestSuite:Rx(): 0.15 RECEIVER RX 49153 > 4477 [ACK] Seq=1 Ack=1 Win=32768 ns3::TcpOptionTS(100;50) ns3::TcpOptionEnd(EOL) size 0
(...)
The output is cut to show the threeway handshake. As we can see from the headers, the rWnd of RECEIVER is
set to 0, and thankfully our tests are not failing. Now we need to test for the persistent timer, which
should be started by the SENDER after it receives the SYN-ACK. Since the Rx method is called before any
computation on the received packet, we should utilize another method, namely ProcessedAck, which is the
method called after each processed ACK. In the following, we show how to check if the persistent event is
running after the processing of the SYN-ACK:
void
TcpZeroWindowTest::ProcessedAck (const Ptr<const TcpSocketState> tcb,
const TcpHeader& h, SocketWho who)
{
if (who == SENDER)
{
if (h.GetFlags () & TcpHeader::SYN)
{
EventId persistentEvent = GetPersistentEvent (SENDER);
NS_TEST_ASSERT_MSG_EQ (persistentEvent.IsRunning (), true,
"Persistent event not started");
}
}
}
Since we programmed the increase of the buffer size after 10 simulated seconds, we expect the persistent
timer to fire before any rWnd changes. When it fires, the SENDER should send a window probe, and the
receiver should reply reporting again a zero window situation. At first, we investigates on what the
sender sends:
if (Simulator::Now ().GetSeconds () <= 6.0)
{
NS_TEST_ASSERT_MSG_EQ (p->GetSize () - h.GetSerializedSize(), 0,
"Data packet sent anyway");
}
else if (Simulator::Now ().GetSeconds () > 6.0 &&
Simulator::Now ().GetSeconds () <= 7.0)
{
NS_TEST_ASSERT_MSG_EQ (m_zeroWindowProbe, false, "Sent another probe");
if (! m_zeroWindowProbe)
{
NS_TEST_ASSERT_MSG_EQ (p->GetSize () - h.GetSerializedSize(), 1,
"Data packet sent instead of window probe");
NS_TEST_ASSERT_MSG_EQ (h.GetSequenceNumber(), SequenceNumber32 (1),
"Data packet sent instead of window probe");
m_zeroWindowProbe = true;
}
}
We divide the events by simulated time. At line 1, we check everything that happens before the 6.0
seconds mark; for instance, that no data packets are sent, and that the state remains OPEN for both
sender and receiver.
Since the persist timeout is initialized at 6 seconds (exercise left for the reader: edit the test,
getting this value from the Attribute system), we need to check (line 6) between 6.0 and 7.0 simulated
seconds that the probe is sent. Only one probe is allowed, and this is the reason for the check at line
11.
if (Simulator::Now ().GetSeconds () > 6.0 &&
Simulator::Now ().GetSeconds () <= 7.0)
{
NS_TEST_ASSERT_MSG_EQ (h.GetSequenceNumber(), SequenceNumber32 (1),
"Data packet sent instead of window probe");
NS_TEST_ASSERT_MSG_EQ (h.GetWindowSize(), 0,
"No zero window advertised by RECEIVER");
}
For the RECEIVER, the interval between 6 and 7 seconds is when the zero-window segment is sent.
Other checks are redundant; the safest approach is to deny any other packet exchange between the 7 and 10
seconds mark.
else if (Simulator::Now ().GetSeconds () > 7.0 &&
Simulator::Now ().GetSeconds () < 10.0)
{
NS_FATAL_ERROR ("No packets should be sent before the window update");
}
The state checks are performed at the end of the methods, since they are valid in every condition:
NS_TEST_ASSERT_MSG_EQ (GetCongStateFrom (GetTcb(SENDER)), TcpSocketState::CA_OPEN,
"Sender State is not OPEN");
NS_TEST_ASSERT_MSG_EQ (GetCongStateFrom (GetTcb(RECEIVER)), TcpSocketState::CA_OPEN,
"Receiver State is not OPEN");
Now, the interesting part in the Tx method is to check that after the 10.0 seconds mark (when the
RECEIVER sends the active window update) the value of the window should be greater than zero (and
precisely, set to 2500):
else if (Simulator::Now().GetSeconds() >= 10.0)
{
NS_TEST_ASSERT_MSG_EQ (h.GetWindowSize(), 2500,
"Receiver window not updated");
}
To be sure that the sender receives the window update, we can use the Rx method:
if (Simulator::Now().GetSeconds() >= 10.0)
{
NS_TEST_ASSERT_MSG_EQ (h.GetWindowSize(), 2500,
"Receiver window not updated");
m_windowUpdated = true;
}
We check every packet after the 10 seconds mark to see if it has the window updated. At line 5, we also
set to true a boolean variable, to check that we effectively reach this test.
Last but not least, we implement also the NormalClose() method, to check that the connection ends with a
success:
void
TcpZeroWindowTest::NormalClose (SocketWho who)
{
if (who == SENDER)
{
m_senderFinished = true;
}
else if (who == RECEIVER)
{
m_receiverFinished = true;
}
}
The method is called only if all bytes are transmitted successfully. Then, in the method FinalChecks(),
we check all variables, which should be true (which indicates that we have perfectly closed the
connection).
void
TcpZeroWindowTest::FinalChecks ()
{
NS_TEST_ASSERT_MSG_EQ (m_zeroWindowProbe, true,
"Zero window probe not sent");
NS_TEST_ASSERT_MSG_EQ (m_windowUpdated, true,
"Window has not updated during the connection");
NS_TEST_ASSERT_MSG_EQ (m_senderFinished, true,
"Connection not closed successfully (SENDER)");
NS_TEST_ASSERT_MSG_EQ (m_receiverFinished, true,
"Connection not closed successfully (RECEIVER)");
}
To run the test, the usual way is
./test.py -s tcp-zero-window-test
PASS: TestSuite tcp-zero-window-test
1 of 1 tests passed (1 passed, 0 skipped, 0 failed, 0 crashed, 0 valgrind errors)
To see INFO messages, use a combination of ./waf shell and gdb (really useful):
./waf shell && gdb --args ./build/utils/ns3-dev-test-runner-debug --test-name=tcp-zero-window-test --stop-on-failure --fullness=QUICK --assert-on-failure --verbose
and then, hit “Run”.
NOTE:
This code magically runs without any reported errors; however, in real cases, when you discover a bug
you should expect the existing test to fail (this could indicate a well-written test and a bad-writted
model, or a bad-written test; hopefully the first situation). Correcting bugs is an iterative process.
For instance, commits created to make this test case running without errors are 11633:6b74df04cf44,
(others to be merged).
Network Simulation Cradle
The Network Simulation Cradle (NSC) is a framework for wrapping real-world network code into simulators,
allowing simulation of real-world behavior at little extra cost. This work has been validated by
comparing situations using a test network with the same situations in the simulator. To date, it has been
shown that the NSC is able to produce extremely accurate results. NSC supports four real world stacks:
FreeBSD, OpenBSD, lwIP and Linux. Emphasis has been placed on not changing any of the network stacks by
hand. Not a single line of code has been changed in the network protocol implementations of any of the
above four stacks. However, a custom C parser was built to programmatically change source code.
NSC has previously been ported to ns-2 and OMNeT++, and was was added to ns-3 in September 2008 (ns-3.2
release). This section describes the ns-3 port of NSC and how to use it.
NSC has been obsoleted by the Linux kernel support within Direct Code Execution (DCE). However, NSC is
still available through the bake build system. NSC supports Linux kernels 2.6.18 and 2.6.26, and an
experimental version of 2.6.29 exists on ns-3’s code server (http://code.nsnam.org/fw/nsc-linux-2.6.29/),
but newer versions of the kernel have not been ported.
Prerequisites
Presently, NSC has been tested and shown to work on these platforms: Linux i386 and Linux x86-64. NSC
does not support powerpc. Use on FreeBSD or OS X is unsupported (although it may be able to work).
Building NSC requires the packages flex and bison.
NSC requires use of gcc-4.9 or gcc-5 series, and will not build on newer systems lacking the older
compilers.
Configuring and Downloading
NSC must either be downloaded separately from its own repository, or downloading when using the bake
build system of ns-3.
For ns-3.17 through ns-3.28 releases, when using bake, one obtains NSC implicitly as part of an
“allinone” configuration, such as:
$ cd bake
$ python bake.py configure -e ns-allinone-3.27
$ python bake.py download
$ python bake.py build
For ns-3.29 and later versions, including the ‘ns-3-allinone’ development version, one must explicitly
add NSC (‘nsc-0.5.3’) to the bake configuration, such as:
$ cd bake
$ python bake.py configure -e ns-allinone-3.29 -e nsc-0.5.3
$ python bake.py download
$ python bake.py build
Instead of a released version, one may use the ns-3 development version by specifying “ns-3-allinone” to
the configure step above.
NSC may also be downloaded from its download site using Mercurial:
$ hg clone https://secure.wand.net.nz/mercurial/nsc
Prior to the ns-3.17 release, NSC was included in the allinone tarball and the released version did not
need to be separately downloaded.
Building and validating
NSC may be built as part of the bake build process; alternatively, one may build NSC by itself using its
build system; e.g.:
$ cd nsc-dev
$ python scons.py
Once NSC has been built either manually or through the bake system, change into the ns-3 source directory
and try running the following configuration:
$ ./waf configure
If NSC has been previously built and found by waf, then you will see:
Network Simulation Cradle : enabled
If NSC has not been found, you will see:
Network Simulation Cradle : not enabled (NSC not found (see option --with-nsc))
In this case, you must pass the relative or absolute path to the NSC libraries with the “–with-nsc”
configure option; e.g.
$ ./waf configure --with-nsc=/path/to/my/nsc/directory
For ns-3 releases prior to the ns-3.17 release, using the build.py script in ns-3-allinone directory, NSC
will be built by default unless the platform does not support it. To explicitly disable it when building
ns-3, type:
$ ./waf configure --enable-examples --enable-tests --disable-nsc
If waf detects NSC, then building ns-3 with NSC is performed the same way with waf as without it. Once
ns-3 is built, try running the following test suite:
$ ./test.py -s ns3-tcp-interoperability
If NSC has been successfully built, the following test should show up in the results:
PASS TestSuite ns3-tcp-interoperability
This confirms that NSC is ready to use.
Usage
There are a few example files. Try:
$ ./waf --run tcp-nsc-zoo
$ ./waf --run tcp-nsc-lfn
These examples will deposit some .pcap files in your directory, which can be examined by tcpdump or
wireshark.
Let’s look at the examples/tcp/tcp-nsc-zoo.cc file for some typical usage. How does it differ from using
native ns-3 TCP? There is one main configuration line, when using NSC and the ns-3 helper API, that needs
to be set:
InternetStackHelper internetStack;
internetStack.SetNscStack ("liblinux2.6.26.so");
// this switches nodes 0 and 1 to NSCs Linux 2.6.26 stack.
internetStack.Install (n.Get(0));
internetStack.Install (n.Get(1));
The key line is the SetNscStack. This tells the InternetStack helper to aggregate instances of NSC TCP
instead of native ns-3 TCP to the remaining nodes. It is important that this function be called before
calling the Install() function, as shown above.
Which stacks are available to use? Presently, the focus has been on Linux 2.6.18 and Linux 2.6.26 stacks
for ns-3. To see which stacks were built, one can execute the following find command at the ns-3 top
level directory:
$ find nsc -name "*.so" -type f
nsc/linux-2.6.18/liblinux2.6.18.so
nsc/linux-2.6.26/liblinux2.6.26.so
This tells us that we may either pass the library name liblinux2.6.18.so or liblinux2.6.26.so to the
above configuration step.
Stack configuration
NSC TCP shares the same configuration attributes that are common across TCP sockets, as described above
and documented in Doxygen
Additionally, NSC TCP exports a lot of configuration variables into the ns-3 attributes system, via a
sysctl-like interface. In the examples/tcp/tcp-nsc-zoo example, you can see the following configuration:
// this disables TCP SACK, wscale and timestamps on node 1 (the attributes
represent sysctl-values).
Config::Set ("/NodeList/1/$ns3::Ns3NscStack<linux2.6.26>/net.ipv4.tcp_sack",
StringValue ("0"));
Config::Set ("/NodeList/1/$ns3::Ns3NscStack<linux2.6.26>/net.ipv4.tcp_timestamps",
StringValue ("0"));
Config::Set ("/NodeList/1/$ns3::Ns3NscStack<linux2.6.26>/net.ipv4.tcp_window_scaling",
StringValue ("0"));
These additional configuration variables are not available to native ns-3 TCP.
Also note that default values for TCP attributes in ns-3 TCP may differ from the NSC TCP implementation.
Specifically in ns-3:
1. TCP default MSS is 536
2. TCP Delayed ACK count is 2
Therefore when making comparisons between results obtained using NSC and ns-3 TCP, care must be taken to
ensure these values are set appropriately. See /examples/tcp/tcp-nsc-comparison.cc for an example.
NSC API
This subsection describes the API that NSC presents to ns-3 or any other simulator. NSC provides its API
in the form of a number of classes that are defined in sim/sim_interface.h in the nsc directory.
• INetStack INetStack contains the ‘low level’ operations for the operating system network stack, e.g. in
and output functions from and to the network stack (think of this as the ‘network driver interface’).
There are also functions to create new TCP or UDP sockets.
• ISendCallback This is called by NSC when a packet should be sent out to the network. This simulator
should use this callback to re-inject the packet into the simulator so the actual data can be
delivered/routed to its destination, where it will eventually be handed into Receive() (and eventually
back to the receivers NSC instance via INetStack->if_receive()).
• INetStreamSocket This is the structure defining a particular connection endpoint (file descriptor). It
contains methods to operate on this endpoint, e.g. connect, disconnect, accept, listen,
send_data/read_data, …
• IInterruptCallback This contains the wakeup() callback, which is called by NSC whenever something of
interest happens. Think of wakeup() as a replacement of the operating systems wakeup function: Whenever
the operating system would wake up a process that has been waiting for an operation to complete (for
example the TCP handshake during connect()), NSC invokes the wakeup() callback to allow the simulator
to check for state changes in its connection endpoints.
ns-3 implementation
The ns-3 implementation makes use of the above NSC API, and is implemented as follows.
The three main parts are:
• ns3::NscTcpL4Protocol: a subclass of Ipv4L4Protocol (and two NSC classes: ISendCallback and
IInterruptCallback)
• ns3::NscTcpSocketImpl: a subclass of TcpSocket
• ns3::NscTcpSocketFactoryImpl: a factory to create new NSC sockets
src/internet/model/nsc-tcp-l4-protocol is the main class. Upon Initialization, it loads an NSC network
stack to use (via dlopen()). Each instance of this class may use a different stack. The stack (=shared
library) to use is set using the SetNscLibrary() method (at this time its called indirectly via the
internet stack helper). The NSC stack is then set up accordingly (timers etc). The
NscTcpL4Protocol::Receive() function hands the packet it receives (must be a complete TCP/IP packet) to
the NSC stack for further processing. To be able to send packets, this class implements the NSC
send_callback() method. This method is called by NSC whenever the NSC stack wishes to send a packet out
to the network. Its arguments are a raw buffer, containing a complete TCP/IP packet, and a length value.
This method therefore has to convert the raw data to a Ptr<Packet> usable by ns-3. In order to avoid
various IPv4 header issues, the NSC IP header is not included. Instead, the TCP header and the actual
payload are put into the Ptr<Packet>, after this the Packet is passed down to layer 3 for sending the
packet out (no further special treatment is needed in the send code path).
This class calls ns3::NscTcpSocketImpl both from the NSC wakeup() callback and from the receive path (to
ensure that possibly queued data is scheduled for sending).
src/internet/model/nsc-tcp-socket-impl implements the NSC socket interface. Each instance has its own
m_nscTcpSocket. Data that is sent will be handed to the NSC stack via m_nscTcpSocket->send_data() (and
not to NscTcpL4Protocol, this is the major difference compared to ns-3 TCP). The class also queues up
data that is sent before the underlying descriptor has entered an ESTABLISHED state. This class is
called from the NscTcpL4Protocol class, when the NscTcpL4Protocol wakeup() callback is invoked by NSC.
NscTcpSocketImpl then checks the current connection state (SYN_SENT, ESTABLISHED, LISTEN…) and schedules
appropriate callbacks as needed, e.g. a LISTEN socket will schedule accept() to see if a new connection
must be accepted, an ESTABLISHED socket schedules any pending data for writing, schedule a read()
callback, etc.
Note that ns3::NscTcpSocketImpl does not interact with NSC TCP directly: instead, data is redirected to
NSC. NSC TCP calls the NSC TCP sockets of a node when its wakeup() callback is invoked by NSC.
Limitations
• NSC only works on single-interface nodes; attempting to run it on a multi-interface node will cause a
program error.
• Cygwin and OS X PPC are not supported; OS X Intel is not supported but may work
• The non-Linux stacks of NSC are not supported in ns-3
• Not all socket API callbacks are supported
For more information, see this wiki page.
UDP model in ns-3
This chapter describes the UDP model available in ns-3.
Generic support for UDP
ns-3 supports a native implementation of UDP. It provides a connectionless, unreliable datagram packet
service. Packets may be reordered or duplicated before they arrive. UDP calculates and checks checksums
to catch transmission errors.
This implementation inherits from a few common header classes in the src/network directory, so that user
code can swap out implementations with minimal changes to the scripts.
Here are the important abstract base classes:
• class UdpSocket: This is defined in: src/internet/model/udp-socket.{cc,h} This is an abstract base
class of all UDP sockets. This class exists solely for hosting UdpSocket attributes that can be reused
across different implementations, and for declaring UDP-specific multicast API.
• class UdpSocketImpl: This class subclasses UdpSocket, and provides a socket interface to ns-3’s
implementation of UDP.
• class UdpSocketFactory: This is used by the layer-4 protocol instance to create UDP sockets.
• class UdpSocketFactoryImpl: This class is derived from SocketFactory and implements the API for
creating UDP sockets.
• class UdpHeader: This class contains fields corresponding to those in a network UDP header (port
numbers, payload size, checksum) as well as methods for serialization to and deserialization from a
byte buffer.
• class UdpL4Protocol: This is a subclass of IpL4Protocol and provides an implementation of the UDP
protocol.
ns-3 UDP
This is an implementation of the User Datagram Protocol described in RFC 768. UDP uses a simple
connectionless communication model with a minimum of protocol mechanism. The implementation provides
checksums for data integrity, and port numbers for addressing different functions at the source and
destination of the datagram. It has no handshaking dialogues, and thus exposes the user’s data to any
unreliability of the underlying network. There is no guarantee of data delivery, ordering, or duplicate
protection.
Usage
In many cases, usage of UDP is set at the application layer by telling the ns-3 application which kind of
socket factory to use.
Using the helper functions defined in src/applications/helper, here is how one would create a UDP
receiver:
// Create a packet sink on the receiver
uint16_t port = 50000;
Address sinkLocalAddress(InetSocketAddress (Ipv4Address::GetAny (), port));
PacketSinkHelper sinkHelper ("ns3::UdpSocketFactory", sinkLocalAddress);
ApplicationContainer sinkApp = sinkHelper.Install (serverNode);
sinkApp.Start (Seconds (1.0));
sinkApp.Stop (Seconds (10.0));
Similarly, the below snippet configures OnOffApplication traffic source to use UDP:
// Create the OnOff applications to send data to the UDP receiver
OnOffHelper clientHelper ("ns3::UdpSocketFactory", Address ());
clientHelper.SetAttribute ("Remote", remoteAddress);
ApplicationContainer clientApps = (clientHelper.Install (clientNode);
clientApps.Start (Seconds (2.0));
clientApps.Stop (Seconds (9.0));
For users who wish to have a pointer to the actual socket (so that socket operations like Bind(), setting
socket options, etc. can be done on a per-socket basis), UDP sockets can be created by using the
Socket::CreateSocket() method as given below:
Ptr<Node> node = CreateObject<Node> ();
InternetStackHelper internet;
internet.Install (node);
Ptr<SocketFactory> socketFactory = node->GetObject<UdpSocketFactory> ();
Ptr<Socket> socket = socketFactory->CreateSocket ();
socket->Bind (InetSocketAddress (Ipv4Address::GetAny (), 80));
Once a UDP socket is created, we do not need an explicit connection setup before sending and receiving
data. Being a connectionless protocol, all we need to do is to create a socket and bind it to a known
port. For a client, simply create a socket and start sending data. The Bind() call allows an application
to specify a port number and an address on the local machine. It allocates a local IPv4 endpoint for this
socket.
At the end of data transmission, the socket is closed using the Socket::Close(). It returns a 0 on
success and -1 on failure.
Please note that applications usually create the sockets automatically. Please refer to the source code
of your preferred application to discover how and when it creates the socket.
UDP Socket interaction and interface with Application layer
The following is the description of the public interface of the UDP socket, and how the interface is used
to interact with the socket itself.
Socket APIs for UDP connections:
Connect()
This is called when Send() is used instead of SendTo() by the user. It sets the address of the
remote endpoint which is used by Send(). If the remote address is valid, this method makes a
callback to ConnectionSucceeded.
Bind() Bind the socket to an address, or to a general endpoint. A general endpoint is an endpoint with an
ephemeral port allocation (that is, a random port allocation) on the 0.0.0.0 IP address. For
instance, in current applications, data senders usually bind automatically after a Connect() over
a random port. Consequently, the connection will start from this random port towards the
well-defined port of the receiver. The IP 0.0.0.0 is then translated by lower layers into the real
IP of the device.
Bind6()
Same as Bind(), but for IPv6.
BindToNetDevice()
Bind the socket to the specified NetDevice. If set on a socket, this option will force packets to
leave the bound device regardless of the device that IP routing would naturally choose. In the
receive direction, only packets received from the bound interface will be delivered.
ShutdownSend()
Signals the termination of send, or in other words, prevents data from being added to the buffer.
Recv() Grabs data from the UDP socket and forwards it to the application layer. If no data is present
(i.e. m_deliveryQueue.empty() returns 0), an empty packet is returned.
RecvFrom()
Same as Recv(), but with the source address as parameter.
SendTo()
The SendTo() API is the UDP counterpart of the TCP API Send(). It additionally specifies the
address to which the message is to be sent because no prior connection is established in UDP
communication. It returns the number of bytes sent or -1 in case of failure.
Close()
The close API closes a socket and terminates the connection. This results in freeing all the data
structures previously allocated.
----
Public callbacks
These callbacks are called by the UDP socket to notify the application of interesting events. We will
refer to these with the protected name used in socket.h, but we will provide the API function to set the
pointers to these callback as well.
NotifyConnectionSucceeded: SetConnectCallback, 1st argument
Called when the Connect() succeeds and the remote address is validated.
NotifyConnectionFailed: SetConnectCallback, 2nd argument
Called in Connect() when the the remote address validation fails.
NotifyDataSent: SetDataSentCallback
The socket notifies the application that some bytes have been transmitted at the IP layer. These
bytes could still be lost in the node (traffic control layer) or in the network.
NotifySend: SetSendCallback
Invoked to get the space available in the tx buffer when a packet (that carries data) is sent.
NotifyDataRecv: SetRecvCallback
Called when the socket receives a packet (that carries data) in the receiver buffer.
Validation
The following test cases have been provided for UDP implementation in the src/internet/test/udp-test.cc
file.
• UdpSocketImplTest: Checks data received via UDP Socket over IPv4.
• UdpSocketLoopbackTest: Checks data received via UDP Socket Loopback over IPv4.
• Udp6SocketImplTest : Checks data received via UDP Socket over IPv6.
• Udp6SocketLoopbackTest : Checks data received via UDP Socket Loopback over IPv6 Test.
Limitations
• UDP_CORK is presently not the part of this implementation.
• NotifyNormalClose, NotifyErrorClose, NotifyConnectionRequest and NotifyNewConnectionCreated socket API
callbacks are not supported.
Internet Applications Module Documentation
The goal of this module is to hold all the Internet-specific applications, and most notably some very
specific applications (e.g., ping) or daemons (e.g., radvd). Other non-Internet-specific applications
such as packet generators are contained in other modules.
The source code for the new module lives in the directory src/internet-apps.
Each application has its own goals, limitations and scope, which are briefly explained in the following.
All the applications are extensively used in the top-level examples directories. The users are encouraged
to check the scripts therein to have a clear overview of the various options and usage tricks.
V4Ping
This app mimics a “ping” (ICMP Echo) using IPv4. The application allows the following attributes to be
set:
• Remote address
• Verbose mode
• Packet size (default 56 bytes)
• Packet interval (default 1 second)
Moreover, the user can access the measured RTT value (as a Traced Source).
Ping6
This app mimics a “ping” (ICMP Echo) using IPv6. The application allows the following attributes to be
set:
• Remote address
• Local address (sender address)
• Packet size (default 56 bytes)
• Packet interval (default 1 second)
• Max number of packets to send
Radvd
This app mimics a “RADVD” daemon. I.e., the daemon responsible for IPv6 routers advertisements. All the
IPv6 routers should have a RADVD daemon installed.
The configuration of the Radvd application mimics the one of the radvd Linux program.
DHCPv4
The ns-3 implementation of Dynamic Host Configuration Protocol (DHCP) follows the specifications of RFC
2131 and RFC 2132.
The source code for DHCP is located in src/internet-apps/model and consists of the following 6 files:
• dhcp-server.h,
• dhcp-server.cc,
• dhcp-client.h,
• dhcp-client.cc,
• dhcp-header.h and
• dhcp-header.cc
Helpers
The following two files have been added to src/internet-apps/helper for DHCP:
• dhcp-helper.h and
• dhcp-helper.cc
Tests
The tests for DHCP can be found at src/internet-apps/test/dhcp-test.cc
Examples
The examples for DHCP can be found at src/internet-apps/examples/dhcp-example.cc
Scope and Limitations
The server should be provided with a network address, mask and a range of address for the pool. One
client application can be installed on only one netdevice in a node, and can configure address for only
that netdevice.
The following five basic DHCP messages are supported:
• DHCP DISCOVER
• DHCP OFFER
• DHCP REQUEST
• DHCP ACK
• DHCP NACK
Also, the following eight options of BootP are supported:
• 1 (Mask)
• 50 (Requested Address)
• 51 (Address Lease Time)
• 53 (DHCP message type)
• 54 (DHCP server identifier)
• 58 (Address renew time)
• 59 (Address rebind time)
• 255 (end)
The client identifier option (61) can be implemented in near future.
In the current implementation, a DHCP client can obtain IPv4 address dynamically from the DHCP server,
and can renew it within a lease time period.
Multiple DHCP servers can be configured, but the implementation does not support the use of a DHCP Relay
yet. PageBreak
LOW-RATE WIRELESS PERSONAL AREA NETWORK (LR-WPAN)
This chapter describes the implementation of ns-3 models for the low-rate, wireless personal area network
(LR-WPAN) as specified by IEEE standard 802.15.4 (2006).
Model Description
The source code for the lr-wpan module lives in the directory src/lr-wpan.
Design
The model design closely follows the standard from an architectural standpoint.
[image] Architecture and scope of lr-wpan models.UNINDENT
The grey areas in the figure (adapted from Fig 3. of IEEE Std. 802.15.4-2006) show the scope of the
model.
The Spectrum NetDevice from Nicola Baldo is the basis for the implementation.
The implementation also plans to borrow from the ns-2 models developed by Zheng and Lee in the future.
APIs
The APIs closely follow the standard, adapted for ns-3 naming conventions and idioms. The APIs are
organized around the concept of service primitives as shown in the following figure adapted from Figure
14 of IEEE Std. 802.15.4-2006.
[image] Service primitives.UNINDENT
The APIs are organized around four conceptual services and service access points (SAP):
• MAC data service (MCPS)
• MAC management service (MLME)
• PHY data service (PD)
• PHY management service (PLME)
In general, primitives are standardized as follows (e.g. Sec 7.1.1.1.1 of IEEE 802.15.4-2006)::
MCPS-DATA.request (
SrcAddrMode,
DstAddrMode,
DstPANId,
DstAddr,
msduLength,
msdu,
msduHandle,
TxOptions,
SecurityLevel,
KeyIdMode,
KeySource,
KeyIndex
)
This maps to ns-3 classes and methods such as::
struct McpsDataRequestParameters
{
uint8_t m_srcAddrMode;
uint8_t m_dstAddrMode;
...
};
void
LrWpanMac::McpsDataRequest (McpsDataRequestParameters params)
{
...
}
MAC
The MAC at present implements the unslotted CSMA/CA variant, without beaconing. Currently there is no
support for coordinators and the relevant APIs.
The implemented MAC is similar to Contiki’s NullMAC, i.e., a MAC without sleep features. The radio is
assumed to be always active (receiving or transmitting), of completely shut down. Frame reception is not
disabled while performing the CCA.
The main API supported is the data transfer API (McpsDataRequest/Indication/Confirm). CSMA/CA according
to Stc 802.15.4-2006, section 7.5.1.4 is supported. Frame reception and rejection according to Std
802.15.4-2006, section 7.5.6.2 is supported, including acknowledgements. Only short addressing
completely implemented. Various trace sources are supported, and trace sources can be hooked to sinks.
PHY
The physical layer components consist of a Phy model, an error rate model, and a loss model. The error
rate model presently models the error rate for IEEE 802.15.4 2.4 GHz AWGN channel for OQPSK; the model
description can be found in IEEE Std 802.15.4-2006, section E.4.1.7. The Phy model is based on
SpectrumPhy and it follows specification described in section 6 of IEEE Std 802.15.4-2006. It models PHY
service specifications, PPDU formats, PHY constants and PIB attributes. It currently only supports the
transmit power spectral density mask specified in 2.4 GHz per section 6.5.3.1. The noise power density
assumes uniformly distributed thermal noise across the frequency bands. The loss model can fully utilize
all existing simple (non-spectrum phy) loss models. The Phy model uses the existing single spectrum
channel model. The physical layer is modeled on packet level, that is, no preamble/SFD detection is
done. Packet reception will be started with the first bit of the preamble (which is not modeled), if the
SNR is more than -5 dB, see IEEE Std 802.15.4-2006, appendix E, Figure E.2. Reception of the packet will
finish after the packet was completely transmitted. Other packets arriving during reception will add up
to the interference/noise.
Currently the receiver sensitivity is set to a fixed value of -106.58 dBm. This corresponds to a packet
error rate of 1% for 20 byte reference packets for this signal power, according to IEEE Std
802.15.4-2006, section 6.1.7. In the future we will provide support for changing the sensitivity to
different values.
[image] Packet error rate vs. signal power.UNINDENT
NetDevice
Although it is expected that other technology profiles (such as 6LoWPAN and ZigBee) will write their own
NetDevice classes, a basic LrWpanNetDevice is provided, which encapsulates the common operations of
creating a generic LrWpan device and hooking things together.
MAC addresses
Contrary to other technologies, a IEEE 802.15.4 has 2 different kind of addresses:
• Long addresses (64 bits)
• Short addresses (16 bits)
The 64-bit addresses are unique worldwide, and set by the device vendor (in a real device). The 16-bit
addresses are not guaranteed to be unique, and they are typically either assigned during the devices
deployment, or assigned dynamically during the device bootstrap.
In ns-3 the device bootstrap is not (yet) present. Hence, both addresses are set when the device is
created.
The other relavant “address” to consider is the PanId (16 bits), which represents the PAN the device is
attached to.
Due to the limited number of available bytes in a packet, IEEE 802.15.4 tries to use short addresses
instead of long addresses, even though the two might be used at the same time.
For the sake of communicating with the upper layers, and in particular to generate auto-configured IPv6
addresses, each NetDevice must identify itself with a MAC address. The MAC addresses are also used during
packet reception, so it is important to use them consistently.
Focusing on IPv6 Stateless address autoconfiguration (SLAAC), there are two relevant RFCs to consider:
RFC 4944 and RFC 6282, and the two differ on how to build the IPv6 address given the NetDevice address.
RFC 4944 mandates that the IID part of the IPv6 address is calculated as YYYY:00ff:fe00:XXXX, while RFC
6282 mandates that the IID part of the IPv6 address is calculated as 0000:00ff:fe00:XXXX where XXXX is
the device short address, and YYYY is the PanId. In both cases the U/L bit must be set to local, so in
the RFC 4944 the PanId might have one bit flipped.
In order to facilitate interoperability, and to avoid unwanted module dependencies, the ns-3
implementation moves the IID calculation in the LrWpanNetDevice::GetAddress (), which will return an
Address formatted properly, i.e.:
• The Long address (a Mac64Address) if the Short address has not been set, or
• A properly formatted 48-bit pseudo-address (a Mac48Address) if the short address has been set.
The 48-bit pseudo-address is generated according to either RFC 4944 or RFC 6282 depending on the
configuration of an Attribute (PseudoMacAddressMode).
The default is to use RFC 6282 style addresses.
Note that, on reception, a packet might contain either a short or a long address. This is reflected in
the upper-layer notification callback, which can contain either the pseudo-address (48 bits) or the long
address (64 bit) of the sender.
Note also that RFC 4944 or RFC 6282 are the RFCs defining the IPv6 address compression formats (HC1 and
IPHC respectively). It is defintely not a good idea to either mix devices using different pseudo-address
format or compression types in the same network. This point is further discussed in the sixlowpan module
documentation.
Scope and Limitations
Future versions of this document will contain a PICS proforma similar to Appendix D of IEEE
802.15.4-2006. The current emphasis is on the unslotted mode of 802.15.4 operation for use in Zigbee,
and the scope is limited to enabling a single mode (CSMA/CA) with basic data transfer capabilities.
Association with PAN coordinators is not yet supported, nor the use of extended addressing. Interference
is modeled as AWGN but this is currently not thoroughly tested.
The NetDevice Tx queue is not limited, i.e., packets are never dropped due to queue becoming full. They
may be dropped due to excessive transmission retries or channel access failure.
References
• Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless
Personal Area Networks (WPANs), IEEE Computer Society, IEEE Std 802.15.4-2006, 8 September 2006.
•
J. Zheng and Myung J. Lee, “A comprehensive performance study of IEEE 802.15.4,” Sensor Network
Operations, IEEE Press, Wiley Interscience, Chapter 4, pp. 218-237, 2006.
Usage
Enabling lr-wpan
Add lr-wpan to the list of modules built with ns-3.
Helper
The helper is patterned after other device helpers. In particular, tracing (ascii and pcap) is enabled
similarly, and enabling of all lr-wpan log components is performed similarly. Use of the helper is
exemplified in examples/lr-wpan-data.cc. For ascii tracing, the transmit and receive traces are hooked
at the Mac layer.
The default propagation loss model added to the channel, when this helper is used, is the
LogDistancePropagationLossModel with default parameters.
Examples
The following examples have been written, which can be found in src/lr-wpan/examples/:
• lr-wpan-data.cc: A simple example showing end-to-end data transfer.
• lr-wpan-error-distance-plot.cc: An example to plot variations of the packet success ratio as a
function of distance.
• lr-wpan-error-model-plot.cc: An example to test the phy.
• lr-wpan-packet-print.cc: An example to print out the MAC header fields.
• lr-wpan-phy-test.cc: An example to test the phy.
In particular, the module enables a very simplified end-to-end data transfer scenario, implemented in
lr-wpan-data.cc. The figure shows a sequence of events that are triggered when the MAC receives a
DataRequest from the higher layer. It invokes a Clear Channel Assessment (CCA) from the PHY, and if
successful, sends the frame down to the PHY where it is transmitted over the channel and results in a
DataIndication on the peer node.
[image] Data example for simple LR-WPAN data transfer end-to-end.UNINDENT
The example lr-wpan-error-distance-plot.cc plots the packet success ratio (PSR) as a function of
distance, using the default LogDistance propagation loss model and the 802.15.4 error model. The
channel (default 11), packet size (default 20 bytes) and transmit power (default 0 dBm) can be varied
by command line arguments. The program outputs a file named 802.15.4-psr-distance.plt. Loading this
file into gnuplot yields a file 802.15.4-psr-distance.eps, which can be converted to pdf or other
formats. The default output is shown below.
[image] Default output of the program lr-wpan-error-distance-plot.cc.UNINDENT
Tests
The following tests have been written, which can be found in src/lr-wpan/tests/:
• lr-wpan-ack-test.cc: Check that acknowledgments are being used and issued in the correct order.
• lr-wpan-collision-test.cc: Test correct reception of packets with interference and collisions.
• lr-wpan-error-model-test.cc: Check that the error model gives predictable values.
• lr-wpan-packet-test.cc: Test the 802.15.4 MAC header/trailer classes
• lr-wpan-pd-plme-sap-test.cc: Test the PLME and PD SAP per IEEE 802.15.4
• lr-wpan-spectrum-value-helper-test.cc: Test that the conversion between power (expressed as a scalar
quantity) and spectral power, and back again, falls within a 25% tolerance across the range of possible
channels and input powers.
Validation
The model has not been validated against real hardware. The error model has been validated against the
data in IEEE Std 802.15.4-2006, section E.4.1.7 (Figure E.2). The MAC behavior (CSMA backoff) has been
validated by hand against expected behavior. The below plot is an example of the error model validation
and can be reproduced by running lr-wpan-error-model-plot.cc:
[image] Default output of the program lr-wpan-error-model-plot.cc.UNINDENT
LTE MODULE
Design Documentation
Overview
An overview of the LTE-EPC simulation model is depicted in the figure Overview of the LTE-EPC simulation
model. There are two main components:
• the LTE Model. This model includes the LTE Radio Protocol stack (RRC, PDCP, RLC, MAC, PHY). These
entities reside entirely within the UE and the eNB nodes.
• the EPC Model. This model includes core network interfaces, protocols and entities. These entities
and protocols reside within the SGW, PGW and MME nodes, and partially within the eNB nodes.
[image] Overview of the LTE-EPC simulation model.UNINDENT
Design Criteria
LTE Model
The LTE model has been designed to support the evaluation of the following aspects of LTE systems:
• Radio Resource Management
• QoS-aware Packet Scheduling
• Inter-cell Interference Coordination
• Dynamic Spectrum Access
In order to model LTE systems to a level of detail that is sufficient to allow a correct evaluation of
the above mentioned aspects, the following requirements have been considered:
1. At the radio level, the granularity of the model should be at least that of the Resource Block
(RB). In fact, this is the fundamental unit being used for resource allocation. Without this
minimum level of granularity, it is not possible to model accurately packet scheduling and
inter-cell-interference. The reason is that, since packet scheduling is done on a per-RB basis, an
eNB might transmit on a subset only of all the available RBs, hence interfering with other eNBs
only on those RBs where it is transmitting. Note that this requirement rules out the adoption of a
system level simulation approach, which evaluates resource allocation only at the granularity of
call/bearer establishment.
2. The simulator should scale up to tens of eNBs and hundreds of User Equipment (UEs). This rules out
the use of a link level simulator, i.e., a simulator whose radio interface is modeled with a
granularity up to the symbol level. This is because to have a symbol level model it is necessary to
implement all the PHY layer signal processing, whose huge computational complexity severely limits
simulation. In fact, link-level simulators are normally limited to a single eNB and one or a few
UEs.
3. It should be possible within the simulation to configure different cells so that they use different
carrier frequencies and system bandwidths. The bandwidth used by different cells should be allowed
to overlap, in order to support dynamic spectrum licensing solutions such as those described in
[Ofcom2600MHz] and [RealWireless]. The calculation of interference should handle appropriately this
case.
4. To be more representative of the LTE standard, as well as to be as close as possible to real-world
implementations, the simulator should support the MAC Scheduler API published by the FemtoForum
[FFAPI]. This interface is expected to be used by femtocell manufacturers for the implementation of
scheduling and Radio Resource Management (RRM) algorithms. By introducing support for this
interface in the simulator, we make it possible for LTE equipment vendors and operators to test in
a simulative environment exactly the same algorithms that would be deployed in a real system.
5. The LTE simulation model should contain its own implementation of the API defined in [FFAPI].
Neither binary nor data structure compatibility with vendor-specific implementations of the same
interface are expected; hence, a compatibility layer should be interposed whenever a
vendor-specific MAC scheduler is to be used with the simulator. This requirement is necessary to
allow the simulator to be independent from vendor-specific implementations of this interface
specification. We note that [FFAPI] is a logical specification only, and its implementation (e.g.,
translation to some specific programming language) is left to the vendors.
6. The model is to be used to simulate the transmission of IP packets by the upper layers. With this
respect, it shall be considered that in LTE the Scheduling and Radio Resource Management do not
work with IP packets directly, but rather with RLC PDUs, which are obtained by segmentation and
concatenation of IP packets done by the RLC entities. Hence, these functionalities of the RLC layer
should be modeled accurately.
EPC Model
The main objective of the EPC model is to provides means for the simulation of end-to-end IP connectivity
over the LTE model. To this aim, it supports for the interconnection of multiple UEs to the Internet,
via a radio access network of multiple eNBs connected to the core network, as shown in Figure Overview of
the LTE-EPC simulation model.
The following design choices have been made for the EPC model:
1. The Packet Data Network (PDN) type supported is both IPv4 and IPv6. In other words, the
end-to-end connections between the UEs and the remote hosts can be IPv4 and IPv6. However, the
networks between the core network elements (MME, SGWs and PGWs) are IPv4-only.
2. The SGW and PGW functional entities are implemented in different nodes, which are hence referred
to as the SGW node and PGW node, respectively.
3. The MME functional entities is implemented as a network node, which is hence referred to as the
MME node.
4. The scenarios with inter-SGW mobility are not of interest. But several SGW nodes may be present in
simulations scenarios.
5. A requirement for the EPC model is that it can be used to simulate the end-to-end performance of
realistic applications. Hence, it should be possible to use with the EPC model any regular ns-3
application working on top of TCP or UDP.
6. Another requirement is the possibility of simulating network topologies with the presence of
multiple eNBs, some of which might be equipped with a backhaul connection with limited
capabilities. In order to simulate such scenarios, the user data plane protocols being used
between the eNBs and the SGW should be modeled accurately.
7. It should be possible for a single UE to use different applications with different QoS profiles.
Hence, multiple EPS bearers should be supported for each UE. This includes the necessary
classification of TCP/UDP traffic over IP done at the UE in the uplink and at the PGW in the
downlink.
8. The initial focus of the EPC model is mainly on the EPC data plane. The accurate modeling of the
EPC control plane is, for the time being, not a requirement; however, the necessary control plane
interactions among the different network nodes of the core network are realized by implementing
control protocols/messages among them. Direct interaction among the different simulation objects
via the provided helper objects should be avoided as much as possible.
9. The focus of the EPC model is on simulations of active users in ECM connected mode. Hence, all the
functionality that is only relevant for ECM idle mode (in particular, tracking area update and
paging) are not modeled at all.
10. The model should allow the possibility to perform an X2-based handover between two eNBs.
Architecture
LTE Model
UE architecture
The architecture of the LTE radio protocol stack model of the UE is represented in the figures LTE radio
protocol stack architecture for the UE on the data plane and LTE radio protocol stack architecture for
the UE on the control plane which highlight respectively the data plane and the control plane.
[image] LTE radio protocol stack architecture for the UE on the data plane.UNINDENT
[image] LTE radio protocol stack architecture for the UE on the control plane.UNINDENT
The architecture of the PHY/channel model of the UE is represented in figure PHY and channel model
architecture for the UE.
[image] PHY and channel model architecture for the UE.UNINDENT
eNB architecture
The architecture of the LTE radio protocol stack model of the eNB is represented in the figures LTE radio
protocol stack architecture for the eNB on the data plane and LTE radio protocol stack architecture for
the eNB on the control plane which highlight respectively the data plane and the control plane.
[image] LTE radio protocol stack architecture for the eNB on the data plane.UNINDENT
[image] LTE radio protocol stack architecture for the eNB on the control plane.UNINDENT
The architecture of the PHY/channel model of the eNB is represented in figure PHY and channel model
architecture for the eNB.
[image] PHY and channel model architecture for the eNB.UNINDENT
EPC Model
EPC data plane
In Figure LTE-EPC data plane protocol stack, we represent the end-to-end LTE-EPC data plane protocol
stack as it is modeled in the simulator. The figure shows all nodes in the data path, i.e. UE, eNB, SGW,
PGW and a remote host in the Internet. All protocol stacks (S5 protocol stack, S1-U protocol stack and
the LTE radio protocol stack) specified by 3GPP are present.
[image] LTE-EPC data plane protocol stack.UNINDENT
EPC control plane
The architecture of the implementation of the control plane model is shown in figure LTE-EPC control
plane protocol stack. The control interfaces that are modeled explicitly are the S1-MME, the S11, and
the S5 interfaces. The X2 interface is also modeled explicitly and it is described in more detail in
section X2
The S1-MME, the S11 and the S5 interfaces are modeled using procotol data units sent over its respective
links. These interfaces use the SCTP protocol as transport protocol but currently, the SCTP protocol is
not modeled in the ns-3 simulator, so the UDP protocol is used instead of the SCTP protocol.
[image] LTE-EPC control plane protocol stack.UNINDENT
Channel and Propagation
For channel modeling purposes, the LTE module uses the SpectrumChannel interface provided by the spectrum
module. At the time of this writing, two implementations of such interface are available:
SingleModelSpectrumChannel and MultiModelSpectrumChannel, and the LTE module requires the use of the
MultiModelSpectrumChannel in order to work properly. This is because of the need to support different
frequency and bandwidth configurations. All the propagation models supported by MultiModelSpectrumChannel
can be used within the LTE module.
Use of the Buildings model with LTE
The recommended propagation model to be used with the LTE module is the one provided by the Buildings
module, which was in fact designed specifically with LTE (though it can be used with other wireless
technologies as well). Please refer to the documentation of the Buildings module for generic information
on the propagation model it provides.
In this section we will highlight some considerations that specifically apply when the Buildings module
is used together with the LTE module.
The naming convention used in the following will be:
• User equipment: UE
• Macro Base Station: MBS
• Small cell Base Station (e.g., pico/femtocell): SC
The LTE module considers FDD only, and implements downlink and uplink propagation separately. As a
consequence, the following pathloss computations are performed
• MBS <-> UE (indoor and outdoor)
• SC (indoor and outdoor) <-> UE (indoor and outdoor)
The LTE model does not provide the following pathloss computations:
• UE <-> UE
• MBS <-> MBS
• MBS <-> SC
• SC <-> SC
The Buildings model does not know the actual type of the node; i.e., it is not aware of whether a
transmitter node is a UE, a MBS, or a SC. Rather, the Buildings model only cares about the position of
the node: whether it is indoor and outdoor, and what is its z-axis respect to the rooftop level. As a
consequence, for an eNB node that is placed outdoor and at a z-coordinate above the rooftop level, the
propagation models typical of MBS will be used by the Buildings module. Conversely, for an eNB that is
placed outdoor but below the rooftop, or indoor, the propagation models typical of pico and femtocells
will be used.
For communications involving at least one indoor node, the corresponding wall penetration losses will be
calculated by the Buildings model. This covers the following use cases:
• MBS <-> indoor UE
• outdoor SC <-> indoor UE
• indoor SC <-> indoor UE
• indoor SC <-> outdoor UE
Please refer to the documentation of the Buildings module for details on the actual models used in each
case.
Fading Model
The LTE module includes a trace-based fading model derived from the one developed during the GSoC 2010
[Piro2011]. The main characteristic of this model is the fact that the fading evaluation during
simulation run-time is based on per-calculated traces. This is done to limit the computational complexity
of the simulator. On the other hand, it needs huge structures for storing the traces; therefore, a
trade-off between the number of possible parameters and the memory occupancy has to be found. The most
important ones are:
• users’ speed: relative speed between users (affects the Doppler frequency, which in turns affects
the time-variance property of the fading)
• number of taps (and relative power): number of multiple paths considered, which affects the
frequency property of the fading.
• time granularity of the trace: sampling time of the trace.
• frequency granularity of the trace: number of values in frequency to be evaluated.
• length of trace: ideally large as the simulation time, might be reduced by windowing mechanism.
• number of users: number of independent traces to be used (ideally one trace per user).
With respect to the mathematical channel propagation model, we suggest the one provided by the
rayleighchan function of Matlab, since it provides a well accepted channel modelization both in time and
frequency domain. For more information, the reader is referred to [mathworks].
The simulator provides a matlab script (src/lte/model/fading-traces/fading-trace-generator.m) for
generating traces based on the format used by the simulator. In detail, the channel object created with
the rayleighchan function is used for filtering a discrete-time impulse signal in order to obtain the
channel impulse response. The filtering is repeated for different TTI, thus yielding subsequent
time-correlated channel responses (one per TTI). The channel response is then processed with the pwelch
function for obtaining its power spectral density values, which are then saved in a file with the proper
format compatible with the simulator model.
Since the number of variable it is pretty high, generate traces considering all of them might produce a
high number of traces of huge size. On this matter, we considered the following assumptions of the
parameters based on the 3GPP fading propagation conditions (see Annex B.2 of [TS36104]):
• users’ speed: typically only a few discrete values are considered, i.e.:
• 0 and 3 kmph for pedestrian scenarios
• 30 and 60 kmph for vehicular scenarios
• 0, 3, 30 and 60 for urban scenarios
• channel taps: only a limited number of sets of channel taps are normally considered, for example
three models are mentioned in Annex B.2 of [TS36104].
• time granularity: we need one fading value per TTI, i.e., every 1 ms (as this is the granularity in
time of the ns-3 LTE PHY model).
• frequency granularity: we need one fading value per RB (which is the frequency granularity of the
spectrum model used by the ns-3 LTE model).
• length of the trace: the simulator includes the windowing mechanism implemented during the GSoC
2011, which consists of picking up a window of the trace each window length in a random fashion.
• per-user fading process: users share the same fading trace, but for each user a different starting
point in the trace is randomly picked up. This choice was made to avoid the need to provide one
fading trace per user.
According to the parameters we considered, the following formula express in detail the total size
S_{traces} of the fading traces:
S_{traces} = S_{sample} \times N_{RB} \times \frac{T_{trace}}{T_{sample}} \times N_{scenarios} \mbox{ [bytes]}
where S_{sample} is the size in bytes of the sample (e.g., 8 in case of double precision, 4 in case of
float precision), N_{RB} is the number of RB or set of RBs to be considered, T_{trace} is the total
length of the trace, T_{sample} is the time resolution of the trace (1 ms), and N_{scenarios} is the
number of fading scenarios that are desired (i.e., combinations of different sets of channel taps and
user speed values). We provide traces for 3 different scenarios one for each taps configuration defined
in Annex B.2 of [TS36104]:
• Pedestrian: with nodes’ speed of 3 kmph.
• Vehicular: with nodes’ speed of 60 kmph.
• Urban: with nodes’ speed of 3 kmph.
hence N_{scenarios} = 3. All traces have T_{trace} = 10 s and RB_{NUM} = 100. This results in a total 24
MB bytes of traces.
Antennas
Being based on the SpectrumPhy, the LTE PHY model supports antenna modeling via the ns-3 AntennaModel
class. Hence, any model based on this class can be associated with any eNB or UE instance. For instance,
the use of the CosineAntennaModel associated with an eNB device allows to model one sector of a macro
base station. By default, the IsotropicAntennaModel is used for both eNBs and UEs.
PHY
Overview
The physical layer model provided in this LTE simulator is based on the one described in [Piro2011], with
the following modifications. The model now includes the inter cell interference calculation and the
simulation of uplink traffic, including both packet transmission and CQI generation.
Subframe Structure
The subframe is divided into control and data part as described in Figure LTE subframe division..
[image] LTE subframe division..UNINDENT
Considering the granularity of the simulator based on RB, the control and the reference signaling have
to be consequently modeled considering this constraint. According to the standard [TS36211], the
downlink control frame starts at the beginning of each subframe and lasts up to three symbols across
the whole system bandwidth, where the actual duration is provided by the Physical Control Format
Indicator Channel (PCFICH). The information on the allocation are then mapped in the remaining resource
up to the duration defined by the PCFICH, in the so called Physical Downlink Control Channel (PDCCH). A
PDCCH transports a single message called Downlink Control Information (DCI) coming from the MAC layer,
where the scheduler indicates the resource allocation for a specific user. The PCFICH and PDCCH are
modeled with the transmission of the control frame of a fixed duration of 3/14 of milliseconds spanning
in the whole available bandwidth, since the scheduler does not estimate the size of the control region.
This implies that a single transmission block models the entire control frame with a fixed power (i.e.,
the one used for the PDSCH) across all the available RBs. According to this feature, this transmission
represents also a valuable support for the Reference Signal (RS). This allows of having every TTI an
evaluation of the interference scenario since all the eNB are transmitting (simultaneously) the control
frame over the respective available bandwidths. We note that, the model does not include the power
boosting since it does not reflect any improvement in the implemented model of the channel estimation.
The Sounding Reference Signal (SRS) is modeled similar to the downlink control frame. The SRS is
periodically placed in the last symbol of the subframe in the whole system bandwidth. The RRC module
already includes an algorithm for dynamically assigning the periodicity as function of the actual
number of UEs attached to a eNB according to the UE-specific procedure (see Section 8.2 of [TS36213]).
MAC to Channel delay
To model the latency of real MAC and PHY implementations, the PHY model simulates a MAC-to-channel delay
in multiples of TTIs (1ms). The transmission of both data and control packets are delayed by this amount.
CQI feedback
The generation of CQI feedback is done accordingly to what specified in [FFAPI]. In detail, we considered
the generation of periodic wideband CQI (i.e., a single value of channel state that is deemed
representative of all RBs in use) and inband CQIs (i.e., a set of value representing the channel state
for each RB).
The CQI index to be reported is obtained by first obtaining a SINR measurement and then passing this SINR
measurement to the Adaptive Modulation and Coding module which will map it to the CQI index.
In downlink, the SINR used to generate CQI feedback can be calculated in two different ways:
1. Ctrl method: SINR is calculated combining the signal power from the reference signals (which in the
simulation is equivalent to the PDCCH) and the interference power from the PDCCH. This approach
results in considering any neighboring eNB as an interferer, regardless of whether this eNB is
actually performing any PDSCH transmission, and regardless of the power and RBs used for eventual
interfering PDSCH transmissions.
2. Mixed method: SINR is calculated combining the signal power from the reference signals (which in
the simulation is equivalent to the PDCCH) and the interference power from the PDSCH. This approach
results in considering as interferers only those neighboring eNBs that are actively transmitting
data on the PDSCH, and allows to generate inband CQIs that account for different amounts of
interference on different RBs according to the actual interference level. In the case that no PDSCH
transmission is performed by any eNB, this method consider that interference is zero, i.e., the
SINR will be calculated as the ratio of signal to noise only.
To switch between this two CQI generation approaches, LteHelper::UsePdschForCqiGeneration needs to be
configured: false for first approach and true for second approach (true is default value):
Config::SetDefault ("ns3::LteHelper::UsePdschForCqiGeneration", BooleanValue (true));
In uplink, two types of CQIs are implemented:
• SRS based, periodically sent by the UEs.
• PUSCH based, calculated from the actual transmitted data.
The scheduler interface include an attribute system called UlCqiFilter for managing the filtering of the
CQIs according to their nature, in detail:
• SRS_UL_CQI for storing only SRS based CQIs.
• PUSCH_UL_CQI for storing only PUSCH based CQIs.
It has to be noted that, the FfMacScheduler provides only the interface and it is matter of the actual
scheduler implementation to include the code for managing these attributes (see scheduler related section
for more information on this matter).
Interference Model
The PHY model is based on the well-known Gaussian interference models, according to which the powers of
interfering signals (in linear units) are summed up together to determine the overall interference power.
The sequence diagram of Figure Sequence diagram of the PHY interference calculation procedure shows how
interfering signals are processed to calculate the SINR, and how SINR is then used for the generation of
CQI feedback.
[image] Sequence diagram of the PHY interference calculation procedure.UNINDENT
LTE Spectrum Model
The usage of the radio spectrum by eNBs and UEs in LTE is described in [TS36101]. In the simulator, radio
spectrum usage is modeled as follows. Let f_c denote the LTE Absolute Radio Frequency Channel Number,
which identifies the carrier frequency on a 100 kHz raster; furthermore, let B be the Transmission
Bandwidth Configuration in number of Resource Blocks. For every pair (f_c,B) used in the simulation we
define a corresponding SpectrumModel using the functionality provided by the sec-spectrum-module . model
using the Spectrum framework described in [Baldo2009]. f_c and B can be configured for every eNB
instantiated in the simulation; hence, each eNB can use a different spectrum model. Every UE will
automatically use the spectrum model of the eNB it is attached to. Using the MultiModelSpectrumChannel
described in [Baldo2009], the interference among eNBs that use different spectrum models is properly
accounted for. This allows to simulate dynamic spectrum access policies, such as for example the
spectrum licensing policies that are discussed in [Ofcom2600MHz].
Data PHY Error Model
The simulator includes an error model of the data plane (i.e., PDSCH and PUSCH) according to the standard
link-to-system mapping (LSM) techniques. The choice is aligned with the standard system simulation
methodology of OFDMA radio transmission technology. Thanks to LSM we are able to maintain a good level
of accuracy and at the same time limiting the computational complexity increase. It is based on the
mapping of single link layer performance obtained by means of link level simulators to system (in our
case network) simulators. In particular link the layer simulator is used for generating the performance
of a single link from a PHY layer perspective, usually in terms of code block error rate (BLER), under
specific static conditions. LSM allows the usage of these parameters in more complex scenarios, typical
of system/network simulators, where we have more links, interference and “colored” channel propagation
phenomena (e.g., frequency selective fading).
To do this the Vienna LTE Simulator [ViennaLteSim] has been used for what concerns the extraction of link
layer performance and the Mutual Information Based Effective SINR (MIESM) as LSM mapping function using
part of the work recently published by the Signet Group of University of Padua [PaduaPEM].
MIESM
The specific LSM method adopted is the one based on the usage of a mutual information metric, commonly
referred to as the mutual information per per coded bit (MIB or MMIB when a mean of multiples MIBs is
involved). Another option would be represented by the Exponential ESM (EESM); however, recent studies
demonstrate that MIESM outperforms EESM in terms of accuracy [LozanoCost].
[image] MIESM computational procedure diagram.UNINDENT
The mutual information (MI) is dependent on the constellation mapping and can be calculated per
transport block (TB) basis, by evaluating the MI over the symbols and the subcarrier. However, this
would be too complex for a network simulator. Hence, in our implementation a flat channel response
within the RB has been considered; therefore the overall MI of a TB is calculated averaging the MI
evaluated per each RB used in the TB. In detail, the implemented scheme is depicted in Figure MIESM
computational procedure diagram, where we see that the model starts by evaluating the MI value for each
RB, represented in the figure by the SINR samples. Then the equivalent MI is evaluated per TB basis by
averaging the MI values. Finally, a further step has to be done since the link level simulator returns
the performance of the link in terms of block error rate (BLER) in a addive white gaussian noise
(AWGN) channel, where the blocks are the code blocks (CBs) independently encoded/decoded by the turbo
encoder. On this matter the standard 3GPP segmentation scheme has been used for estimating the actual
CB size (described in section 5.1.2 of [TS36212]). This scheme divides the TB in N_{K_-} blocks of size
K_- and N_{K+} blocks of size K_+. Therefore the overall TB BLER (TBLER) can be expressed as
TBLER = 1- \prod\limits_{i=1}^{C}(1-CBLER_i)
where the CBLER_i is the BLER of the CB i obtained according to the link level simulator CB BLER
curves. For estimating the CBLER_i, the MI evaluation has been implemented according to its numerical
approximation defined in [wimaxEmd]. Moreover, for reducing the complexity of the computation, the
approximation has been converted into lookup tables. In detail, Gaussian cumulative model has been used
for approximating the AWGN BLER curves with three parameters which provides a close fit to the standard
AWGN performances, in formula:
CBLER_i = \frac{1}{2}\left[1-erf\left(\frac{x-b_{ECR}}{\sqrt{2}c_{ECR}} \right) \right]
where x is the MI of the TB, b_{ECR} represents the “transition center” and c_{ECR} is related to the
“transition width” of the Gaussian cumulative distribution for each Effective Code Rate (ECR) which is
the actual transmission rate according to the channel coding and MCS. For limiting the computational
complexity of the model we considered only a subset of the possible ECRs in fact we would have
potentially 5076 possible ECRs (i.e., 27 MCSs and 188 CB sizes). On this respect, we will limit the CB
sizes to some representative values (i.e., 40, 140, 160, 256, 512, 1024, 2048, 4032, 6144), while for
the others the worst one approximating the real one will be used (i.e., the smaller CB size value
available respect to the real one). This choice is aligned to the typical performance of turbo codes,
where the CB size is not strongly impacting on the BLER. However, it is to be notes that for CB sizes
lower than 1000 bits the effect might be relevant (i.e., till 2 dB); therefore, we adopt this
unbalanced sampling interval for having more precision where it is necessary. This behaviour is
confirmed by the figures presented in the Annes Section.
BLER Curves
On this respect, we reused part of the curves obtained within [PaduaPEM]. In detail, we introduced the CB
size dependency to the CB BLER curves with the support of the developers of [PaduaPEM] and of the LTE
Vienna Simulator. In fact, the module released provides the link layer performance only for what concerns
the MCSs (i.e, with a given fixed ECR). In detail the new error rate curves for each has been evaluated
with a simulation campaign with the link layer simulator for a single link with AWGN noise and for CB
size of 104, 140, 256, 512, 1024, 2048, 4032 and 6144. These curves has been mapped with the Gaussian
cumulative model formula presented above for obtaining the correspondents b_{ECR} and c_{ECR} parameters.
The BLER performance of all MCS obtained with the link level simulator are plotted in the following
figures (blue lines) together with their correspondent mapping to the Gaussian cumulative distribution
(red dashed lines).
[image] BLER for MCS 1, 2, 3 and 4..UNINDENT
[image] BLER for MCS 5, 6, 7 and 8..UNINDENT
[image] BLER for MCS 9, 10, 11 and 12..UNINDENT
[image] BLER for MCS 13, 14, 15 and 16..UNINDENT
[image] BLER for MCS 17, 17, 19 and 20..UNINDENT
[image] BLER for MCS 21, 22, 23 and 24..UNINDENT
[image] BLER for MCS 25, 26, 27 and 28..UNINDENT
[image] BLER for MCS 29..UNINDENT
Integration of the BLER curves in the ns-3 LTE module
The model implemented uses the curves for the LSM of the recently LTE PHY Error Model released in the ns3
community by the Signet Group [PaduaPEM] and the new ones generated for different CB sizes. The
LteSpectrumPhy class is in charge of evaluating the TB BLER thanks to the methods provided by the
LteMiErrorModel class, which is in charge of evaluating the TB BLER according to the vector of the
perceived SINR per RB, the MCS and the size in order to proper model the segmentation of the TB in CBs.
In order to obtain the vector of the perceived SINRs for data and control signals, two instances of
LteChunkProcessor (dedicated to evaluate the SINR for obtaining physical error performance) have been
attached to UE downlink and eNB uplink LteSpectrumPhy modules for evaluating the error model distribution
of PDSCH (UE side) and ULSCH (eNB side).
The model can be disabled for working with a zero-losses channel by setting the DataErrorModelEnabled
attribute of the LteSpectrumPhy class (by default is active). This can be done according to the standard
ns3 attribute system procedure, that is:
Config::SetDefault ("ns3::LteSpectrumPhy::DataErrorModelEnabled", BooleanValue (false));
Control Channels PHY Error Model
The simulator includes the error model for downlink control channels (PCFICH and PDCCH), while in uplink
it is assumed and ideal error-free channel. The model is based on the MIESM approach presented before for
considering the effects of the frequency selective channel since most of the control channels span the
whole available bandwidth.
PCFICH + PDCCH Error Model
The model adopted for the error distribution of these channels is based on an evaluation study carried
out in the RAN4 of 3GPP, where different vendors investigated the demodulation performance of the PCFICH
jointly with PDCCH. This is due to the fact that the PCFICH is the channel in charge of communicating to
the UEs the actual dimension of the PDCCH (which spans between 1 and 3 symbols); therefore the correct
decodification of the DCIs depends on the correct interpretation of both ones. In 3GPP this problem have
been evaluated for improving the cell-edge performance [FujitsuWhitePaper], where the interference among
neighboring cells can be relatively high due to signal degradation. A similar problem has been notices in
femto-cell scenario and, more in general, in HetNet scenarios the bottleneck has been detected mainly as
the PCFICH channel [Bharucha2011], where in case of many eNBs are deployed in the same service area, this
channel may collide in frequency, making impossible the correct detection of the PDCCH channel, too.
In the simulator, the SINR perceived during the reception has been estimated according to the MIESM model
presented above in order to evaluate the error distribution of PCFICH and PDCCH. In detail, the SINR
samples of all the RBs are included in the evaluation of the MI associated to the control frame and,
according to this values, the effective SINR (eSINR) is obtained by inverting the MI evaluation process.
It has to be noted that, in case of MIMO transmission, both PCFICH and the PDCCH use always the transmit
diversity mode as defined by the standard. According to the eSINR perceived the decodification error
probability can be estimated as function of the results presented in [R4-081920]. In case an error occur,
the DCIs discarded and therefore the UE will be not able to receive the correspondent Tbs, therefore
resulting lost.
MIMO Model
The use of multiple antennas both at transmitter and receiver side, known as multiple-input and
multiple-output (MIMO), is a problem well studied in literature during the past years. Most of the work
concentrate on evaluating analytically the gain that the different MIMO schemes might have in term of
capacity; however someones provide also information of the gain in terms of received power [CatreuxMIMO].
According to the considerations above, a model more flexible can be obtained considering the gain that
MIMO schemes bring in the system from a statistical point of view. As highlighted before, [CatreuxMIMO]
presents the statistical gain of several MIMO solutions respect to the SISO one in case of no correlation
between the antennas. In the work the gain is presented as the cumulative distribution function (CDF) of
the output SINR for what concern SISO, MIMO-Alamouti, MIMO-MMSE, MIMO-OSIC-MMSE and MIMO-ZF schemes.
Elaborating the results, the output SINR distribution can be approximated with a log-normal one with
different mean and variance as function of the scheme considered. However, the variances are not so
different and they are approximatively equal to the one of the SISO mode already included in the
shadowing component of the BuildingsPropagationLossModel, in detail:
• SISO: \mu = 13.5 and \sigma = 20 [dB].
• MIMO-Alamouti: \mu = 17.7 and \sigma = 11.1 [dB].
• MIMO-MMSE: \mu = 10.7 and \sigma = 16.6 [dB].
• MIMO-OSIC-MMSE: \mu = 12.6 and \sigma = 15.5 [dB].
• MIMO-ZF: \mu = 10.3 and \sigma = 12.6 [dB].
Therefore the PHY layer implements the MIMO model as the gain perceived by the receiver when using a MIMO
scheme respect to the one obtained using SISO one. We note that, these gains referred to a case where
there is no correlation between the antennas in MIMO scheme; therefore do not model degradation due to
paths correlation.
UE PHY Measurements Model
According to [TS36214], the UE has to report a set of measurements of the eNBs that the device is able to
perceive: the reference signal received power (RSRP) and the reference signal received quality (RSRQ).
The former is a measure of the received power of a specific eNB, while the latter includes also channel
interference and thermal noise. The UE has to report the measurements jointly with the physical cell
identity (PCI) of the cell. Both the RSRP and RSRQ measurements are performed during the reception of the
RS, while the PCI is obtained with the Primary Synchronization Signal (PSS). The PSS is sent by the eNB
each 5 subframes and in detail in the subframes 1 and 6. In real systems, only 504 distinct PCIs are
available, and hence it could occur that two nearby eNBs use the same PCI; however, in the simulator we
model PCIs using simulation metadata, and we allow up to 65535 distinct PCIs, thereby avoiding PCI
collisions provided that less that 65535 eNBs are simulated in the same scenario.
According to [TS36133] sections 9.1.4 and 9.1.7, RSRP is reported by PHY layer in dBm while RSRQ in dB.
The values of RSRP and RSRQ are provided to higher layers through the C-PHY SAP (by means of
UeMeasurementsParameters struct) every 200 ms as defined in [TS36331]. Layer 1 filtering is performed by
averaging the all the measurements collected during the last window slot. The periodicity of reporting
can be adjusted for research purposes by means of the LteUePhy::UeMeasurementsFilterPeriod attribute.
The formulas of the RSRP and RSRQ can be simplified considering the assumption of the PHY layer that the
channel is flat within the RB, the finest level of accuracy. In fact, this implies that all the REs
within a RB have the same power, therefore:
RSRP = \frac{\sum_{k=0}^{K-1}\frac{\sum_{m=0}^{M-1}(P(k,m))}{M}}{K}
= \frac{\sum_{k=0}^{K-1}\frac{(M \times P(k))}{M}}{K}
= \frac{\sum_{k=0}^{K-1}(P(k))}{K}
where P(k,m) represents the signal power of the RE m within the RB k, which, as observed before, is
constant within the same RB and equal to P(k), M is the number of REs carrying the RS in a RB and K is
the number of RBs. It is to be noted that P(k), and in general all the powers defined in this section, is
obtained in the simulator from the PSD of the RB (which is provided by the
LteInterferencePowerChunkProcessor), in detail:
P(k) = PSD_{RB}(k)*180000/12
where PSD_{RB}(k) is the power spectral density of the RB k, 180000 is the bandwidth in Hz of the RB and
12 is the number of REs per RB in an OFDM symbol. Similarly, for RSSI we have
RSSI = \sum_{k=0}^{K-1} \frac{\sum_{s=0}^{S-1} \sum_{r=0}^{R-1}( P(k,s,r) + I(k,s,r) + N(k,s,r))}{S}
where S is the number of OFDM symbols carrying RS in a RB and R is the number of REs carrying a RS in a
OFDM symbol (which is fixed to 2) while P(k,s,r), I(k,s,r) and N(k,s,r) represent respectively the
perceived power of the serving cell, the interference power and the noise power of the RE r in symbol s.
As for RSRP, the measurements within a RB are always equals among each others according to the PHY model;
therefore P(k,s,r) = P(k), I(k,s,r) = I(k) and N(k,s,r) = N(k), which implies that the RSSI can be
calculated as:
RSSI = \sum_{k=0}^{K-1} \frac{S \times 2 \times ( P(k) + I(k) + N(k))}{S}
= \sum_{k=0}^{K-1} 2 \times ( P(k) + I(k) + N (k))
Considering the constraints of the PHY reception chain implementation, and in order to maintain the level
of computational complexity low, only RSRP can be directly obtained for all the cells. This is due to the
fact that LteSpectrumPhy is designed for evaluating the interference only respect to the signal of the
serving eNB. This implies that the PHY layer is optimized for managing the power signals information with
the serving eNB as a reference. However, RSRP and RSRQ of neighbor cell i can be extracted by the current
information available of the serving cell j as detailed in the following:
RSRP_i = \frac{\sum_{k=0}^{K-1}(P_i(k))}{K}
RSSI_i = RSSI_j = \sum_{k=0}^{K-1} 2 \times ( I_j(k) + P_j(k) + N_j(k) )
RSRQ_i^j = K \times RSRP_i / RSSI_j
where RSRP_i is the RSRP of the neighbor cell i, P_i(k) is the power perceived at any RE within the RB k,
K is the total number of RBs, RSSI_i is the RSSI of the neighbor cell i when the UE is attached to cell
j (which, since it is the sum of all the received powers, coincides with RSSI_j), I_j(k) is the total
interference perceived by UE in any RE of RB k when attached to cell i (obtained by the
LteInterferencePowerChunkProcessor), P_j(k) is the power perceived of cell j in any RE of the RB k and N
is the power noise spectral density in any RE. The sample is considered as valid in case of the RSRQ
evaluated is above the LteUePhy::RsrqUeMeasThreshold attribute.
HARQ
The HARQ scheme implemented is based on a incremental redundancy (IR) solutions combined with multiple
stop-and-wait processes for enabling a continuous data flow. In detail, the solution adopted is the soft
combining hybrid IR Full incremental redundancy (also called IR Type II), which implies that the
retransmissions contain only new information respect to the previous ones. The resource allocation
algorithm of the HARQ has been implemented within the respective scheduler classes (i.e.,
RrFfMacScheduler and PfFfMacScheduler, refer to their correspondent sections for more info), while the
decodification part of the HARQ has been implemented in the LteSpectrumPhy and LteHarqPhy classes which
will be detailed in this section.
According to the standard, the UL retransmissions are synchronous and therefore are allocated 7 ms after
the original transmission. On the other hand, for the DL, they are asynchronous and therefore can be
allocated in a more flexible way starting from 7 ms and it is a matter of the specific scheduler
implementation. The HARQ processes behavior is depicted in Figure:ref:fig-harq-processes-scheme.
At the MAC layer, the HARQ entity residing in the scheduler is in charge of controlling the 8 HARQ
processes for generating new packets and managing the retransmissions both for the DL and the UL. The
scheduler collects the HARQ feedback from eNB and UE PHY layers (respectively for UL and DL connection)
by means of the FF API primitives SchedUlTriggerReq and SchedUlTriggerReq. According to the HARQ feedback
and the RLC buffers status, the scheduler generates a set of DCIs including both retransmissions of HARQ
blocks received erroneous and new transmissions, in general, giving priority to the former. On this
matter, the scheduler has to take into consideration one constraint when allocating the resource for HARQ
retransmissions, it must use the same modulation order of the first transmission attempt (i.e., QPSK for
MCS \in [0..9], 16QAM for MCS \in [10..16] and 64QAM for MCS \in [17..28]). This restriction comes from
the specification of the rate matcher in the 3GPP standard [ TS36212]_, where the algorithm fixes the
modulation order for generating the different blocks of the redundancy versions.
The PHY Error Model model (i.e., the LteMiErrorModel class already presented before) has been extended
for considering IR HARQ according to [wimaxEmd], where the parameters for the AWGN curves mapping for
MIESM mapping in case of retransmissions are given by:
R_{eff} = \frac{X}{\sum\limits_{i=1}^q C_i}
M_{I eff} = \frac{\sum\limits_{i=1}^q C_i M_i}{\sum\limits_{i=1}^q C_i}
where X is the number of original information bits, C_i are number of coded bits, M_i are the mutual
information per HARQ block received on the total number of q retransmissions. Therefore, in order to be
able to return the error probability with the error model implemented in the simulator evaluates the
R_{eff} and the MI_{I eff} and return the value of error probability of the ECR of the same modulation
with closest lower rate respect to the R_{eff}. In order to consider the effect of HARQ retransmissions a
new sets of curves have been integrated respect to the standard one used for the original MCS. The new
curves are intended for covering the cases when the most conservative MCS of a modulation is used which
implies the generation of R_{eff} lower respect to the one of standard MCSs. On this matter the curves
for 1, 2 and 3 retransmissions have been evaluated for 10 and 17. For MCS 0 we considered only the first
retransmission since the produced code rate is already very conservative (i.e., 0.04) and returns an
error rate enough robust for the reception (i.e., the downturn of the BLER is centered around -18 dB).
It is to be noted that, the size of first TB transmission has been assumed as containing all the
information bits to be coded; therefore X is equal to the size of the first TB sent of a an HARQ process.
The model assumes that the eventual presence of parity bits in the codewords is already considered in the
link level curves. This implies that as soon as the minimum R_{eff} is reached the model is not including
the gain due to the transmission of further parity bits.
[image] HARQ processes behavior in LTE.UNINDENT
The part of HARQ devoted to manage the decodification of the HARQ blocks has been implemented in the
LteHarqPhy and LteSpectrumPhy classes. The former is in charge of maintaining the HARQ information for
each active process . The latter interacts with LteMiErrorModel class for evaluating the correctness of
the blocks received and includes the messaging algorithm in charge of communicating to the HARQ entity
in the scheduler the result of the decodifications. These messages are encapsulated in the
dlInfoListElement for DL and ulInfoListElement for UL and sent through the PUCCH and the PHICH
respectively with an ideal error free model according to the assumptions in their implementation. A
sketch of the iteration between HARQ and LTE protocol stack in represented in
Figure:ref:fig-harq-architecture.
Finally, the HARQ engine is always active both at MAC and PHY layer; however, in case of the scheduler
does not support HARQ the system will continue to work with the HARQ functions inhibited (i.e., buffers
are filled but not used). This implementation characteristic gives backward compatibility with
schedulers implemented before HARQ integration.
[image] Interaction between HARQ and LTE protocol stack.UNINDENT
MAC
Resource Allocation Model
We now briefly describe how resource allocation is handled in LTE, clarifying how it is modeled in the
simulator. The scheduler is in charge of generating specific structures called Data Control Indication
(DCI) which are then transmitted by the PHY of the eNB to the connected UEs, in order to inform them of
the resource allocation on a per subframe basis. In doing this in the downlink direction, the scheduler
has to fill some specific fields of the DCI structure with all the information, such as: the Modulation
and Coding Scheme (MCS) to be used, the MAC Transport Block (TB) size, and the allocation bitmap which
identifies which RBs will contain the data transmitted by the eNB to each user.
For the mapping of resources to physical RBs, we adopt a localized mapping approach (see [Sesia2009],
Section 9.2.2.1); hence in a given subframe each RB is always allocated to the same user in both slots.
The allocation bitmap can be coded in different formats; in this implementation, we considered the
Allocation Type 0 defined in [TS36213], according to which the RBs are grouped in Resource Block Groups
(RBG) of different size determined as a function of the Transmission Bandwidth Configuration in use.
For certain bandwidth values not all the RBs are usable, since the group size is not a common divisor of
the group. This is for instance the case when the bandwidth is equal to 25 RBs, which results in a RBG
size of 2 RBs, and therefore 1 RB will result not addressable. In uplink the format of the DCIs is
different, since only adjacent RBs can be used because of the SC-FDMA modulation. As a consequence, all
RBs can be allocated by the eNB regardless of the bandwidth configuration.
Adaptive Modulation and Coding
The simulator provides two Adaptive Modulation and Coding (AMC) models: one based on the GSoC model
[Piro2011] and one based on the physical error model (described in the following sections).
The former model is a modified version of the model described in [Piro2011], which in turn is inspired
from [Seo2004]. Our version is described in the following. Let i denote the generic user, and let
\gamma_i be its SINR. We get the spectral efficiency \eta_i of user i using the following equations:
\mathrm{BER} = 0.00005
\Gamma = \frac{ -\ln{ (5 * \mathrm{BER}) } }{ 1.5}
\eta_i = \log_2 { \left( 1 + \frac{ {\gamma}_i }{ \Gamma } \right)}
The procedure described in [R1-081483] is used to get the corresponding MCS scheme. The spectral
efficiency is quantized based on the channel quality indicator (CQI), rounding to the lowest value, and
is mapped to the corresponding MCS scheme.
Finally, we note that there are some discrepancies between the MCS index in [R1-081483] and that
indicated by the standard: [TS36213] Table 7.1.7.1-1 says that the MCS index goes from 0 to 31, and 0
appears to be a valid MCS scheme (TB size is not 0) but in [R1-081483] the first useful MCS index is 1.
Hence to get the value as intended by the standard we need to subtract 1 from the index reported in
[R1-081483].
The alternative model is based on the physical error model developed for this simulator and explained in
the following subsections. This scheme is able to adapt the MCS selection to the actual PHY layer
performance according to the specific CQI report. According to their definition, a CQI index is assigned
when a single PDSCH TB with the modulation coding scheme and code rate correspondent to that CQI index in
table 7.2.3-1 of [TS36213] can be received with an error probability less than 0.1. In case of wideband
CQIs, the reference TB includes all the RBGs available in order to have a reference based on the whole
available resources; while, for subband CQIs, the reference TB is sized as the RBGs.
Transport Block model
The model of the MAC Transport Blocks (TBs) provided by the simulator is simplified with respect to the
3GPP specifications. In particular, a simulator-specific class (PacketBurst) is used to aggregate MAC
SDUs in order to achieve the simulator’s equivalent of a TB, without the corresponding implementation
complexity. The multiplexing of different logical channels to and from the RLC layer is performed using
a dedicated packet tag (LteRadioBearerTag), which performs a functionality which is partially equivalent
to that of the MAC headers specified by 3GPP.
The FemtoForum MAC Scheduler Interface
This section describes the ns-3 specific version of the LTE MAC Scheduler Interface Specification
published by the FemtoForum [FFAPI].
We implemented the ns-3 specific version of the FemtoForum MAC Scheduler Interface [FFAPI] as a set of
C++ abstract classes; in particular, each primitive is translated to a C++ method of a given class. The
term implemented here is used with the same meaning adopted in [FFAPI], and hence refers to the process
of translating the logical interface specification to a particular programming language. The primitives
in [FFAPI] are grouped in two groups: the CSCHED primitives, which deal with scheduler configuration, and
the SCHED primitives, which deal with the execution of the scheduler. Furthermore, [FFAPI] defines
primitives of two different kinds: those of type REQ go from the MAC to the Scheduler, and those of type
IND/CNF go from the scheduler to the MAC. To translate these characteristics into C++, we define the
following abstract classes that implement Service Access Points (SAPs) to be used to issue the
primitives:
• the FfMacSchedSapProvider class defines all the C++ methods that correspond to SCHED primitives of
type REQ;
• the FfMacSchedSapUser class defines all the C++ methods that correspond to SCHED primitives of type
CNF/IND;
• the FfMacCschedSapProvider class defines all the C++ methods that correspond to CSCHED primitives of
type REQ;
• the FfMacCschedSapUser class defines all the C++ methods that correspond to CSCHED primitives of
type CNF/IND;
There are 3 blocks involved in the MAC Scheduler interface: Control block, Subframe block and Scheduler
block. Each of these blocks provide one part of the MAC Scheduler interface. The figure below shows the
relationship between the blocks and the SAPs defined in our implementation of the MAC Scheduler
Interface.
[image]
In addition to the above principles, the following design choices have been taken:
• The definition of the MAC Scheduler interface classes follows the naming conventions of the ns-3
Coding Style. In particular, we follow the CamelCase convention for the primitive names. For
example, the primitive CSCHED_CELL_CONFIG_REQ is translated to CschedCellConfigReq in the ns-3 code.
• The same naming conventions are followed for the primitive parameters. As the primitive parameters
are member variables of classes, they are also prefixed with a m_.
• regarding the use of vectors and lists in data structures, we note that [FFAPI] is a pretty much
C-oriented API. However, considered that C++ is used in ns-3, and that the use of C arrays is
discouraged, we used STL vectors (std::vector) for the implementation of the MAC Scheduler
Interface, instead of using C arrays as implicitly suggested by the way [FFAPI] is written.
• In C++, members with constructors and destructors are not allow in unions. Hence all those data
structures that are said to be unions in [FFAPI] have been defined as structs in our code.
The figure below shows how the MAC Scheduler Interface is used within the eNB.
[image]
The User side of both the CSCHED SAP and the SCHED SAP are implemented within the eNB MAC, i.e., in the
file lte-enb-mac.cc. The eNB MAC can be used with different scheduler implementations without
modifications. The same figure also shows, as an example, how the Round Robin Scheduler is implemented:
to interact with the MAC of the eNB, the Round Robin scheduler implements the Provider side of the SCHED
SAP and CSCHED SAP interfaces. A similar approach can be used to implement other schedulers as well. A
description of each of the scheduler implementations that we provide as part of our LTE simulation module
is provided in the following subsections.
Round Robin (RR) Scheduler
The Round Robin (RR) scheduler is probably the simplest scheduler found in the literature. It works by
dividing the available resources among the active flows, i.e., those logical channels which have a
non-empty RLC queue. If the number of RBGs is greater than the number of active flows, all the flows can
be allocated in the same subframe. Otherwise, if the number of active flows is greater than the number of
RBGs, not all the flows can be scheduled in a given subframe; then, in the next subframe the allocation
will start from the last flow that was not allocated. The MCS to be adopted for each user is done
according to the received wideband CQIs.
For what concern the HARQ, RR implements the non adaptive version, which implies that in allocating the
retransmission attempts RR uses the same allocation configuration of the original block, which means
maintaining the same RBGs and MCS. UEs that are allocated for HARQ retransmissions are not considered for
the transmission of new data in case they have a transmission opportunity available in the same TTI.
Finally, HARQ can be disabled with ns3 attribute system for maintaining backward compatibility with old
test cases and code, in detail:
Config::SetDefault ("ns3::RrFfMacScheduler::HarqEnabled", BooleanValue (false));
The scheduler implements the filtering of the uplink CQIs according to their nature with UlCqiFilter
attribute, in detail:
• SRS_UL_CQI: only SRS based CQI are stored in the internal attributes.
• PUSCH_UL_CQI: only PUSCH based CQI are stored in the internal attributes.
Proportional Fair (PF) Scheduler
The Proportional Fair (PF) scheduler [Sesia2009] works by scheduling a user when its instantaneous
channel quality is high relative to its own average channel condition over time. Let i,j denote generic
users; let t be the subframe index, and k be the resource block index; let M_{i,k}(t) be MCS usable by
user i on resource block k according to what reported by the AMC model (see Adaptive Modulation and
Coding); finally, let S(M, B) be the TB size in bits as defined in [TS36213] for the case where a number
B of resource blocks is used. The achievable rate R_{i}(k,t) in bit/s for user i on resource block group
k at subframe t is defined as
R_{i}(k,t) = \frac{S\left( M_{i,k}(t), 1\right)}{\tau}
where \tau is the TTI duration. At the start of each subframe t, each RBG is assigned to a certain user.
In detail, the index \widehat{i}_{k}(t) to which RBG k is assigned at time t is determined as
\widehat{i}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
\left( \frac{ R_{j}(k,t) }{ T_\mathrm{j}(t) } \right)
where T_{j}(t) is the past througput performance perceived by the user j. According to the above
scheduling algorithm, a user can be allocated to different RBGs, which can be either adjacent or not,
depending on the current condition of the channel and the past throughput performance T_{j}(t). The
latter is determined at the end of the subframe t using the following exponential moving average
approach:
T_{j}(t) =
(1-\frac{1}{\alpha})T_{j}(t-1) +\frac{1}{\alpha} \widehat{T}_{j}(t)
where \alpha is the time constant (in number of subframes) of the exponential moving average, and
\widehat{T}_{j}(t) is the actual throughput achieved by the user i in the subframe t. \widehat{T}_{j}(t)
is measured according to the following procedure. First we determine the MCS \widehat{M}_j(t) actually
used by user j:
\widehat{M}_j(t) = \min_{k: \widehat{i}_{k}(t) = j}{M_{j,k}(t)}
then we determine the total number \widehat{B}_j(t) of RBGs allocated to user j:
\widehat{B}_j(t) = \left| \{ k : \widehat{i}_{k}(t) = j \} \right|
where |\cdot| indicates the cardinality of the set; finally,
\widehat{T}_{j}(t) = \frac{S\left( \widehat{M}_j(t), \widehat{B}_j(t)
\right)}{\tau}
For what concern the HARQ, PF implements the non adaptive version, which implies that in allocating the
retransmission attempts the scheduler uses the same allocation configuration of the original block, which
means maintaining the same RBGs and MCS. UEs that are allocated for HARQ retransmissions are not
considered for the transmission of new data in case they have a transmission opportunity available in the
same TTI. Finally, HARQ can be disabled with ns3 attribute system for maintaining backward compatibility
with old test cases and code, in detail:
Config::SetDefault ("ns3::PfFfMacScheduler::HarqEnabled", BooleanValue (false));
Maximum Throughput (MT) Scheduler
The Maximum Throughput (MT) scheduler [FCapo2012] aims to maximize the overall throughput of eNB. It
allocates each RB to the user that can achieve the maximum achievable rate in the current TTI.
Currently, MT scheduler in NS-3 has two versions: frequency domain (FDMT) and time domain (TDMT). In
FDMT, every TTI, MAC scheduler allocates RBGs to the UE who has highest achievable rate calculated by
subband CQI. In TDMT, every TTI, MAC scheduler selects one UE which has highest achievable rate
calculated by wideband CQI. Then MAC scheduler allocates all RBGs to this UE in current TTI. The
calculation of achievable rate in FDMT and TDMT is as same as the one in PF. Let i,j denote generic
users; let t be the subframe index, and k be the resource block index; let M_{i,k}(t) be MCS usable by
user i on resource block k according to what reported by the AMC model (see Adaptive Modulation and
Coding); finally, let S(M, B) be the TB size in bits as defined in [TS36213] for the case where a number
B of resource blocks is used. The achievable rate R_{i}(k,t) in bit/s for user i on resource block k at
subframe t is defined as
R_{i}(k,t) = \frac{S\left( M_{i,k}(t), 1\right)}{\tau}
where \tau is the TTI duration. At the start of each subframe t, each RB is assigned to a certain user.
In detail, the index \widehat{i}_{k}(t) to which RB k is assigned at time t is determined as
\widehat{i}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
\left( { R_{j}(k,t) } \right)
When there are several UEs having the same achievable rate, current implementation always selects the
first UE created in script. Although MT can maximize cell throughput, it cannot provide fairness to UEs
in poor channel condition.
Throughput to Average (TTA) Scheduler
The Throughput to Average (TTA) scheduler [FCapo2012] can be considered as an intermediate between MT and
PF. The metric used in TTA is calculated as follows:
\widehat{i}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
\left( \frac{ R_{j}(k,t) }{ R_{j}(t) } \right)
Here, R_{i}(k,t) in bit/s represents the achievable rate for user i on resource block k at subframe t.
The calculation method already is shown in MT and PF. Meanwhile, R_{i}(t) in bit/s stands for the
achievable rate for i at subframe t. The difference between those two achievable rates is how to get MCS.
For R_{i}(k,t), MCS is calculated by subband CQI while R_{i}(t) is calculated by wideband CQI. TTA
scheduler can only be implemented in frequency domain (FD) because the achievable rate of particular RBG
is only related to FD scheduling.
Blind Average Throughput Scheduler
The Blind Average Throughput scheduler [FCapo2012] aims to provide equal throughput to all UEs under eNB.
The metric used in TTA is calculated as follows:
\widehat{i}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
\left( \frac{ 1 }{ T_\mathrm{j}(t) } \right)
where T_{j}(t) is the past throughput performance perceived by the user j and can be calculated by the
same method in PF scheduler. In the time domain blind average throughput (TD-BET), the scheduler selects
the UE with largest priority metric and allocates all RBGs to this UE. On the other hand, in the
frequency domain blind average throughput (FD-BET), every TTI, the scheduler first selects one UE with
lowest pastAverageThroughput (largest priority metric). Then scheduler assigns one RBG to this UE, it
calculates expected throughput of this UE and uses it to compare with past average throughput T_{j}(t) of
other UEs. The scheduler continues to allocate RBG to this UE until its expected throughput is not the
smallest one among past average throughput T_{j}(t) of all UE. Then the scheduler will use the same way
to allocate RBG for a new UE which has the lowest past average throughput T_{j}(t) until all RBGs are
allocated to UEs. The principle behind this is that, in every TTI, the scheduler tries the best to
achieve the equal throughput among all UEs.
Token Bank Fair Queue Scheduler
Token Bank Fair Queue (TBFQ) is a QoS aware scheduler which derives from the leaky-bucket mechanism. In
TBFQ, a traffic flow of user i is characterized by following parameters:
• t_{i}: packet arrival rate (byte/sec )
• r_{i}: token generation rate (byte/sec)
• p_{i}: token pool size (byte)
• E_{i}: counter that records the number of token borrowed from or given to the token bank by flow i ;
E_{i} can be smaller than zero
Each K bytes data consumes k tokens. Also, TBFQ maintains a shared token bank (B) so as to balance the
traffic between different flows. If token generation rate r_{i} is bigger than packet arrival rate t_{i},
then tokens overflowing from token pool are added to the token bank, and E_{i} is increased by the same
amount. Otherwise, flow i needs to withdraw tokens from token bank based on a priority metric
frac{E_{i}}{r_{i}}, and E_{i} is decreased. Obviously, the user contributes more on token bank has
higher priority to borrow tokens; on the other hand, the user borrows more tokens from bank has lower
priority to continue to withdraw tokens. Therefore, in case of several users having the same token
generation rate, traffic rate and token pool size, user suffers from higher interference has more
opportunity to borrow tokens from bank. In addition, TBFQ can police the traffic by setting the token
generation rate to limit the throughput. Additionally, TBFQ also maintains following three parameters
for each flow:
• Debt limit d_{i}: if E_{i} belows this threshold, user i cannot further borrow tokens from bank.
This is for preventing malicious UE to borrow too much tokens.
• Credit limit c_{i}: the maximum number of tokens UE i can borrow from the bank in one time.
• Credit threshold C: once E_{i} reaches debt limit, UE i must store C tokens to bank in order to
further borrow token from bank.
LTE in NS-3 has two versions of TBFQ scheduler: frequency domain TBFQ (FD-TBFQ) and time domain TBFQ
(TD-TBFQ). In FD-TBFQ, the scheduler always select UE with highest metric and allocates RBG with highest
subband CQI until there are no packets within UE’s RLC buffer or all RBGs are allocated [FABokhari2009].
In TD-TBFQ, after selecting UE with maximum metric, it allocates all RBGs to this UE by using wideband
CQI [WKWong2004].
Priority Set Scheduler
Priority set scheduler (PSS) is a QoS aware scheduler which combines time domain (TD) and frequency
domain (FD) packet scheduling operations into one scheduler [GMonghal2008]. It controls the fairness
among UEs by a specified Target Bit Rate (TBR).
In TD scheduler part, PSS first selects UEs with non-empty RLC buffer and then divide them into two sets
based on the TBR:
• set 1: UE whose past average throughput is smaller than TBR; TD scheduler calculates their priority
metric in Blind Equal Throughput (BET) style:
\widehat{i}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
\left( \frac{ 1 }{ T_\mathrm{j}(t) } \right)
• set 2: UE whose past average throughput is larger (or equal) than TBR; TD scheduler calculates their
priority metric in Proportional Fair (PF) style:
\widehat{i}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
\left( \frac{ R_{j}(k,t) }{ T_\mathrm{j}(t) } \right)
UEs belonged to set 1 have higher priority than ones in set 2. Then PSS will select N_{mux} UEs with
highest metric in two sets and forward those UE to FD scheduler. In PSS, FD scheduler allocates RBG k to
UE n that maximums the chosen metric. Two PF schedulers are used in PF scheduler:
• Proportional Fair scheduled (PFsch)
\widehat{Msch}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
\left( \frac{ R_{j}(k,t) }{ Tsch_\mathrm{j}(t) } \right)
• Carrier over Interference to Average (CoIta)
\widehat{Mcoi}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
\left( \frac{ CoI[j,k] }{ \sum_{k=0}^{N_{RBG}} CoI[j,k] } \right)
where Tsch_{j}(t) is similar past throughput performance perceived by the user j, with the difference
that it is updated only when the i-th user is actually served. CoI[j,k] is an estimation of the SINR on
the RBG k of UE j. Both PFsch and CoIta is for decoupling FD metric from TD scheduler. In addition, PSS
FD scheduler also provide a weight metric W[n] for helping controlling fairness in case of low number of
UEs.
W[n] = max (1, \frac{TBR}{ T_{j}(t) })
where T_{j}(t) is the past throughput performance perceived by the user j . Therefore, on RBG k, the FD
scheduler selects the UE j that maximizes the product of the frequency domain metric (Msch, MCoI) by
weight W[n]. This strategy will guarantee the throughput of lower quality UE tend towards the TBR.
Config::SetDefault ("ns3::PfFfMacScheduler::HarqEnabled", BooleanValue (false));
The scheduler implements the filtering of the uplink CQIs according to their nature with UlCqiFilter
attribute, in detail:
• SRS_UL_CQI: only SRS based CQI are stored in the internal attributes.
• PUSCH_UL_CQI: only PUSCH based CQI are stored in the internal attributes.
Channel and QoS Aware Scheduler
The Channel and QoS Aware (CQA) Scheduler [Bbojovic2014] is an LTE MAC downlink scheduling algorithm that
considers the head of line (HOL) delay, the GBR parameters and channel quality over different subbands.
The CQA scheduler is based on joint TD and FD scheduling.
In the TD (at each TTI) the CQA scheduler groups users by priority. The purpose of grouping is to enforce
the FD scheduling to consider first the flows with highest HOL delay. The grouping metric m_{td} for user
j=1,...,N is defined in the following way:
m_{td}^{j}(t) = \lceil\frac{d_{hol}^{j}(t)}{g}\rceil \;,
where d_{hol}^{j}(t) is the current value of HOL delay of flow j, and g is a grouping parameter that
determines granularity of the groups, i.e. the number of the flows that will be considered in the FD
scheduling iteration.
The groups of flows selected in the TD iteration are forwarded to the FD scheduling starting from the
flows with the highest value of the m_{td} metric until all RBGs are assigned in the corresponding TTI.
In the FD, for each RBG k=1,...,K, the CQA scheduler assigns the current RBG to the user j that has the
maximum value of the FD metric which we define in the following way:
m_{fd}^{(k,j)}(t) = d_{HOL}^{j}(t) \cdot m_{GBR}^j(t) \cdot m_{ca}^{k,j}(t) \;,
where m_{GBR}^j(t) is calculated as follows:
m_{GBR}^j(t)=\frac{GBR^j}{\overline{R^j}(t)}=\frac{GBR^j}{(1-\alpha)\cdot\overline{R^j}(t-1)+\alpha \cdot
r^j(t)} \;,
where GBR^j is the bit rate specified in EPS bearer of the flow j, \overline{R^j}(t) is the past averaged
throughput that is calculated with a moving average, r^{j}(t) is the throughput achieved at the time t,
and \alpha is a coefficient such that 0 \le \alpha \le1.
For m_{ca}^{(k,j)}(t) we consider two different metrics: m_{pf}^{(k,j)}(t) and m_{ff}^{(k,j)}(t). m_{pf}
is the Proportional Fair metric which is defined as follows:
m_{pf}^{(k,j)}(t) = \frac{R_e^{(k,j)}}{\overline{R^j}(t)} \;,
where R_e^{(k,j)}(t) is the estimated achievable throughput of user j over RBG k calculated by the
Adaptive Modulation and Coding (AMC) scheme that maps the channel quality indicator (CQI) value to the
transport block size in bits.
The other channel awareness metric that we consider is m_{ff} which is proposed in [GMonghal2008] and it
represents the frequency selective fading gains over RBG k for user j and is calculated in the following
way:
m_{ff}^{(k,j)}(t) = \frac{CQI^{(k,j)}(t)}{\sum_{k=1}^{K}CQI(t)^{(k,j)}} \;,
where CQI^{(k,j)}(t) is the last reported CQI value from user j for the k-th RBG.
The user can select whether m_{pf} or m_{ff} is used by setting the attribute
ns3::CqaFfMacScheduler::CqaMetric respectively to "CqaPf" or "CqaFf".
Random Access
The LTE model includes a model of the Random Access procedure based on some simplifying assumptions,
which are detailed in the following for each of the messages and signals described in the specs
[TS36321].
• Random Access (RA) preamble: in real LTE systems this corresponds to a Zadoff-Chu (ZC) sequence
using one of several formats available and sent in the PRACH slots which could in principle overlap
with PUSCH. PRACH Configuration Index 14 is assumed, i.e., preambles can be sent on any system
frame number and subframe number. The RA preamble is modeled using the LteControlMessage class,
i.e., as an ideal message that does not consume any radio resources. The collision of preamble
transmission by multiple UEs in the same cell are modeled using a protocol interference model, i.e.,
whenever two or more identical preambles are transmitted in same cell at the same TTI, no one of
these identical preambles will be received by the eNB. Other than this collision model, no error
model is associated with the reception of a RA preamble.
• Random Access Response (RAR): in real LTE systems, this is a special MAC PDU sent on the DL-SCH.
Since MAC control elements are not accurately modeled in the simulator (only RLC and above PDUs
are), the RAR is modeled as an LteControlMessage that does not consume any radio resources. Still,
during the RA procedure, the LteEnbMac will request to the scheduler the allocation of resources for
the RAR using the FF MAC Scheduler primitive SCHED_DL_RACH_INFO_REQ. Hence, an enhanced scheduler
implementation (not available at the moment) could allocate radio resources for the RAR, thus
modeling the consumption of Radio Resources for the transmission of the RAR.
• Message 3: in real LTE systems, this is an RLC TM SDU sent over resources specified in the UL Grant
in the RAR. In the simulator, this is modeled as a real RLC TM RLC PDU whose UL resources are
allocated by the scheduler upon call to SCHED_DL_RACH_INFO_REQ.
• Contention Resolution (CR): in real LTE system, the CR phase is needed to address the case where two
or more UE sent the same RA preamble in the same TTI, and the eNB was able to detect this preamble
in spite of the collision. Since this event does not occur due to the protocol interference model
used for the reception of RA preambles, the CR phase is not modeled in the simulator, i.e., the CR
MAC CE is never sent by the eNB and the UEs consider the RA to be successful upon reception of the
RAR. As a consequence, the radio resources consumed for the transmission of the CR MAC CE are not
modeled.
Figure Sequence diagram of the Contention-based MAC Random Access procedure and Sequence diagram of the
Non-contention-based MAC Random Access procedure shows the sequence diagrams of respectively the
contention-based and non-contention-based MAC random access procedure, highlighting the interactions
between the MAC and the other entities.
[image] Sequence diagram of the Contention-based MAC Random Access procedure.UNINDENT
[image] Sequence diagram of the Non-contention-based MAC Random Access procedure.UNINDENT
RLC
Overview
The RLC entity is specified in the 3GPP technical specification [TS36322], and comprises three different
types of RLC: Transparent Mode (TM), Unacknowledged Mode (UM) and Acknowledged Mode (AM). The simulator
includes one model for each of these entities
The RLC entities provide the RLC service interface to the upper PDCP layer and the MAC service interface
to the lower MAC layer. The RLC entities use the PDCP service interface from the upper PDCP layer and the
MAC service interface from the lower MAC layer.
Figure Implementation Model of PDCP, RLC and MAC entities and SAPs shows the implementation model of the
RLC entities and its relationship with all the other entities and services in the protocol stack.
[image] Implementation Model of PDCP, RLC and MAC entities and SAPs.UNINDENT
Service Interfaces
RLC Service Interface
The RLC service interface is divided into two parts:
• the RlcSapProvider part is provided by the RLC layer and used by the upper PDCP layer and
• the RlcSapUser part is provided by the upper PDCP layer and used by the RLC layer.
Both the UM and the AM RLC entities provide the same RLC service interface to the upper PDCP layer.
RLC Service Primitives
The following list specifies which service primitives are provided by the RLC service interfaces:
• RlcSapProvider::TransmitPdcpPdu
• The PDCP entity uses this primitive to send a PDCP PDU to the lower RLC entity in the
transmitter peer
• RlcSapUser::ReceivePdcpPdu
• The RLC entity uses this primitive to send a PDCP PDU to the upper PDCP entity in the receiver
peer
MAC Service Interface
The MAC service interface is divided into two parts:
• the MacSapProvider part is provided by the MAC layer and used by the upper RLC layer and
• the MacSapUser part is provided by the upper RLC layer and used by the MAC layer.
MAC Service Primitives
The following list specifies which service primitives are provided by the MAC service interfaces:
• MacSapProvider::TransmitPdu
• The RLC entity uses this primitive to send a RLC PDU to the lower MAC entity in the
transmitter peer
• MacSapProvider::ReportBufferStatus
• The RLC entity uses this primitive to report the MAC entity the size of pending buffers in the
transmitter peer
• MacSapUser::NotifyTxOpportunity
• The MAC entity uses this primitive to notify the RLC entity a transmission opportunity
• MacSapUser::ReceivePdu
• The MAC entity uses this primitive to send an RLC PDU to the upper RLC entity in the receiver
peer
AM RLC
The processing of the data transfer in the Acknowledge Mode (AM) RLC entity is explained in section 5.1.3
of [TS36322]. In this section we describe some details of the implementation of the RLC entity.
Buffers for the transmit operations
Our implementation of the AM RLC entity maintains 3 buffers for the transmit operations:
• Transmission Buffer: it is the RLC SDU queue. When the AM RLC entity receives a SDU in the
TransmitPdcpPdu service primitive from the upper PDCP entity, it enqueues it in the Transmission
Buffer. We put a limit on the RLC buffer size and the LteRlc TxDrop trace source is called when a
drop due to a full buffer occurs.
• Transmitted PDUs Buffer: it is the queue of transmitted RLC PDUs for which an ACK/NACK has not been
received yet. When the AM RLC entity sends a PDU to the MAC entity, it also puts a copy of the
transmitted PDU in the Transmitted PDUs Buffer.
• Retransmission Buffer: it is the queue of RLC PDUs which are considered for retransmission (i.e.,
they have been NACKed). The AM RLC entity moves this PDU to the Retransmission Buffer, when it
retransmits a PDU from the Transmitted Buffer.
Transmit operations in downlink
The following sequence diagram shows the interactions between the different entities (RRC, PDCP, AM RLC,
MAC and MAC scheduler) of the eNB in the downlink to perform data communications.
Figure Sequence diagram of data PDU transmission in downlink shows how the upper layers send data PDUs
and how the data flow is processed by the different entities/services of the LTE protocol stack.
[image] Sequence diagram of data PDU transmission in downlink.UNINDENT
The PDCP entity calls the Transmit_PDCP_PDU service primitive in order to send a data PDU. The AM RLC
entity processes this service primitive according to the AM data transfer procedures defined in section
5.1.3 of [TS36322].
When the Transmit_PDCP_PDU service primitive is called, the AM RLC entity performs the following
operations:
• Put the data SDU in the Transmission Buffer.
• Compute the size of the buffers (how the size of buffers is computed will be explained afterwards).
• Call the Report_Buffer_Status service primitive of the eNB MAC entity in order to notify to the eNB
MAC entity the sizes of the buffers of the AM RLC entity. Then, the eNB MAC entity updates the
buffer status in the MAC scheduler using the SchedDlRlcBufferReq service primitive of the FF MAC
Scheduler API.
Afterwards, when the MAC scheduler decides that some data can be sent, the MAC entity notifies it to the
RLC entity, i.e. it calls the Notify_Tx_Opportunity service primitive, then the AM RLC entity does the
following:
• Create a single data PDU by segmenting and/or concatenating the SDUs in the Transmission Buffer.
• Move the data PDU from the Transmission Buffer to the Transmitted PDUs Buffer.
• Update state variables according section 5.1.3.1.1 of [TS36322].
• Call the Transmit_PDU primitive in order to send the data PDU to the MAC entity.
Retransmission in downlink
The sequence diagram of Figure Sequence diagram of data PDU retransmission in downlink shows the
interactions between the different entities (AM RLC, MAC and MAC scheduler) of the eNB in downlink when
data PDUs must be retransmitted by the AM RLC entity.
[image] Sequence diagram of data PDU retransmission in downlink.UNINDENT
The transmitting AM RLC entity can receive STATUS PDUs from the peer AM RLC entity. STATUS PDUs are
sent according section 5.3.2 of [TS36322] and the processing of reception is made according section
5.2.1 of [TS36322].
When a data PDUs is retransmitted from the Transmitted PDUs Buffer, it is also moved to the
Retransmission Buffer.
Transmit operations in uplink
The sequence diagram of Figure Sequence diagram of data PDU transmission in uplink shows the interactions
between the different entities of the UE (RRC, PDCP, RLC and MAC) and the eNB (MAC and Scheduler) in
uplink when data PDUs are sent by the upper layers.
[image] Sequence diagram of data PDU transmission in uplink.UNINDENT
It is similar to the sequence diagram in downlink; the main difference is that in this case the
Report_Buffer_Status is sent from the UE MAC to the MAC Scheduler in the eNB over the air using the
control channel.
Retransmission in uplink
The sequence diagram of Figure Sequence diagram of data PDU retransmission in uplink shows the
interactions between the different entities of the UE (AM RLC and MAC) and the eNB (MAC) in uplink when
data PDUs must be retransmitted by the AM RLC entity.
[image] Sequence diagram of data PDU retransmission in uplink.UNINDENT
Calculation of the buffer size
The Transmission Buffer contains RLC SDUs. A RLC PDU is one or more SDU segments plus an RLC header. The
size of the RLC header of one RLC PDU depends on the number of SDU segments the PDU contains.
The 3GPP standard (section 6.1.3.1 of [TS36321]) says clearly that, for the uplink, the RLC and MAC
headers are not considered in the buffer size that is to be report as part of the Buffer Status Report.
For the downlink, the behavior is not specified. Neither [FFAPI] specifies how to do it. Our RLC model
works by assuming that the calculation of the buffer size in the downlink is done exactly as in the
uplink, i.e., not considering the RLC and MAC header size.
We note that this choice affects the interoperation with the MAC scheduler, since, in response to the
Notify_Tx_Opportunity service primitive, the RLC is expected to create a PDU of no more than the size
requested by the MAC, including RLC overhead. Hence, unneeded fragmentation can occur if (for example)
the MAC notifies a transmission exactly equal to the buffer size previously reported by the RLC. We
assume that it is left to the Scheduler to implement smart strategies for the selection of the size of
the transmission opportunity, in order to eventually avoid the inefficiency of unneeded fragmentation.
Concatenation and Segmentation
The AM RLC entity generates and sends exactly one RLC PDU for each transmission opportunity even if it is
smaller than the size reported by the transmission opportunity. So for instance, if a STATUS PDU is to be
sent, then only this PDU will be sent in that transmission opportunity.
The segmentation and concatenation for the SDU queue of the AM RLC entity follows the same philosophy as
the same procedures of the UM RLC entity but there are new state variables (see [TS36322] section 7.1)
only present in the AM RLC entity.
It is noted that, according to the 3GPP specs, there is no concatenation for the Retransmission Buffer.
Re-segmentation
The current model of the AM RLC entity does not support the re-segmentation of the retransmission buffer.
Rather, the AM RLC entity just waits to receive a big enough transmission opportunity.
Unsupported features
We do not support the following procedures of [TS36322] :
• “Send an indication of successful delivery of RLC SDU” (See section 5.1.3.1.1)
• “Indicate to upper layers that max retransmission has been reached” (See section 5.2.1)
• “SDU discard procedures” (See section 5.3)
• “Re-establishment procedure” (See section 5.4)
We do not support any of the additional primitives of RLC SAP for AM RLC entity. In particular:
• no SDU discard notified by PDCP
• no notification of successful / failed delivery by AM RLC entity to PDCP entity
UM RLC
In this section we describe the implementation of the Unacknowledged Mode (UM) RLC entity.
Transmit operations in downlink
The transmit operations of the UM RLC are similar to those of the AM RLC previously described in Section
Transmit operations in downlink, with the difference that, following the specifications of [TS36322],
retransmission are not performed, and there are no STATUS PDUs.
Transmit operations in uplink
The transmit operations in the uplink are similar to those of the downlink, with the main difference that
the Report_Buffer_Status is sent from the UE MAC to the MAC Scheduler in the eNB over the air using the
control channel.
Calculation of the buffer size
The calculation of the buffer size for the UM RLC is done using the same approach of the AM RLC, please
refer to section Calculation of the buffer size for the corresponding description.
TM RLC
In this section we describe the implementation of the Transparent Mode (TM) RLC entity.
Transmit operations in downlink
In the simulator, the TM RLC still provides to the upper layers the same service interface provided by
the AM and UM RLC entities to the PDCP layer; in practice, this interface is used by an RRC entity (not a
PDCP entity) for the transmission of RLC SDUs. This choice is motivated by the fact that the services
provided by the TM RLC to the upper layers, according to [TS36322], is a subset of those provided by the
UM and AM RLC entities to the PDCP layer; hence, we reused the same interface for simplicity.
The transmit operations in the downlink are performed as follows. When the Transmit_PDCP_PDU service
primitive is called by the upper layers, the TM RLC does the following:
• put the SDU in the Transmission Buffer
• compute the size of the Transmission Buffer
• call the Report_Buffer_Status service primitive of the eNB MAC entity
Afterwards, when the MAC scheduler decides that some data can be sent by the logical channel to which the
TM RLC entity belongs, the MAC entity notifies it to the TM RLC entity by calling the
Notify_Tx_Opportunity service primitive. Upon reception of this primitive, the TM RLC entity does the
following:
• if the TX opportunity has a size that is greater than or equal to the size of the head-of-line SDU
in the Transmission Buffer
• dequeue the head-of-line SDU from the Transmission Buffer
• create one RLC PDU that contains entirely that SDU, without any RLC header
• Call the Transmit_PDU primitive in order to send the RLC PDU to the MAC entity.
Transmit operations in uplink
The transmit operations in the uplink are similar to those of the downlink, with the main difference that
a transmission opportunity can also arise from the assignment of the UL GRANT as part of the Random
Access procedure, without an explicit Buffer Status Report issued by the TM RLC entity.
Calculation of the buffer size
As per the specifications [TS36322], the TM RLC does not add any RLC header to the PDUs being
transmitted. Because of this, the buffer size reported to the MAC layer is calculated simply by summing
the size of all packets in the transmission buffer, thus notifying to the MAC the exact buffer size.
SM RLC
In addition to the AM, UM and TM implementations that are modeled after the 3GPP specifications, a
simplified RLC model is provided, which is called Saturation Mode (SM) RLC. This RLC model does not
accept PDUs from any above layer (such as PDCP); rather, the SM RLC takes care of the generation of RLC
PDUs in response to the notification of transmission opportunities notified by the MAC. In other words,
the SM RLC simulates saturation conditions, i.e., it assumes that the RLC buffer is always full and can
generate a new PDU whenever notified by the scheduler.
The SM RLC is used for simplified simulation scenarios in which only the LTE Radio model is used, without
the EPC and hence without any IP networking support. We note that, although the SM RLC is an unrealistic
traffic model, it still allows for the correct simulation of scenarios with multiple flows belonging to
different (non real-time) QoS classes, in order to test the QoS performance obtained by different
schedulers. This can be done since it is the task of the Scheduler to assign transmission resources based
on the characteristics (e.g., Guaranteed Bit Rate) of each Radio Bearer, which are specified upon the
definition of each Bearer within the simulation program.
As for schedulers designed to work with real-time QoS traffic that has delay constraints, the SM RLC is
probably not an appropriate choice. This is because the absence of actual RLC SDUs (replaced by the
artificial generation of Buffer Status Reports) makes it not possible to provide the Scheduler with
meaningful head-of-line-delay information, which is often the metric of choice for the implementation of
scheduling policies for real-time traffic flows. For the simulation and testing of such schedulers, it is
advisable to use either the UM or the AM RLC models instead.
PDCP
PDCP Model Overview
The reference document for the specification of the PDCP entity is [TS36323]. With respect to this
specification, the PDCP model implemented in the simulator supports only the following features:
• transfer of data (user plane or control plane);
• maintenance of PDCP SNs;
• transfer of SN status (for use upon handover);
The following features are currently not supported:
• header compression and decompression of IP data flows using the ROHC protocol;
• in-sequence delivery of upper layer PDUs at re-establishment of lower layers;
• duplicate elimination of lower layer SDUs at re-establishment of lower layers for radio bearers
mapped on RLC AM;
• ciphering and deciphering of user plane data and control plane data;
• integrity protection and integrity verification of control plane data;
• timer based discard;
• duplicate discarding.
PDCP Service Interface
The PDCP service interface is divided into two parts:
• the PdcpSapProvider part is provided by the PDCP layer and used by the upper layer and
• the PdcpSapUser part is provided by the upper layer and used by the PDCP layer.
PDCP Service Primitives
The following list specifies which service primitives are provided by the PDCP service interfaces:
• PdcpSapProvider::TransmitPdcpSdu
• The RRC entity uses this primitive to send an RRC PDU to the lower PDCP entity in the
transmitter peer
• PdcpSapUser::ReceivePdcpSdu
• The PDCP entity uses this primitive to send an RRC PDU to the upper RRC entity in the receiver
peer
RRC
Features
The RRC model implemented in the simulator provides the following functionality:
• generation (at the eNB) and interpretation (at the UE) of System Information (in particular the
Master Information Block and, at the time of this writing, only System Information Block Type 1 and
2)
• initial cell selection
• RRC connection establishment procedure
• RRC reconfiguration procedure, supporting the following use cases: + reconfiguration of the SRS
configuration index + reconfiguration of the PHY TX mode (MIMO) + reconfiguration of UE measurements
+ data radio bearer setup + handover
• RRC connection re-establishment, supporting the following use cases: + handover
Architecture
The RRC model is divided into the following components:
• the RRC entities LteUeRrc and LteEnbRrc, which implement the state machines of the RRC entities
respectively at the UE and the eNB;
• the RRC SAPs LteUeRrcSapProvider, LteUeRrcSapUser, LteEnbRrcSapProvider, LteEnbRrcSapUser, which
allow the RRC entities to send and receive RRC messages and information elmenents;
• the RRC protocol classes LteUeRrcProtocolIdeal, LteEnbRrcProtocolIdeal, LteUeRrcProtocolReal,
LteEnbRrcProtocolReal, which implement two different models for the transmission of RRC messages.
Additionally, the RRC components use various other SAPs in order to interact with the rest of the
protocol stack. A representation of all the SAPs that are used is provided in the figures LTE radio
protocol stack architecture for the UE on the data plane, LTE radio protocol stack architecture for the
UE on the control plane, LTE radio protocol stack architecture for the eNB on the data plane and LTE
radio protocol stack architecture for the eNB on the control plane.
UE RRC State Machine
In Figure UE RRC State Machine we represent the state machine as implemented in the RRC UE entity.
[image] UE RRC State Machine.UNINDENT
All the states are transient, however, the UE in “CONNECTED_NORMALLY” state will only switch to the
IDLE state if the downlink SINR is below a defined threshold, which would lead to radio link failure
Radio Link Failure. One the other hand, the UE would not be able switch to IDLE mode due to a handover
failure, as mentioned in X2.
ENB RRC State Machine
The eNB RRC maintains the state for each UE that is attached to the cell. From an implementation point of
view, the state of each UE is contained in an instance of the UeManager class. The state machine is
represented in Figure ENB RRC State Machine for each UE.
[image] ENB RRC State Machine for each UE.UNINDENT
Initial Cell Selection
Initial cell selection is an IDLE mode procedure, performed by UE when it has not yet camped or attached
to an eNodeB. The objective of the procedure is to find a suitable cell and attach to it to gain access
to the cellular network.
It is typically done at the beginning of simulation, as depicted in Figure Sample runs of initial cell
selection in UE and timing of related events below. The time diagram on the left side is illustrating the
case where initial cell selection succeed on first try, while the diagram on the right side is for the
case where it fails on the first try and succeed on the second try. The timing assumes the use of real
RRC protocol model (see RRC protocol models) and no transmission error.
[image] Sample runs of initial cell selection in UE and timing of related events.UNINDENT
The functionality is based on 3GPP IDLE mode specifications, such as in [TS36300], [TS36304], and
[TS36331]. However, a proper implementation of IDLE mode is still missing in the simulator, so we
reserve several simplifying assumptions:
• multiple carrier frequency is not supported;
• multiple Public Land Mobile Network (PLMN) identities (i.e. multiple network operators) is not
supported;
• RSRQ measurements are not utilized;
• stored information cell selection is not supported;
• “Any Cell Selection” state and camping to an acceptable cell is not supported;
• marking a cell as barred or reserved is not supported;
• Idle cell reselection is not supported, hence it is not possible for UE to camp to a different cell
after the initial camp has been placed; and
• UE’s Closed Subscriber Group (CSG) white list contains only one CSG identity.
Also note that initial cell selection is only available for EPC-enabled simulations. LTE-only simulations
must use the manual attachment method. See section sec-network-attachment of the User Documentation for
more information on their differences in usage.
The next subsections cover different parts of initial cell selection, namely cell search, broadcast of
system information, and cell selection evaluation.
Cell Search
Cell search aims to detect surrounding cells and measure the strength of received signal from each of
these cells. One of these cells will become the UE’s entry point to join the cellular network.
The measurements are based on the RSRP of the received PSS, averaged by Layer 1 filtering, and performed
by the PHY layer, as previously described in more detail in section UE PHY Measurements Model. PSS is
transmitted by eNodeB over the central 72 sub-carriers of the DL channel (Section 5.1.7.3 [TS36300]),
hence we model cell search to operate using a DL bandwidth of 6 RBs. Note that measurements of RSRQ are
not available at this point of time in simulation. As a consequence, the LteUePhy::RsrqUeMeasThreshold
attribute does not apply during cell search.
By using the measured RSRP, the PHY entity is able to generate a list of detected cells, each with its
corresponding cell ID and averaged RSRP. This list is periodically pushed via CPHY SAP to the RRC entity
as a measurement report.
The RRC entity inspects the report and simply choose the cell with the strongest RSRP, as also indicated
in Section 5.2.3.1 of [TS36304]. Then it instructs back the PHY entity to synchronize to this particular
cell. The actual operating bandwidth of the cell is still unknown at this time, so the PHY entity listens
only to the minimum bandwidth of 6 RBs. Nevertheless, the PHY entity will be able to receive system
broadcast message from this particular eNodeB, which is the topic of the next subsection.
Broadcast of System Information
System information blocks are broadcasted by eNodeB to UEs at predefined time intervals, adapted from
Section 5.2.1.2 of [TS36331]. The supported system information blocks are:
•
Master Information Block (MIB)
Contains parameters related to the PHY layer, generated during cell configuration and
broadcasted every 10 ms at the beginning of radio frame as a control message.
•
System Information Block Type 1 (SIB1)
Contains information regarding network access, broadcasted every 20 ms at the middle of radio
frame as a control message. Not used in manual attachment method. UE must have decoded MIB
before it can receive SIB1.
•
System Information Block Type 2 (SIB2)
Contains UL- and RACH-related settings, scheduled to transmit via RRC protocol at 16 ms after
cell configuration, and then repeats every 80 ms (configurable through
LteEnbRrc::SystemInformationPeriodicity attribute. UE must be camped to a cell in order to
be able to receive its SIB2.
Reception of system information is fundamental for UE to advance in its lifecycle. MIB enables the UE to
increase the initial DL bandwidth of 6 RBs to the actual operating bandwidth of the network. SIB1
provides information necessary for cell selection evaluation (explained in the next section). And finally
SIB2 is required before the UE is allowed to switch to CONNECTED state.
Cell Selection Evaluation
UE RRC reviews the measurement report produced in Cell Search and the cell access information provided by
SIB1. Once both information is available for a specific cell, the UE triggers the evaluation process. The
purpose of this process is to determine whether the cell is a suitable cell to camp to.
The evaluation process is a slightly simplified version of Section 5.2.3.2 of [TS36304]. It consists of
the following criteria:
• Rx level criterion; and
• closed subscriber group (CSG) criterion.
The first criterion, Rx level, is based on the cell’s measured RSRP Q_{rxlevmeas}, which has to be higher
than a required minimum Q_{rxlevmin} in order to pass the criterion:
Q_{rxlevmeas} - Q_{rxlevmin} > 0
where Q_{rxlevmin} is determined by each eNodeB and is obtainable by UE from SIB1.
The last criterion, CSG, is a combination of a true-or-false parameter called CSG indication and a simple
number CSG identity. The basic rule is that UE shall not camp to eNodeB with a different CSG identity.
But this rule is only enforced when CSG indication is valued as true. More details are provided in
Section sec-network-attachment of the User Documentation.
When the cell passes all the above criteria, the cell is deemed as suitable. Then UE camps to it
(IDLE_CAMPED_NORMALLY state).
After this, upper layer may request UE to enter CONNECTED mode. Please refer to section RRC connection
establishment for details on this.
On the other hand, when the cell does not pass the CSG criterion, then the cell is labeled as acceptable
(Section 10.1.1.1 [TS36300]). In this case, the RRC entity will tell the PHY entity to synchronize to the
second strongest cell and repeat the initial cell selection procedure using that cell. As long as no
suitable cell is found, the UE will repeat these steps while avoiding cells that have been identified as
acceptable.
Radio Admission Control
Radio Admission Control is supported by having the eNB RRC reply to an RRC CONNECTION REQUEST message
sent by the UE with either an RRC CONNECTION SETUP message or an RRC CONNECTION REJECT message, depending
on whether the new UE is to be admitted or not. In the current implementation, the behavior is determined
by the boolean attribute ns3::LteEnbRrc::AdmitRrcConnectionRequest. There is currently no Radio Admission
Control algorithm that dynamically decides whether a new connection shall be admitted or not.
Radio Bearer Configuration
Some implementation choices have been made in the RRC regarding the setup of radio bearers:
• three Logical Channel Groups (out of four available) are configured for uplink buffer status report
purposes, according to the following policy:
• LCG 0 is for signaling radio bearers
• LCG 1 is for GBR data radio bearers
• LCG 2 is for Non-GBR data radio bearers
Radio Link Failure
In real LTE networks, Radio link failure (RLF) can happen due to several reasons. It can be triggered if
a UE is unable to decode PDCCH due to poor signal quality, upon maximum RLC retransmissions, RACH
problems and other reasons. 3GPP only specifies guidelines to detect RLF at the UE side, in [TS36331] and
[TS36133]. On the other hand, the eNB implementation is expected to be vendor specific. To implement
the RLF functionality in ns-3, we have assumed the following simplifications:
• The RLF detection procedure at eNodeB is not implemented. Instead, a direct function call by using
the SAP between UE and eNB RRC (for both ideal and real RRC) is used to notify the eNB about the
RLF.
• No RRC connection re-establishment procedure is implemented, thus, the UE directly goes to the IDLE
state upon RLF. This is in fact as per the standard [TS36331] sec 5.3.11.3, since, at this stage the
LTE module does not support the Access Stratum (AS) security.
The above mentioned RLF specifications can be divided into the following two categories:
1. RLF detection
2. Actions upon RLF detection
In the following, we will explain the RLF implementation in context of these two categories.
RLF detection implementation
The RLF detection at the UE is implemented as per [TS36133], i.e., by monitoring the radio link quality
based on the reference signals (which in the simulation is equivalent to the PDCCH) in the downlink.
Thus, it is independent of the method used for the downlink CQI computation, i.e., Ctrl method and Mixed
method. Moreover, when using FFR, especially for hard-FFR, and CQIs based on Mixed method, UEs might
experience relatively good performance and RLF simultaneously. This is due to the fact that the
interference in PDSCH is affected by the actual data transmissions on the specific RBs and the power
control. Therefore, UEs might experience good SINR in PDSCH, while bad SINR in PDCCH channel. For more
details about these methods please refer to CQI feedback. Also, it does not matter if the DL control
error model is disabled, a UE can still detect the RLF since the SINR based on the control channel is
reported to the LteUePhy class, using a callback hooked in LteHelper while installing a UE device.
The RLF detection starts once the RRC connection is established between UE and eNodeB, i.e., UE is in
“CONNECTED_NORMALLY” state; upon which the RLF parameters are configured (see
LteUePhy::DoConfigureRadioLinkFailureDetection). In real networks, these parameters are transmitted by
the eNB using IE UE-TimersAndConstants or RLF-TimersAndConstants. However, for the sake of
simplification, in the simulator they are presented as the attributes of the LteUePhy and LteUeRrc
classes. Moreover, what concerns the carrier aggregation, i.e., when a UE is configured with multiple
component carriers, the RLF detection is only performed by the primary component carrier, i.e. component
carrier id 0 (see LteUePhy::DoNotifyConnectionSuccessful). In LteUePhy class, CQI calculation is
triggered for every downlink subframe received, and the average SINR value is measured across all
resource blocks. For the RLF detection, these SINR values are averaged over a downlink frame and if the
result is less than a defined threshold Qout (default: -5dB), the frame cannot be decoded
(see``LteUePhy::RadioLinkFailureDetection``). The Qout threshold corresponds to 10% block error rate
(BLER) of a hypothetical PDCCH transmission taking into account the PCFICH errors [R4-081920] (also refer
to Control Channels PHY Error Model). Once, the UE is unable to decode 20 consecutive frames, i.e., the
Qout evaluation period (200ms) is reached, an out-of-sync indication is sent to the UE RRC layer (see
LteUeRrc::DoNotifyOutOfSync). Else, the counter for the unsuccessfully decoded frames is reset to zero.
At the LteUeRrc, when the number of consecutive out-of-sync indications matches with the value of N310
parameter, the T310 timer is started and LteUePhy is notified to start measuring for in-sync indications
(see LteUePhy::DoStartInSnycDetection). We note that, the UE RRC state is not changed till the expiration
of T310 timer. If the resultant SINR values averaged over a downlink frame is greater than a defined
threshold Qin (default: -3.8dB), the frame is considered to be successfully decoded. Qin corresponds to
2% BLER [R4-081920] of a hypothetical PDCCH transmission taking into account the PCFICH errors. Once the
UE is able to decode 10 consecutive frames, an in-sync indication is sent to the UE RRC layer (see
LteUeRrc::DoNotifyInSync). Else, the counter for the successfully decoded frames is reset to zero. If
prior to the T310 timer expiry, the number of consecutive in-sync indications matches with N311 parameter
of LteUeRRC, the UE is considered back in-sync. At this stage, the related parameters are reset to
initiate the radio link failure detection from the beginning (see
LteUePhy::DoConfigureRadioLinkFailureDetection). On the other hand, If the T310 timer expires, the UE
considers that a RLF has occurred (see LteUeRrc::RadioLinkFailureDetected).
Actions upon RLF
Once the T310 timer is expired, a UE is considered to be in RLF; upon which the UE RRC:
• Sends a request to the eNB RRC to remove the UE context
• Moves to “CONNECTED_PHY_PROBLEM” state
• Notifies the UE NAS layer about the release of RRC connection.
Then, after getting the notification from the UE RRC the NAS does the following:
• Delete all the TFTs
• Reset the bearer counter
• Restore the bearer list, which is used to activate the bearers for the next RRC connection. This
restoration of the bearers is achieved by maintaining an additional list, i.e.,
m_bearersToBeActivatedListForReconnection in EpcUeNas class
• Switch the NAS state to OFF by calling EpcUeNas::Disconnect
• Tells the UE RRC to disconnect
The UE RRC, upon receiving the call to disconnect from the EpcUeNas class, performs the action as
specified by [TS36331] 5.3.11.3, and finally leaves the connected state, i.e., its RRC state is changed
from “CONNECTED_PHY_PROBLEM” to “IDLE_START” to perform cell selection as shown in figures UE RRC State
Machine and UE procedures after radio link failure.
At this stage, the LTE module does not support the paging functionality, therefore, to allow a UE to read
SIB2 message after camping on a suitable cell after RLF, a work around is used in
LteUeRrc::EvaluateCellForSelection method. As per this workaround, the UE RRC invokes the call to
LteUeRrc::DoConnect method, which enables the UE to switch its state from “IDLE_CAMPED_NORMALLY” to
“IDLE_WAIT_SIB2”, thus, allowing it to perform the random access.
[image] UE procedures after radio link failure.UNINDENT
The eNB RRC, after receiving the notification from the UE RRC starts the procedure of UE context
deletion, which also involves the deletion of the UE context removal from the EPC UE context removal
from EPC and the eNB stack UE context removal from eNB stack. We note that, the UE context at the MME
is not removed since, bearers are only added at the start of a simulation in MME, and cannot be added
again unless scheduled for addition during a simulation.
[image] UE context removal from EPC.UNINDENT
[image] UE context removal from eNB stack.UNINDENT
UE RRC Measurements Model
UE RRC measurements support
The UE RRC entity provides support for UE measurements; in particular, it implements the procedures
described in Section 5.5 of [TS36331], with the following simplifying assumptions:
• only E-UTRA intra-frequency measurements are supported, which implies:
• only one measurement object is used during the simulation;
• measurement gaps are not needed to perform the measurements;
• Event B1 and B2 are not implemented;
• only reportStrongestCells purpose is supported, while reportCGI and reportStrongestCellsForSON
purposes are not supported;
• s-Measure is not supported;
• carrier aggregation is now supported in the LTE module - Event A6 is not implemented;
• speed dependent scaling of time-to-trigger (Section 5.5.6.2 of [TS36331]) is not supported.
Overall design
The model is based on the concept of UE measurements consumer, which is an entity that may request an
eNodeB RRC entity to provide UE measurement reports. Consumers are, for example, Handover algorithm,
which compute handover decision based on UE measurement reports. Test cases and user’s programs may also
become consumers. Figure Relationship between UE measurements and its consumers depicts the relationship
between these entities.
[image] Relationship between UE measurements and its consumers.UNINDENT
The whole UE measurements function at the RRC level is divided into 4 major parts:
1. Measurement configuration (handled by LteUeRrc::ApplyMeasConfig)
2. Performing measurements (handled by LteUeRrc::DoReportUeMeasurements)
3. Measurement report triggering (handled by LteUeRrc::MeasurementReportTriggering)
4. Measurement reporting (handled by LteUeRrc::SendMeasurementReport)
The following sections will describe each of the parts above.
Measurement configuration
An eNodeB RRC entity configures UE measurements by sending the configuration parameters to the UE RRC
entity. This set of parameters are defined within the MeasConfig Information Element (IE) of the RRC
Connection Reconfiguration message (RRC connection reconfiguration).
The eNodeB RRC entity implements the configuration parameters and procedures described in Section 5.5.2
of [TS36331], with the following simplifying assumption:
• configuration (i.e. addition, modification, and removal) can only be done before the simulation
begins;
• all UEs attached to the eNodeB will be configured the same way, i.e. there is no support for
configuring specific measurement for specific UE; and
• it is assumed that there is a one-to-one mapping between the PCI and the E-UTRAN Global Cell
Identifier (EGCI). This is consistent with the PCI modeling assumptions described in UE PHY
Measurements Model.
The eNodeB RRC instance here acts as an intermediary between the consumers and the attached UEs. At the
beginning of simulation, each consumer provides the eNodeB RRC instance with the UE measurements
configuration that it requires. After that, the eNodeB RRC distributes the configuration to attached
UEs.
Users may customize the measurement configuration using several methods. Please refer to Section
sec-configure-ue-measurements of the User Documentation for the description of these methods.
Performing measurements
UE RRC receives both RSRP and RSRQ measurements on periodical basis from UE PHY, as described in UE PHY
Measurements Model. Layer 3 filtering will be applied to these received measurements. The implementation
of the filtering follows Section 5.5.3.2 of [TS36331]:
F_n = (1 - a) \times F_{n-1} + a \times M_n
where:
• M_n is the latest received measurement result from the physical layer;
• F_n is the updated filtered measurement result;
• F_{n-1} is the old filtered measurement result, where F_0 = M_1 (i.e. the first measurement is not
filtered); and
• a = (\frac{1}{2})^{\frac{k}{4}}, where k is the configurable filterCoefficent provided by the
QuantityConfig;
k = 4 is the default value, but can be configured by setting the RsrpFilterCoefficient and
RsrqFilterCoefficient attributes in LteEnbRrc.
Therefore k = 0 will disable Layer 3 filtering. On the other hand, past measurements can be granted more
influence on the filtering results by using larger value of k.
Measurement reporting triggering
In this part, UE RRC will go through the list of active measurement configuration and check whether the
triggering condition is fulfilled in accordance with Section 5.5.4 of [TS36331]. When at least one
triggering condition from all the active measurement configuration is fulfilled, the measurement
reporting procedure (described in the next subsection) will be initiated.
3GPP defines two kinds of triggerType: periodical and event-based. At the moment, only event-based
criterion is supported. There are various events that can be selected, which are briefly described in the
table below:
List of supported event-based triggering criteria
┌─────────┬───────────────────────────────────────┐
│Name │ Description │
├─────────┼───────────────────────────────────────┤
│Event A1 │ Serving cell becomes better than │
│ │ threshold │
├─────────┼───────────────────────────────────────┤
│Event A2 │ Serving cell becomes worse than │
│ │ threshold │
├─────────┼───────────────────────────────────────┤
│Event A3 │ Neighbour becomes offset dB better │
│ │ than serving cell │
├─────────┼───────────────────────────────────────┤
│Event A4 │ Neighbour becomes better than │
│ │ threshold │
├─────────┼───────────────────────────────────────┤
│Event A5 │ Serving becomes worse than threshold1 │
│ │ AND neighbour becomes better than │
│ │ threshold2 │
└─────────┴───────────────────────────────────────┘
Two main conditions to be checked in an event-based trigger are the entering condition and the leaving
condition. More details on these two can be found in Section 5.5.4 of [TS36331].
An event-based trigger can be further configured by introducing hysteresis and time-to-trigger.
Hysteresis (Hys) defines the distance between the entering and leaving conditions in dB. Similarly,
time-to-trigger introduces delay to both entering and leaving conditions, but as a unit of time.
The periodical type of reporting trigger is not supported, but its behavior can be easily obtained by
using an event-based trigger. This can be done by configuring the measurement in such a way that the
entering condition is always fulfilled, for example, by setting the threshold of Event A1 to zero (the
minimum level). As a result, the measurement reports will always be triggered at every certain interval,
as determined by the reportInterval field within LteRrcSap::ReportConfigEutra, therefore producing the
same behaviour as periodical reporting.
As a limitation with respect to 3GPP specifications, the current model does not support any cell-specific
configuration. These configuration parameters are defined in measurement object. As a consequence,
incorporating a list of black cells into the triggering process is not supported. Moreover, cell-specific
offset (i.e., O_{cn} and O_{cp} in Event A3, A4, and A5) are not supported as well. The value equal to
zero is always assumed in place of them.
Measurement reporting
This part handles the submission of measurement report from the UE RRC entity to the serving eNodeB
entity via RRC protocol. Several simplifying assumptions have been adopted:
• reportAmount is not applicable (i.e. always assumed to be infinite);
• in measurement reports, the reportQuantity is always assumed to be BOTH, i.e., both RSRP and RSRQ
are always reported, regardless of the triggerQuantity.
Handover
The RRC model supports UE mobility in CONNECTED mode by invoking the X2-based handover procedure. The
model is intra-EUTRAN and intra-frequency, as based on Section 10.1.2.1 of [TS36300].
This section focuses on the process of triggering a handover. The handover execution procedure itself is
covered in Section X2.
There are two ways to trigger the handover procedure:
• explicitly (or manually) triggered by the simulation program by scheduling an execution of the
method LteEnbRrc::SendHandoverRequest; or
• automatically triggered by the eNodeB RRC entity based on UE measurements and according to the
selected handover algorithm.
Section sec-x2-based-handover of the User Documentation provides some examples on using both explicit and
automatic handover triggers in simulation. The next subsection will take a closer look on the automatic
method, by describing the design aspects of the handover algorithm interface and the available handover
algorithms.
Handover algorithm
Handover in 3GPP LTE has the following properties:
•
UE-assisted
The UE provides input to the network in the form of measurement reports. This is handled by
the UE RRC Measurements Model.
•
Network-controlled
The network (i.e. the source eNodeB and the target eNodeB) decides when to trigger the
handover and oversees its execution.
The handover algorithm operates at the source eNodeB and is responsible in making handover decisions in
an “automatic” manner. It interacts with an eNodeB RRC instance via the Handover Management SAP
interface. These relationships are illustrated in Figure Relationship between UE measurements and its
consumers from the previous section.
The handover algorithm interface consists of the following methods:
•
AddUeMeasReportConfigForHandover
(Handover Algorithm -> eNodeB RRC) Used by the handover algorithm to request measurement
reports from the eNodeB RRC entity, by passing the desired reporting configuration. The
configuration will be applied to all future attached UEs.
•
ReportUeMeas
(eNodeB RRC -> Handover Algorithm) Based on the UE measurements configured earlier in
AddUeMeasReportConfigForHandover, UE may submit measurement reports to the eNodeB. The eNodeB
RRC entity uses the ReportUeMeas interface to forward these measurement reports to the
handover algorithm.
•
TriggerHandover
(Handover Algorithm -> eNodeB RRC) After examining the measurement reports (but not
necessarily), the handover algorithm may declare a handover. This method is used to notify
the eNodeB RRC entity about this decision, which will then proceed to commence the handover
procedure.
One note for the AddUeMeasReportConfigForHandover. The method will return the measId (measurement
identity) of the newly created measurement configuration. Typically a handover algorithm would store this
unique number. It may be useful in the ReportUeMeas method, for example when more than one configuration
has been requested and the handover algorithm needs to differentiate incoming reports based on the
configuration that triggered them.
A handover algorithm is implemented by writing a subclass of the LteHandoverAlgorithm abstract superclass
and implementing each of the above mentioned SAP interface methods. Users may develop their own handover
algorithm this way, and then use it in any simulation by following the steps outlined in Section
sec-x2-based-handover of the User Documentation.
Alternatively, users may choose to use one of the 3 built-in handover algorithms provided by the LTE
module: no-op, A2-A4-RSRQ, and strongest cell handover algorithm. They are ready to be used in
simulations or can be taken as an example of implementing a handover algorithm. Each of these built-in
algorithms is covered in each of the following subsections.
No-op handover algorithm
The no-op handover algorithm (NoOpHandoverAlgorithm class) is the simplest possible implementation of
handover algorithm. It basically does nothing, i.e., does not call any of the Handover Management SAP
interface methods. Users may choose this handover algorithm if they wish to disable automatic handover
trigger in their simulation.
A2-A4-RSRQ handover algorithm
The A2-A4-RSRQ handover algorithm provides the functionality of the default handover algorithm originally
included in LENA M6 (ns-3.18), ported to the Handover Management SAP interface as the
A2A4RsrqHandoverAlgorithm class.
As the name implies, the algorithm utilizes the Reference Signal Received Quality (RSRQ) measurements
acquired from Event A2 and Event A4. Thus, the algorithm will add 2 measurement configuration to the
corresponding eNodeB RRC instance. Their intended use are described as follows:
• Event A2 (serving cell’s RSRQ becomes worse than threshold) is leveraged to indicate that the UE is
experiencing poor signal quality and may benefit from a handover.
• Event A4 (neighbour cell’s RSRQ becomes better than threshold) is used to detect neighbouring cells
and acquire their corresponding RSRQ from every attached UE, which are then stored internally by the
algorithm. By default, the algorithm configures Event A4 with a very low threshold, so that the
trigger criteria are always true.
Figure A2-A4-RSRQ handover algorithm below summarizes this procedure.
[image] A2-A4-RSRQ handover algorithm.UNINDENT
Two attributes can be set to tune the algorithm behaviour:
•
ServingCellThreshold
The threshold for Event A2, i.e. a UE must have an RSRQ lower than this threshold to be
considered for a handover.
•
NeighbourCellOffset
The offset that aims to ensure that the UE would receive better signal quality after the
handover. A neighbouring cell is considered as a target cell for the handover only if its
RSRQ is higher than the serving cell’s RSRQ by the amount of this offset.
The value of both attributes are expressed as RSRQ range (Section 9.1.7 of [TS36133]), which is an
integer between 0 and 34, with 0 as the lowest RSRQ.
Strongest cell handover algorithm
The strongest cell handover algorithm, or also sometimes known as the traditional power budget (PBGT)
algorithm, is developed using [Dimou2009] as reference. The idea is to provide each UE with the best
possible Reference Signal Received Power (RSRP). This is done by performing a handover as soon as a
better cell (i.e. with stronger RSRP) is detected.
Event A3 (neighbour cell’s RSRP becomes better than serving cell’s RSRP) is chosen to realize this
concept. The A3RsrpHandoverAlgorithm class is the result of the implementation. Handover is triggered for
the UE to the best cell in the measurement report.
A simulation which uses this algorithm is usually more vulnerable to ping-pong handover (consecutive
handover to the previous source eNodeB within short period of time), especially when the Fading Model is
enabled. This problem is typically tackled by introducing a certain delay to the handover. The algorithm
does this by including hysteresis and time-to-trigger parameters (Section 6.3.5 of [TS36331]) to the UE
measurements configuration.
Hysteresis (a.k.a. handover margin) delays the handover in regard of RSRP. The value is expressed in dB,
ranges between 0 to 15 dB, and have a 0.5 dB accuracy, e.g., an input value of 2.7 dB is rounded to 2.5
dB.
On the other hand, time-to-trigger delays the handover in regard of time. 3GPP defines 16 valid values
for time-to-trigger (all in milliseconds): 0, 40, 64, 80, 100, 128, 160, 256, 320, 480, 512, 640, 1024,
1280, 2560, and 5120.
The difference between hysteresis and time-to-trigger is illustrated in Figure Effect of hysteresis and
time-to-trigger in strongest cell handover algorithm below, which is taken from the
lena-x2-handover-measures example. It depicts the perceived RSRP of serving cell and a neighbouring cell
by a UE which moves pass the border of the cells.
[image] Effect of hysteresis and time-to-trigger in strongest cell handover algorithm.UNINDENT
By default, the algorithm uses a hysteresis of 3.0 dB and time-to-trigger of 256 ms. These values can
be tuned through the Hysteresis and TimeToTrigger attributes of the A3RsrpHandoverAlgorithm class.
Neighbour Relation
LTE module supports a simplified Automatic Neighbour Relation (ANR) function. This is handled by the
LteAnr class, which interacts with an eNodeB RRC instance through the ANR SAP interface.
Neighbour Relation Table
The ANR holds a Neighbour Relation Table (NRT), similar to the description in Section 22.3.2a of
[TS36300]. Each entry in the table is called a Neighbour Relation (NR) and represents a detected
neighbouring cell, which contains the following boolean fields:
•
No Remove
Indicates that the NR shall not be removed from the NRT. This is true by default for
user-provided NR and false otherwise.
•
No X2 Indicates that the NR shall not use an X2 interface in order to initiate procedures towards
the eNodeB parenting the target cell. This is false by default for user-provided NR, and true
otherwise.
•
No HO Indicates that the NR shall not be used by the eNodeB for handover reasons. This is true in
most cases, except when the NR is both user-provided and network-detected.
Each NR entry may have at least one of the following properties:
•
User-provided
This type of NR is created as instructed by the simulation user. For example, a NR is created
automatically upon a user-initiated establishment of X2 connection between 2 eNodeBs, e.g. as
described in Section sec-x2-based-handover. Another way to create a user-provided NR is to
call the AddNeighbourRelation function explicitly.
•
Network-detected
This type of NR is automatically created during the simulation as a result of the discovery
of a nearby cell.
In order to automatically create network-detected NR, ANR utilizes UE measurements. In other words, ANR
is a consumer of UE measurements, as depicted in Figure Relationship between UE measurements and its
consumers. RSRQ and Event A4 (neighbour becomes better than threshold) are used for the reporting
configuration. The default Event A4 threshold is set to the lowest possible, i.e., maximum detection
capability, but can be changed by setting the Threshold attribute of LteAnr class. Note that the
A2-A4-RSRQ handover algorithm also utilizes a similar reporting configuration. Despite the similarity,
when both ANR and this handover algorithm are active in the eNodeB, they use separate reporting
configuration.
Also note that automatic setup of X2 interface is not supported. This is the reason why the No X2 and No
HO fields are true in a network-detected but not user-detected NR.
Role of ANR in Simulation
The ANR SAP interface provides the means of communication between ANR and eNodeB RRC. Some interface
functions are used by eNodeB RRC to interact with the NRT, as shown below:
•
AddNeighbourRelation
(eNodeB RRC -> ANR) Add a new user-provided NR entry into the NRT.
•
GetNoRemove
(eNodeB RRC -> ANR) Get the value of No Remove field of an NR entry of the given cell ID.
•
GetNoHo
(eNodeB RRC -> ANR) Get the value of No HO field of an NR entry of the given cell ID.
•
GetNoX2
(eNodeB RRC -> ANR) Get the value of No X2 field of an NR entry of the given cell ID.
Other interface functions exist to support the role of ANR as a UE measurements consumer, as listed
below:
•
AddUeMeasReportConfigForAnr
(ANR -> eNodeB RRC) Used by the ANR to request measurement reports from the eNodeB RRC
entity, by passing the desired reporting configuration. The configuration will be applied to
all future attached UEs.
•
ReportUeMeas
(eNodeB RRC -> ANR) Based on the UE measurements configured earlier in
AddUeMeasReportConfigForAnr, UE may submit measurement reports to the eNodeB. The eNodeB RRC
entity uses the ReportUeMeas interface to forward these measurement reports to the ANR.
Please refer to the corresponding API documentation for LteAnrSap class for more details on the usage and
the required parameters.
The ANR is utilized by the eNodeB RRC instance as a data structure to keep track of the situation of
nearby neighbouring cells. The ANR also helps the eNodeB RRC instance to determine whether it is possible
to execute a handover procedure to a neighbouring cell. This is realized by the fact that eNodeB RRC will
only allow a handover procedure to happen if the NR entry of the target cell has both No HO and No X2
fields set to false.
ANR is enabled by default in every eNodeB instance in the simulation. It can be disabled by setting the
AnrEnabled attribute in LteHelper class to false.
RRC sequence diagrams
In this section we provide some sequence diagrams that explain the most important RRC procedures being
modeled.
RRC connection establishment
Figure Sequence diagram of the RRC Connection Establishment procedure shows how the RRC Connection
Establishment procedure is modeled, highlighting the role of the RRC layer at both the UE and the eNB, as
well as the interaction with the other layers.
[image] Sequence diagram of the RRC Connection Establishment procedure.UNINDENT
There are several timeouts related to this procedure, which are listed in the following Table Timers in
RRC connection establishment procedure. If any of these timers expired, the RRC connection
establishment procedure is terminated in failure. At the UE side, if T300 timer has expired a
consecutive connEstFailCount times on the same cell it performs the cell selection again [TS36331].
Else, the upper layer (UE NAS) will immediately attempt to retry the procedure.
Timers in RRC connection establishment procedure
┌─────────────────┬────────────┬──────────────────┬──────────────────┬──────────────────┬──────────────────┐
│Name │ Location │ Timer starts │ Timer stops │ Default duration │ When timer │
│ │ │ │ │ │ expired │
├─────────────────┼────────────┼──────────────────┼──────────────────┼──────────────────┼──────────────────┤
│Connection │ eNodeB RRC │ New UE context │ Receive RRC │ 15 ms (Max) │ Remove UE │
│request timeout │ │ added │ CONNECTION │ │ context │
│ │ │ │ REQUEST │ │ │
├─────────────────┼────────────┼──────────────────┼──────────────────┼──────────────────┼──────────────────┤
│Connection │ UE RRC │ Send RRC │ Receive RRC │ 100 ms │ Reset UE MAC │
│timeout (T300 │ │ CONNECTION │ CONNECTION SETUP │ │ │
│timer) │ │ REQUEST │ or REJECT │ │ │
├─────────────────┼────────────┼──────────────────┼──────────────────┼──────────────────┼──────────────────┤
│Connection setup │ eNodeB RRC │ Send RRC │ Receive RRC │ 100 ms │ Remove UE │
│timeout │ │ CONNECTION SETUP │ CONNECTION SETUP │ │ context │
│ │ │ │ COMPLETE │ │ │
├─────────────────┼────────────┼──────────────────┼──────────────────┼──────────────────┼──────────────────┤
│Connection │ eNodeB RRC │ Send RRC │ Never │ 30 ms │ Remove UE │
│rejected timeout │ │ CONNECTION │ │ │ context │
│ │ │ REJECT │ │ │ │
└─────────────────┴────────────┴──────────────────┴──────────────────┴──────────────────┴──────────────────┘
Note: The value of connection request timeout timer at the eNB RRC should not be higher than the T300
timer at UE RRC. It is to make sure that the UE context is already removed at the eNB, once the UE will
perform cell selection upon reaching the connEstFailCount count. Moreover, at the time of writing this
document the Cell Selection Evaluation does not include the Qoffset_{temp} parameter, thus, it is not
applied while selecting the same cell again.
Counters in RRC connection establishment procedure
┌─────────────────┬──────────┬──────────────────┬──────────────┬───────────────┬────────────────┬────────────────┐
│Name │ Location │ Msg │ Monitored by │ Default value │ Limit not │ Limit reached │
│ │ │ │ │ │ reached │ │
├─────────────────┼──────────┼──────────────────┼──────────────┼───────────────┼────────────────┼────────────────┤
│ConnEstFailCount │ eNB MAC │ RachConfigCommon │ UE RRC │ 1 │ Increment the │ Reset the │
│ │ │ in SIB2, HO │ │ │ local counter. │ local counter │
│ │ │ REQ and HO Ack │ │ │ Invalided the │ and perform │
│ │ │ │ │ │ prev SIB2 msg, │ cell │
│ │ │ │ │ │ and try random │ selection. │
│ │ │ │ │ │ access with │ │
│ │ │ │ │ │ the same cell. │ │
└─────────────────┴──────────┴──────────────────┴──────────────┴───────────────┴────────────────┴────────────────┘
RRC connection reconfiguration
Figure Sequence diagram of the RRC Connection Reconfiguration procedure shows how the RRC Connection
Reconfiguration procedure is modeled for the case where MobilityControlInfo is not provided, i.e.,
handover is not performed.
[image] Sequence diagram of the RRC Connection Reconfiguration procedure.UNINDENT
Figure Sequence diagram of the RRC Connection Reconfiguration procedure for the handover case shows how
the RRC Connection Reconfiguration procedure is modeled for the case where MobilityControlInfo is
provided, i.e., handover is to be performed. As specified in [TS36331], After receiving the handover
message, the UE attempts to access the target cell at the first available RACH occasion according to
Random Access resource selection defined in [TS36321]_, i.e. the handover is asynchronous.
Consequently, when allocating a dedicated preamble for the random access in the target cell, E-UTRA
shall ensure it is available from the first RACH occasion the UE may use. Upon successful completion of
the handover, the UE sends a message used to confirm the handover. Note that the random access
procedure in this case is non-contention based, hence in a real LTE system it differs slightly from the
one used in RRC connection established. Also note that the RA Preamble ID is signaled via the Handover
Command included in the X2 Handover Request ACK message sent from the target eNB to the source eNB; in
particular, the preamble is included in the RACH-ConfigDedicated IE which is part of
MobilityControlInfo.
[image] Sequence diagram of the RRC Connection Reconfiguration procedure for the handover case.UNINDENT
RRC protocol models
As previously anticipated, we provide two different models for the transmission and reception of RRC
messages: Ideal and Real. Each of them is described in one of the following subsections.
Ideal RRC protocol model
According to this model, implemented in the classes and LteUeRrcProtocolIdeal and LteEnbRrcProtocolIdeal,
all RRC messages and information elements are transmitted between the eNB and the UE in an ideal fashion,
without consuming radio resources and without errors. From an implementation point of view, this is
achieved by passing the RRC data structure directly between the UE and eNB RRC entities, without
involving the lower layers (PDCP, RLC, MAC, scheduler).
Real RRC protocol model
This model is implemented in the classes LteUeRrcProtocolReal and LteEnbRrcProtocolReal and aims at
modeling the transmission of RRC PDUs as commonly performed in real LTE systems. In particular:
• for every RRC message being sent, a real RRC PDUs is created following the ASN.1 encoding of RRC
PDUs and information elements (IEs) specified in [TS36331]. Some simplification are made with
respect to the IEs included in the PDU, i.e., only those IEs that are useful for simulation purposes
are included. For a detailed list, please see the IEs defined in lte-rrc-sap.h and compare with
[TS36331].
• the encoded RRC PDUs are sent on Signaling Radio Bearers and are subject to the same transmission
modeling used for data communications, thus including scheduling, radio resource consumption,
channel errors, delays, retransmissions, etc.
Signaling Radio Bearer model
We now describe the Signaling Radio Bearer model that is used for the Real RRC protocol model.
• SRB0 messages (over CCCH):
• RrcConnectionRequest: in real LTE systems, this is an RLC TM SDU sent over resources specified in
the UL Grant in the RAR (not in UL DCIs); the reason is that C-RNTI is not known yet at this
stage. In the simulator, this is modeled as a real RLC TM RLC PDU whose UL resources are allocated
by the scheduler upon call to SCHED_DL_RACH_INFO_REQ.
• RrcConnectionSetup: in the simulator this is implemented as in real LTE systems, i.e., with an RLC
TM SDU sent over resources indicated by a regular UL DCI, allocated with SCHED_DL_RLC_BUFFER_REQ
triggered by the RLC TM instance that is mapped to LCID 0 (the CCCH).
• SRB1 messages (over DCCH):
• All the SRB1 messages modeled in the simulator (e.g., RrcConnectionCompleted) are implemented as
in real LTE systems, i.e., with a real RLC SDU sent over RLC AM using DL resources allocated via
Buffer Status Reports. See the RLC model documentation for details.
• SRB2 messages (over DCCH):
• According to [TS36331], “SRB1 is for RRC messages (which may include a piggybacked NAS
message) as well as for NAS messages prior to the establishment of SRB2, all using DCCH
logical channel”, whereas “SRB2 is for NAS messages, using DCCH logical channel” and “SRB2 has
a lower-priority than SRB1 and is always configured by E-UTRAN after security activation”.
Modeling security-related aspects is not a requirement of the LTE simulation model, hence we
always use SRB1 and never activate SRB2.
ASN.1 encoding of RRC IE’s
The messages defined in RRC SAP, common to all Ue/Enb SAP Users/Providers, are transported in a
transparent container to/from a Ue/Enb. The encoding format for the different Information Elements are
specified in [TS36331], using ASN.1 rules in the unaligned variant. The implementation in Ns3/Lte has
been divided in the following classes:
• Asn1Header : Contains the encoding / decoding of basic ASN types
• RrcAsn1Header : Inherits Asn1Header and contains the encoding / decoding of common IE’s defined in
[TS36331]
• Rrc specific messages/IEs classes : A class for each of the messages defined in RRC SAP header
Asn1Header class - Implementation of base ASN.1 types
This class implements the methods to Serialize / Deserialize the ASN.1 types being used in [TS36331],
according to the packed encoding rules in ITU-T X.691. The types considered are:
• Boolean : a boolean value uses a single bit (1=true, 0=false).
• Integer : a constrained integer (with min and max values defined) uses the minimum amount of bits to
encode its range (max-min+1).
• Bitstring : a bistring will be copied bit by bit to the serialization buffer.
• Octetstring : not being currently used.
• Sequence : the sequence generates a preamble indicating the presence of optional and default fields.
It also adds a bit indicating the presence of extension marker.
• Sequence…Of : the sequence…of type encodes the number of elements of the sequence as an integer (the
subsequent elements will need to be encoded afterwards).
• Choice : indicates which element among the ones in the choice set is being encoded.
• Enumeration : is serialized as an integer indicating which value is used, among the ones in the
enumeration, with the number of elements in the enumeration as upper bound.
• Null : the null value is not encoded, although its serialization function is defined to provide a
clearer map between specification and implementation.
The class inherits from ns-3 Header, but Deserialize() function is declared pure virtual, thus inherited
classes having to implement it. The reason is that deserialization will retrieve the elements in RRC
messages, each of them containing different information elements.
Additionally, it has to be noted that the resulting byte length of a specific type/message can vary,
according to the presence of optional fields, and due to the optimized encoding. Hence, the serialized
bits will be processed using PreSerialize() function, saving the result in m_serializationResult Buffer.
As the methods to read/write in a ns3 buffer are defined in a byte basis, the serialization bits are
stored into m_serializationPendingBits attribute, until the 8 bits are set and can be written to buffer
iterator. Finally, when invoking Serialize(), the contents of the m_serializationResult attribute will be
copied to Buffer::Iterator parameter
RrcAsn1Header : Common IEs
As some Information Elements are being used for several RRC messages, this class implements the following
common IE’s:
• SrbToAddModList
• DrbToAddModList
• LogicalChannelConfig
• RadioResourceConfigDedicated
• PhysicalConfigDedicated
• SystemInformationBlockType1
• SystemInformationBlockType2
• RadioResourceConfigCommonSIB
Rrc specific messages/IEs classes
The following RRC SAP have been implemented:
• RrcConnectionRequest
• RrcConnectionSetup
• RrcConnectionSetupCompleted
• RrcConnectionReconfiguration
• RrcConnectionReconfigurationCompleted
• HandoverPreparationInfo
• RrcConnectionReestablishmentRequest
• RrcConnectionReestablishment
• RrcConnectionReestablishmentComplete
• RrcConnectionReestablishmentReject
• RrcConnectionRelease
NAS
The focus of the LTE-EPC model is on the NAS Active state, which corresponds to EMM Registered, ECM
connected, and RRC connected. Because of this, the following simplifications are made:
• EMM and ECM are not modeled explicitly; instead, the NAS entity at the UE will interact directly with
the MME to perform actions that are equivalent (with gross simplifications) to taking the UE to the
states EMM Connected and ECM Connected;
• the NAS also takes care of multiplexing uplink data packets coming from the upper layers into the
appropriate EPS bearer by using the Traffic Flow Template classifier (TftClassifier).
• the NAS does not support PLMN and CSG selection
• the NAS does not support any location update/paging procedure in idle mode
Figure Sequence diagram of the attach procedure shows how the simplified NAS model implements the attach
procedure. Note that both the default and eventual dedicated EPS bearers are activated as part of this
procedure.
[image] Sequence diagram of the attach procedure.UNINDENT
S1, S5 and S11
S1-U and S5 (user plane)
The S1-U and S5 interfaces are modeled in a realistic way by encapsulating data packets over GTP/UDP/IP,
as done in real LTE-EPC systems. The corresponding protocol stack is shown in Figure LTE-EPC data plane
protocol stack. As shown in the figure, there are two different layers of IP networking. The first one is
the end-to-end layer, which provides end-to-end connectivity to the users; this layer involves the UEs,
the PGW and the remote host (including eventual internet routers and hosts in between), but does not
involve the eNB and the SGW. In this version of LTE, the EPC supports both IPv4 and IPv6 type users. The
3GPP unique 64 bit IPv6 prefix allocation process for each UE and PGW is followed here. Each EPC is
assigned a unique 16 bit IPv4 and a 48 bit IPv6 network address from the pool of 7.0.0.0/8 and
7777:f00d::/32 respectively. In the end-to-end IP connection between UE and PGW, all addresses are
configured using these prefixes. The PGW’s address is used by all UEs as the gateway to reach the
internet.
The second layer of IP networking is the EPC local area network. This involves all eNB nodes, SGW nodes
and PGW nodes. This network is implemented as a set of point-to-point links which connect each eNB with
its corresponding SGW node and a point-to-point link which connect each SGW node with its corresponding
PGW node; thus, each SGW has a set of point-to-point devices, each providing connectivity to a different
eNB. By default, a 10.x.y.z/30 subnet is assigned to each point-to-point link (a /30 subnet is the
smallest subnet that allows for two distinct host addresses).
As specified by 3GPP, the end-to-end IP communications is tunneled over the local EPC IP network using
GTP/UDP/IP. In the following, we explain how this tunneling is implemented in the EPC model. The
explanation is done by discussing the end-to-end flow of data packets.
[image] Data flow in the downlink between the internet and the UE.UNINDENT
To begin with, we consider the case of the downlink, which is depicted in Figure Data flow in the
downlink between the internet and the UE. Downlink IPv4/IPv6 packets are generated from a generic
remote host, and addressed to one of the UE device. Internet routing will take care of forwarding the
packet to the generic NetDevice of the PGW node which is connected to the internet (this is the Gi
interface according to 3GPP terminology). The PGW has a VirtualNetDevice which is assigned the base
IPv4 address of the EPC network; hence, static routing rules will cause the incoming packet from the
internet to be routed through this VirtualNetDevice. In case of IPv6 address as destination, a manual
route towards the VirtualNetDevice is inserted in the routing table, containing the 48 bit IPv6 prefix
from which all the IPv6 addresses of the UEs and PGW are configured. Such device starts the GTP/UDP/IP
tunneling procedure, by forwarding the packet to a dedicated application in the PGW node which is
called EpcPgwApplication. This application does the following operations:
1. it determines the SGW node to which it must route the traffic for this UE, by looking at the IP
destination address (which is the address of the UE);
2. it classifies the packet using Traffic Flow Templates (TFTs) to identify to which EPS Bearer it
belongs. EPS bearers have a one-to-one mapping to S5 Bearers, so this operation returns the GTP-U
Tunnel Endpoint Identifier (TEID) to which the packet belongs;
3. it adds the corresponding GTP-U protocol header to the packet;
4. finally, it sends the packet over a UDP socket to the S5 point-to-point NetDevice, addressed to the
appropriate SGW.
As a consequence, the end-to-end IP packet with newly added IP, UDP and GTP headers is sent through one
of the S5 links to the SGW, where it is received and delivered locally (as the destination address of the
outermost IP header matches the SGW IP address). The local delivery process will forward the packet, via
an UDP socket, to a dedicated application called EpcSgwApplication. This application then performs the
following operations:
1. it determines the eNB node to which the UE is attached, by looking at the S5 TEID;
2. it maps the S5 TEID to get the S1 TEID. EPS bearers have a one-to-one mapping to S1-U Bearers, so
this operation returns the S1 GTP-U Tunnel Endpoint Identifier (TEID) to which the packet belongs;
3. it adds a new GTP-U protocol header to the packet;
4. finally, it sends the packet over a UDP socket to the S1-U point-to-point NetDevice, addressed to
the eNB to which the UE is attached.
Finally, the end-to-end IP packet with newly added IP, UDP and GTP headers is sent through one of the S1
links to the eNB, where it is received and delivered locally (as the destination address of the outermost
IP header matches the eNB IP address). The local delivery process will forward the packet, via an UDP
socket, to a dedicated application called EpcEnbApplication. This application then performs the following
operations:
1. it removes the GTP header and retrieves the S1 TEID which is contained in it;
2. leveraging on the one-to-one mapping between S1-U bearers and Radio Bearers (which is a 3GPP
requirement), it determines the Bearer ID (BID) to which the packet belongs;
3. it records the BID in a dedicated tag called EpsBearerTag, which is added to the packet;
4. it forwards the packet to the LteEnbNetDevice of the eNB node via a raw packet socket
Note that, at this point, the outmost header of the packet is the end-to-end IP header, since the
IP/UDP/GTP headers of the S1 protocol stack have already been stripped. Upon reception of the packet from
the EpcEnbApplication, the LteEnbNetDevice will retrieve the BID from the EpsBearerTag, and based on the
BID will determine the Radio Bearer instance (and the corresponding PDCP and RLC protocol instances)
which are then used to forward the packet to the UE over the LTE radio interface. Finally, the
LteUeNetDevice of the UE will receive the packet, and delivery it locally to the IP protocol stack, which
will in turn delivery it to the application of the UE, which is the end point of the downlink
communication.
[image] Data flow in the uplink between the UE and the internet.UNINDENT
The case of the uplink is depicted in Figure Data flow in the uplink between the UE and the internet.
Uplink IP packets are generated by a generic application inside the UE, and forwarded by the local
TCP/IP stack to the LteUeNetDevice of the UE. The LteUeNetDevice then performs the following
operations:
1. it classifies the packet using TFTs and determines the Radio Bearer to which the packet belongs
(and the corresponding RBID);
2. it identifies the corresponding PDCP protocol instance, which is the entry point of the LTE Radio
Protocol stack for this packet;
3. it sends the packet to the eNB over the LTE Radio Protocol stack.
The eNB receives the packet via its LteEnbNetDevice. Since there is a single PDCP and RLC protocol
instance for each Radio Bearer, the LteEnbNetDevice is able to determine the BID of the packet. This BID
is then recorded onto an EpsBearerTag, which is added to the packet. The LteEnbNetDevice then forwards
the packet to the EpcEnbApplication via a raw packet socket.
Upon receiving the packet, the EpcEnbApplication performs the following operations:
1. it retrieves the BID from the EpsBearerTag in the packet;
2. it determines the corresponding EPS Bearer instance and GTP-U TEID by leveraging on the one-to-one
mapping between S1-U bearers and Radio Bearers;
3. it adds a GTP-U header on the packet, including the TEID determined previously;
4. it sends the packet to the SGW node via the UDP socket connected to the S1-U point-to-point net
device.
At this point, the packet contains the S1-U IP, UDP and GTP headers in addition to the original
end-to-end IP header. When the packet is received by the corresponding S1-U point-to-point NetDevice of
the SGW node, it is delivered locally (as the destination address of the outmost IP header matches the
address of the point-to-point net device). The local delivery process will forward the packet to the
EpcSgwApplication via the corresponding UDP socket. The EpcSgwApplication then perfoms the following
operations:
1. it removes the GTP header and retrieves the S1-U TEID;
2. it maps the S1-U TEID to get the S5 TEID to which the packet belongs;
3. it determines the PGW to which it must send the packet from the TEID mapping;
4. it add a new GTP-U protocol header to the packet;
5. finally, it sends the packet over a UDP socket to the S5 point-to-point NetDevice, addressed to the
corresponding PGW.
At this point, the packet contains the S5 IP, UDP and GTP headers in addition to the original end-to-end
IP header. When the packet is received by the corresponding S5 point-to-point NetDevice of the PGW node,
it is delivered locally (as the destination address of the outmost IP header matches the address of the
point-to-point net device). The local delivery process will forward the packet to the EpcPgwApplication
via the corresponding UDP socket. The EpcPgwApplication then removes the GTP header and forwards the
packet to the VirtualNetDevice. At this point, the outmost header of the packet is the end-to-end IP
header. Hence, if the destination address within this header is a remote host on the internet, the packet
is sent to the internet via the corresponding NetDevice of the PGW. In the event that the packet is
addressed to another UE, the IP stack of the PGW will redirect the packet again to the VirtualNetDevice,
and the packet will go through the downlink delivery process in order to reach its destination UE.
Note that the EPS Bearer QoS is not enforced on the S1-U and S5 links, it is assumed that the
overprovisioning of the link bandwidth is sufficient to meet the QoS requirements of all bearers.
S1AP
The S1-AP interface provides control plane interaction between the eNB and the MME. In the simulator,
this interface is modeled in a realistic fashion transmitting the encoded S1AP messages and information
elements specified in [TS36413] on the S1-MME link.
The S1-AP primitives that are modeled are:
• INITIAL UE MESSAGE
• INITIAL CONTEXT SETUP REQUEST
• INITIAL CONTEXT SETUP RESPONSE
• PATH SWITCH REQUEST
• PATH SWITCH REQUEST ACKNOWLEDGE
S5 and S11
The S5 interface provides control plane interaction between the SGW and the PGW. The S11 interface
provides control plane interaction between the SGw and the MME. Both interfaces use the GPRS Tunneling
Protocol (GTPv2-C) to tunnel signalling messages [TS29274] and use UDP as transport protocol. In the
simulator, these interfaces and protocol are modeled in a realistic fashion transmitting the encoded
GTP-C messages.
The GTPv2-C primitives that are modeled are:
• CREATE SESSION REQUEST
• CREATE SESSION RESPONSE
• MODIFY BEARER REQUEST
• MODIFY BEARER RESPONSE
• DELETE SESSION REQUEST
• DELETE SESSION RESPONSE
• DELETE BEARER COMMAND
• DELETE BEARER REQUEST
• DELETE BEARER RESPONSE
Of these primitives, the first two are used upon initial UE attachment for the establishment of the S1-U
and S5 bearers. Section NAS shows the implementation of the attach procedure. The other primitives are
used during the handover to switch the S1-U bearers from the source eNB to the target eNB as a
consequence of the reception by the MME of a PATH SWITCH REQUEST S1-AP message.
X2
The X2 interface interconnects two eNBs [TS36420]. From a logical point of view, the X2 interface is a
point-to-point interface between the two eNBs. In a real E-UTRAN, the logical point-to-point interface
should be feasible even in the absence of a physical direct connection between the two eNBs. In the X2
model implemented in the simulator, the X2 interface is a point-to-point link between the two eNBs. A
point-to-point device is created in both eNBs and the two point-to-point devices are attached to the
point-to-point link.
For a representation of how the X2 interface fits in the overall architecture of the LENA simulation
model, the reader is referred to the figure Overview of the LTE-EPC simulation model.
The X2 interface implemented in the simulator provides detailed implementation of the following
elementary procedures of the Mobility Management functionality [TS36423]:
• Handover Request procedure
• Handover Request Acknowledgement procedure
• SN Status Transfer procedure
• UE Context Release procedure
These procedures are involved in the X2-based handover. You can find the detailed description of the
handover in section 10.1.2.1 of [TS36300]. We note that the simulator model currently supports only the
seamless handover as defined in Section 2.6.3.1 of [Sesia2009]; in particular, lossless handover as
described in Section 2.6.3.2 of [Sesia2009] is not supported at the time of this writing.
Figure Sequence diagram of the X2-based handover below shows the interaction of the entities of the X2
model in the simulator. The shaded labels indicate the moments when the UE or eNodeB transition to
another RRC state.
[image] Sequence diagram of the X2-based handover.UNINDENT
The figure also shows two timers within the handover procedure: the handover leaving timer is
maintained by the source eNodeB, while the handover joining timer by the target eNodeB. The duration of
the timers can be configured in the HandoverLeavingTimeoutDuration and HandoverJoiningTimeoutDuration
attributes of the respective LteEnbRrc instances. When one of these timers expire, the handover
procedure is considered as failed.
However, there is no proper handling of handover failure in the current version of LTE module. Users
should tune the simulation properly in order to avoid handover failure, otherwise unexpected behaviour
may occur. Please refer to Section sec-tuning-handover-simulation of the User Documentation for some
tips regarding this matter.
The X2 model is an entity that uses services from:
• the X2 interfaces,
• They are implemented as Sockets on top of the point-to-point devices.
• They are used to send/receive X2 messages through the X2-C and X2-U interfaces (i.e. the
point-to-point device attached to the point-to-point link) towards the peer eNB.
• the S1 application.
• Currently, it is the EpcEnbApplication.
• It is used to get some information needed for the Elementary Procedures of the X2 messages.
and it provides services to:
• the RRC entity (X2 SAP)
• to send/receive RRC messages. The X2 entity sends the RRC message as a transparent container in
the X2 message. This RRC message is sent to the UE.
Figure Implementation Model of X2 entity and SAPs shows the implementation model of the X2 entity and its
relationship with all the other entities and services in the protocol stack.
[image] Implementation Model of X2 entity and SAPs.UNINDENT
The RRC entity manages the initiation of the handover procedure. This is done in the Handover
Management submodule of the eNB RRC entity. The target eNB may perform some Admission Control
procedures. This is done in the Admission Control submodule. Initially, this submodule will accept any
handover request.
X2 interfaces
The X2 model contains two interfaces:
• the X2-C interface. It is the control interface and it is used to send the X2-AP PDUs (i.e. the
elementary procedures).
• the X2-U interface. It is used to send the bearer data when there is DL forwarding.
Figure X2 interface protocol stacks shows the protocol stacks of the X2-U interface and X2-C interface
modeled in the simulator.
[image] X2 interface protocol stacks.UNINDENT
X2-C
The X2-C interface is the control part of the X2 interface and it is used to send the X2-AP PDUs (i.e.
the elementary procedures).
In the original X2 interface control plane protocol stack, SCTP is used as the transport protocol but
currently, the SCTP protocol is not modeled in the ns-3 simulator and its implementation is out-of-scope
of the project. The UDP protocol is used as the datagram oriented protocol instead of the SCTP protocol.
X2-U
The X2-U interface is used to send the bearer data when there is DL forwarding during the execution of
the X2-based handover procedure. Similarly to what done for the S1-U interface, data packets are
encapsulated over GTP/UDP/IP when being sent over this interface. Note that the EPS Bearer QoS is not
enforced on the X2-U links, it is assumed that the overprovisioning of the link bandwidth is sufficient
to meet the QoS requirements of all bearers.
X2 Service Interface
The X2 service interface is used by the RRC entity to send and receive messages of the X2 procedures. It
is divided into two parts:
• the EpcX2SapProvider part is provided by the X2 entity and used by the RRC entity and
• the EpcX2SapUser part is provided by the RRC entity and used by the RRC enity.
The primitives that are supported in our X2-C model are described in the following subsections.
X2-C primitives for handover execution
The following primitives are used for the X2-based handover:
• HANDOVER REQUEST
• HANDOVER REQUEST ACK
• HANDOVER PREPARATION FAILURE
• SN STATUS STRANSFER
• UE CONTEXT RELEASE
all the above primitives are used by the currently implemented RRC model during the preparation and
execution of the handover procedure. Their usage interacts with the RRC state machine; therefore, they
are not meant to be used for code customization, at least unless it is desired to modify the RRC state
machine.
X2-C SON primitives
The following primitives can be used to implement Self-Organized Network (SON) functionalities:
• LOAD INFORMATION
• RESOURCE STATUS UPDATE
note that the current RRC model does not actually use these primitives, they are included in the model
just to make it possible to develop SON algorithms included in the RRC logic that make use of them.
As a first example, we show here how the load information primitive can be used. We assume that the
LteEnbRrc has been modified to include the following new member variables:
std::vector<EpcX2Sap::UlInterferenceOverloadIndicationItem>
m_currentUlInterferenceOverloadIndicationList;
std::vector <EpcX2Sap::UlHighInterferenceInformationItem>
m_currentUlHighInterferenceInformationList;
EpcX2Sap::RelativeNarrowbandTxBand m_currentRelativeNarrowbandTxBand;
for a detailed description of the type of these variables, we suggest to consult the file epc-x2-sap.h,
the corresponding doxygen documentation, and the references therein to the relevant sections of 3GPP TS
36.423. Now, assume that at run time these variables have been set to meaningful values following the
specifications just mentioned. Then, you can add the following code in the LteEnbRrc class implementation
in order to send a load information primitive:
EpcX2Sap::CellInformationItem cii;
cii.sourceCellId = m_cellId;
cii.ulInterferenceOverloadIndicationList = m_currentUlInterferenceOverloadIndicationList;
cii.ulHighInterferenceInformationList = m_currentUlHighInterferenceInformationList;
cii.relativeNarrowbandTxBand = m_currentRelativeNarrowbandTxBand;
EpcX2Sap::LoadInformationParams params;
params.targetCellId = cellId;
params.cellInformationList.push_back (cii);
m_x2SapProvider->SendLoadInformation (params);
The above code allows the source eNB to send the message. The method LteEnbRrc::DoRecvLoadInformation
will be called when the target eNB receives the message. The desired processing of the load information
should therefore be implemented within that method.
In the following second example we show how the resource status update primitive is used. We assume that
the LteEnbRrc has been modified to include the following new member variable:
EpcX2Sap::CellMeasurementResultItem m_cmri;
similarly to before, we refer to epc-x2-sap.h and the references therein for detailed information about
this variable type. Again, we assume that the variable has been already set to a meaningful value. Then,
you can add the following code in order to send a resource status update:
EpcX2Sap::ResourceStatusUpdateParams params;
params.targetCellId = cellId;
params.cellMeasurementResultList.push_back (m_cmri);
m_x2SapProvider->SendResourceStatusUpdate (params);
The method eEnbRrc::DoRecvResourceStatusUpdate will be called when the target eNB receives the resource
status update message. The desired processing of this message should therefore be implemented within that
method.
Finally, we note that the setting and processing of the appropriate values for the variable passed to the
above described primitives is deemed to be specific of the SON algorithm being implemented, and hence is
not covered by this documentation.
Unsupported primitives
Mobility Robustness Optimization primitives such as Radio Link Failure indication and Handover Report are
not supported at this stage.
S11
The S11 interface provides control plane interaction between the SGW and the MME using the GTPv2-C
protocol specified in [TS29274]. In the simulator, this interface is modeled in an ideal fashion, with
direct interaction between the SGW and the MME objects, without actually implementing the encoding of the
messages and without actually transmitting any PDU on any link.
The S11 primitives that are modeled are:
• CREATE SESSION REQUEST
• CREATE SESSION RESPONSE
• MODIFY BEARER REQUEST
• MODIFY BEARER RESPONSE
Of these primitives, the first two are used upon initial UE attachment for the establishment of the S1-U
bearers; the other two are used during handover to switch the S1-U bearers from the source eNB to the
target eNB as a consequence of the reception by the MME of a PATH SWITCH REQUEST S1-AP message.
Power Control
This section describes the ns-3 implementation of Downlink and Uplink Power Control.
Downlink Power Control
Since some of Frequency Reuse Algorithms require Downlink Power Control, this feature was also
implemented in ns-3.
[image] Sequence diagram of Downlink Power Control.UNINDENT
Figure Sequence diagram of Downlink Power Control shows the sequence diagram of setting downlink P_A
value for UE, highlighting the interactions between the RRC and the other entities. FR algorithm
triggers RRC to change P_A values for UE. Then RRC starts RrcConnectionReconfiguration function to
inform UE about new configuration. After successful RrcConnectionReconfiguration, RRC can set P_A value
for UE by calling function SetPa from CphySap, value is saved in new map m_paMap which contain P_A
values for each UE served by eNb.
When LteEnbPhy starts new subframe, DCI control messages are processed to get vector of used RBs. Now
also GeneratePowerAllocationMap(uint16_t rnti, int rbId) function is also called. This function check
P_A value for UE, generate power for each RB and store it in m_dlPowerAllocationMap. Then this map is
used by CreateTxPowerSpectralDensityWithPowerAllocation function to create Ptr<SpectrumValue> txPsd.
PdschConfigDedicated (TS 36.331, 6.3.2 PDSCH-Config) was added in LteRrcSap::PhysicalConfigDedicated
struct, which is used in RrcConnectionReconfiguration process.
Uplink Power Control
Uplink power control controls the transmit power of the different uplink physical channels. This
functionality is described in 3GPP TS 36.213 section 5.
Uplink Power Control is enabled by default, and can be disabled by attribute system:
Config::SetDefault ("ns3::LteUePhy::EnableUplinkPowerControl", BooleanValue (false));
Two Uplink Power Control mechanisms are implemented:
• Open Loop Uplink Power Control: the UE transmission power depends on estimation of the downlink
path-loss and channel configuration
• Closed Loop Uplink Power Control: as in Open Loop, in addition eNB can control the UE transmission
power by means of explicit Transmit Power Control TPC commands transmitted in the downlink.
To switch between these two mechanism types, one should change parameter:
Config::SetDefault ("ns3::LteUePowerControl::ClosedLoop", BooleanValue (true));
By default, Closed Loop Power Control is enabled.
Two modes of Closed Loop Uplink Power Control are available:
• Absolute mode: TxPower is computed with absolute TPC values
• Accumulative mode: TxPower is computed with accumulated TPC values
To switch between these two modes, one should change parameter:
Config::SetDefault ("ns3::LteUePowerControl::AccumulationEnabled", BooleanValue (true));
By default, Accumulation Mode is enabled and TPC commands in DL-DCI are set by all schedulers to 1, what
is mapped to value of 0 in Accumulation Mode.
Uplink Power Control for PUSCH
The setting of the UE Transmit power for a Physical Uplink Shared Channel (PUSCH) transmission is defined
as follows:
• If the UE transmits PUSCH without a simultaneous PUCCH for the serving cell c, then the UE transmit
power P_{PUSCH,c}(i) for PUSCH transmission in subframe i for the serving cell c is given by:
P_{PUSCH,c}(i)=\min\begin{Bmatrix}
P_{CMAX,c}(i)\\
10\log_{10}(M_{PUSCH,c}(i))+ P_{O\_PUSCH,c}(j)
+ \alpha_{c} (j) * PL_{c} + \Delta_{TF,c}(i) + f_{c}(i)
\end{Bmatrix} [dBm]
• If the UE transmits PUSCH simultaneous with PUCCH for the serving cell c, then the UE transmit power
P_{PUSCH,c}(i) for the PUSCH transmission in subframe i for the serving cell c is given by:
P_{PUSCH,c}(i)=\min\begin{Bmatrix}
10\log_{10}(\hat{P}_{CMAX,c}(i) - \hat{P}_{PUCCH}(i))\\
10\log_{10}(M_{PUSCH,c}(i))+ P_{O\_PUSCH,c}(j)
+ \alpha_{c} (j) * PL_{c} + \Delta_{TF,c}(i) + f_{c}(i)
\end{Bmatrix} [dBm]
Since Uplink Power Control for PUCCH is not implemented, this case is not implemented as well.
• If the UE is not transmitting PUSCH for the serving cell c, for the accumulation of TPC command
received with DCI format 3/3A for PUSCH, the UE shall assume that the UE transmit power
P_{PUSCH,c}(i) for the PUSCH transmission in subframe i for the serving cell c is computed by
P_{PUSCH,c}(i)=\min\begin{Bmatrix}
{P}_{CMAX,c}(i)\\
P_{O\_PUSCH,c}(1) + \alpha_{c} (1) * PL_{c} + f_{c}(i)
\end{Bmatrix} [dBm]
where:
• P_{CMAX,c}(i) is the configured UE transmit power defined in 3GPP 36.101. Table 6.2.2-1 in
subframe i for serving cell c and \hat{P}_{CMAX,c}(i) is the linear value of P_{CMAX,c}(i).
Default value for P_{CMAX,c}(i) is 23 dBm
• M_{PUSCH,c}(i) is the bandwidth of the PUSCH resource assignment expressed in number of resource
blocks valid for subframe i and serving cell c .
• P_{O\_PUSCH,c}(j) is a parameter composed of the sum of a component P_{O\_NOMINAL\_PUSCH,c}(j)
provided from higher layers for j={0,1} and a component P_{O\_UE\_PUSCH,c}(j) provided by higher
layers for j={0,1} for serving cell c. SIB2 message needs to be extended to carry these two
components, but currently they can be set via attribute system:
Config::SetDefault ("ns3::LteUePowerControl::PoNominalPusch", IntegerValue (-90));
Config::SetDefault ("ns3::LteUePowerControl::PoUePusch", IntegerValue (7));
• \alpha_{c} (j) is a 3-bit parameter provided by higher layers for serving cell c. For j=0,1,
\alpha_c \in \left \{ 0, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 \right \} For j=2, \alpha_{c} (j) =
1. This parameter is configurable by attribute system:
Config::SetDefault ("ns3::LteUePowerControl::Alpha", DoubleValue (0.8));
• PL_{c} is the downlink pathloss estimate calculated in the UE for serving cell c in dB and
PL_{c} = referenceSignalPower – higher layer filtered RSRP, where referenceSignalPower is
provided by higher layers and RSRP. referenceSignalPower is provided in SIB2 message
• \Delta_{TF,c}(i) = 10\log_{10}((2^{BPRE\cdot K_s}-1)\cdot\beta_{offset}^{PUSCH} ) for K_{s} =
1.25 and \Delta_{TF,c}(i) = 0 for K_{s} = 0. Only second case is implemented.
• f_{c}(i) is component of Closed Loop Power Control. It is the current PUSCH power control
adjustment state for serving cell c.
If Accumulation Mode is enabled f_{c}(i) is given by:
f_{c}(i) = f_{c}(i-1) + \delta_{PUSCH,c}(i - K_{PUSCH})
where: \delta_{PUSCH,c} is a correction value, also referred to as a TPC command and is included
in PDCCH with DCI; \delta_{PUSCH,c}(i - K_{PUSCH}) was signalled on PDCCH/EPDCCH with DCI for
serving cell c on subframe (i - K_{PUSCH}); K_{PUSCH} = 4 for FDD.
If UE has reached P_{CMAX,c}(i) for serving cell c, positive TPC commands for serving cell c are
not be accumulated. If UE has reached minimum power, negative TPC commands are not be
accumulated. Minimum UE power is defined in TS36.101 section 6.2.3. Default value is -40 dBm.
If Accumulation Mode is not enabled f_{c}(i) is given by:
f_{c}(i) = \delta_{PUSCH,c}(i - K_{PUSCH})
where: \delta_{PUSCH,c} is a correction value, also referred to as a TPC command and is included
in PDCCH with DCI; \delta_{PUSCH,c}(i - K_{PUSCH}) was signalled on PDCCH/EPDCCH with DCI for
serving cell c on subframe (i - K_{PUSCH}); K_{PUSCH} = 4 for FDD.
Mapping of TPC Command Field in DCI format 0/3/4 to absolute and accumulated \delta_{PUSCH,c}
values is defined in TS36.231 section 5.1.1.1 Table 5.1.1.1-2
Uplink Power Control for PUCCH
Since all uplink control messages are an ideal messages and do not consume any radio resources, Uplink
Power Control for PUCCH is not needed and it is not implemented.
Uplink Power Control for SRS
The setting of the UE Transmit power P_{SRS} for the SRS transmitted on subframe i for serving cell c is
defined by
P_{PUSCH,c}(i)=\min\begin{Bmatrix}
{P}_{CMAX,c}(i)\\
P_{SRS\_OFFSET,c}(m) + 10\log_{10}(M_{SRS,c})+
P_{O\_PUSCH,c}(j) + \alpha_{c}(j) * PL_{c} + f_{c}(i)
\end{Bmatrix} [dBm]
where:
• P_{CMAX,c}(i) is the configured UE transmit power defined in 3GPP 36.101. Table 6.2.2-1.
Default value for P_{CMAX,c}(i) is 23 dBm
• P_{SRS\_OFFSET,c}(m) is semi-statically configured by higher layers for m=0,1 for serving cell c
. For SRS transmission given trigger type 0 then m=0,1 and for SRS transmission given trigger
type 1 then m=1. For K_{s} = 0 P_Srs_Offset_Value is computed with equation:
P_{SRS\_OFFSET,c}(m)value = -10.5 + P_{SRS\_OFFSET,c}(m) * 1.5 [dBm]
This parameter is configurable by attribute system:
Config::SetDefault ("ns3::LteUePowerControl::PsrsOffset", IntegerValue (7));
• M_{SRS,c} is the bandwidth of the SRS transmission in subframe i for serving cell c expressed in
number of resource blocks. In current implementation SRS is sent over entire UL bandwidth.
• f_{c}(i) is the current PUSCH power control adjustment state for serving cell c, as defined in
Uplink Power Control for PUSCH
• P_{O\_PUSCH,c}(j) and \alpha_{c}(j) are parameters as defined in Uplink Power Control for PUSCH,
where j = 1 .
Fractional Frequency Reuse
Overview
This section describes the ns-3 support for Fractional Frequency Reuse algorithms. All implemented
algorithms are described in [ASHamza2013]. Currently 7 FR algorithms are implemented:
• ns3::LteFrNoOpAlgorithm
• ns3::LteFrHardAlgorithm
• ns3::LteFrStrictAlgorithm
• ns3::LteFrSoftAlgorithm
• ns3::LteFfrSoftAlgorithm
• ns3::LteFfrEnhancedAlgorithm
• ns3::LteFfrDistributedAlgorithm
New LteFfrAlgorithm class was created and it is a abstract class for Frequency Reuse algorithms
implementation. Also, two new SAPs between FR-Scheduler and FR-RRC were added.
[image] Sequence diagram of Scheduling with FR algorithm.UNINDENT
Figure Sequence diagram of Scheduling with FR algorithm shows the sequence diagram of scheduling
process with FR algorithm. In the beginning of scheduling process, scheduler asks FR entity for
available RBGs. According to implementation FR returns all RBGs available in cell or filter them based
on its policy. Then when trying to assign some RBG to UE, scheduler asks FR entity if this RBG is
allowed for this UE. When FR returns true, scheduler can assign this RBG to this UE, if not scheduler
is checking another RBG for this UE. Again, FR response depends on implementation and policy applied to
UE.
Supported FR algorithms
No Frequency Reuse
The NoOp FR algorithm (LteFrNoOpAlgorithm class) is implementation of Full Frequency Reuse scheme, that
means no frequency partitioning is performed between eNBs of the same network (frequency reuse factor,
FRF equals 1). eNBs uses entire system bandwidth and transmit with uniform power over all RBGs. It is the
simplest scheme and is the basic way of operating an LTE network. This scheme allows for achieving the
high peak data rate. But from the other hand, due to heavy interference levels from neighbouring cells,
cell-edge users performance is greatly limited.
Figure Full Frequency Reuse scheme below presents frequency and power plan for Full Frequency Reuse
scheme.
[image] Full Frequency Reuse scheme.UNINDENT
In ns-3, the NoOp FR algorithm always allows scheduler to use full bandwidth and allows all UEs to use
any RBG. It simply does nothing new (i.e. it does not limit eNB bandwidth, FR algorithm is disabled),
it is the simplest implementation of FrAlgorithm class and is installed in eNb by default.
Hard Frequency Reuse
The Hard Frequency Reuse algorithm provides the simplest scheme which allows to reduce inter-cell
interference level. In this scheme whole frequency bandwidth is divided into few (typically 3, 4, or 7)
disjoint sub-bands. Adjacent eNBs are allocated with different sub-band. Frequency reuse factor equals
the number of sub-bands. This scheme allows to significantly reduce ICI at the cell edge, so the
performance of cell-users is improved. But due to the fact, that each eNB uses only one part of whole
bandwidth, peak data rate level is also reduced by the factor equal to the reuse factor.
Figure Hard Frequency Reuse scheme below presents frequency and power plan for Hard Frequency Reuse
scheme.
[image] Hard Frequency Reuse scheme.UNINDENT
In our implementation, the Hard FR algorithm has only vector of RBGs available for eNB and pass it to
MAC Scheduler during scheduling functions. When scheduler ask, if RBG is allowed for specific UE it
always return true.
Strict Frequency Reuse
Strict Frequency Reuse scheme is combination of Full and Hard Frequency Reuse schemes. It consists of
dividing the system bandwidth into two parts which will have different frequency reuse. One common
sub-band of the system bandwidth is used in each cell interior (frequency reuse-1), while the other part
of the bandwidth is divided among the neighboring eNBs as in hard frequency reuse (frequency reuse-N,
N>1), in order to create one sub-band with a low inter-cell interference level in each sector. Center UEs
will be granted with the fully-reused frequency chunks, while cell-edge UEs with orthogonal chunks. It
means that interior UEs from one cell do not share any spectrum with edge UEs from second cell, which
reduces interference for both. As can be noticed, Strict FR requires a total of N + 1 sub-bands, and
allows to achieve RFR in the middle between 1 and 3.
Figure Strict Frequency Reuse scheme below presents frequency and power plan for Strict Frequency Reuse
scheme with a cell-edge reuse factor of N = 3.
[image] Strict Frequency Reuse scheme.UNINDENT
In our implementation, Strict FR algorithm has two maps, one for each sub-band. If UE can be served
within private sub-band, its RNTI is added to m_privateSubBandUe map. If UE can be served within common
sub-band, its RNTI is added to m_commonSubBandUe map. Strict FR algorithm needs to decide within which
sub-band UE should be served. It uses UE measurements provided by RRB and compare them with signal
quality threshold (this parameter can be easily tuned by attribute mechanism). Threshold has influence
on interior to cell radius ratio.
Soft Frequency Reuse
In Soft Frequency Reuse (SFR) scheme each eNb transmits over the entire system bandwidth, but there are
two sub-bands, within UEs are served with different power level. Since cell-center UEs share the
bandwidth with neighboring cells, they usually transmit at lower power level than the cell-edge UEs. SFR
is more bandwidth efficient than Strict FR, because it uses entire system bandwidth, but it also results
in more interference to both cell interior and edge users.
There are two possible versions of SFR scheme:
• In first version, the sub-band dedicated for the cell-edge UEs may also be used by the cell-center
UEs but with reduced power level and only if it is not occupied by the cell-edge UEs. Cell-center
sub-band is available to the centre UEs only. Figure Soft Frequency Reuse scheme version 1 below
presents frequency and power plan for this version of Soft Frequency Reuse scheme.
[image] Soft Frequency Reuse scheme version 1.UNINDENT
• In second version, cell-center UEs do not have access to cell-edge sub-band. In this way, each
cell can use the whole system bandwidth while reducing the interference to the neighbors cells.
From the other hand, lower ICI level at the cell-edge is achieved at the expense of lower spectrum
utilization. Figure Soft Frequency Reuse scheme version 2 below presents frequency and power plan
for this version of Soft Frequency Reuse scheme.
[image] Soft Frequency Reuse scheme version 2.UNINDENT
SFR algorithm maintain two maps. If UE should be served with lower power level, its RNTI is added to
m_lowPowerSubBandUe map. If UE should be served with higher power level, its RNTI is added to
m_highPowerSubBandUe map. To decide with which power level UE should be served SFR algorithm utilize
UE measurements, and compares them to threshold. Signal quality threshold and PdschConfigDedicated
(i.e. P_A value) for inner and outer area can be configured by attributes system. SFR utilizes
Downlink Power Control described here.
Soft Fractional Frequency Reuse
Soft Fractional Frequency Reuse (SFFR) is an combination of Strict and Soft Frequency Reuse schemes.
While Strict FR do not use the subbands allocated for outer region in the adjacent cells, soft FFR uses
these subbands for the inner UEs with low transmit power. As a result, the SFFR, like SFR, use the
subband with high transmit power level and with low transmit power level. Unlike the Soft FR and like
Strict FR, the Soft FFR uses the common sub-band which can enhance the throughput of the inner users.
Figure Soft Fractional Fractional Frequency Reuse scheme below presents frequency and power plan for Soft
Fractional Frequency Reuse.
[image] Soft Fractional Fractional Frequency Reuse scheme.UNINDENT
Enhanced Fractional Frequency Reuse
Enhanced Fractional Frequency Reuse (EFFR) described in [ZXie2009] defines 3 cell-types for directly
neighboring cells in a cellular system, and reserves for each cell-type a part of the whole frequency
band named Primary Segment, which among different type cells should be orthogonal. The remaining
subchannels constitute the Secondary Segment. The Primary Segment of a cell-type is at the same time a
part of the Secondary Segments belonging to the other two cell-types. Each cell can occupy all
subchannels of its Primary Segment at will, whereas only a part of subchannels in the Secondary Segment
can be used by this cell in an interference-aware manner.The Primary Segment of each cell is divided into
a reuse-3 part and reuse-1 part. The reuse-1 part can be reused by all types of cells in the system,
whereas reuse-3 part can only be exclusively reused by other same type cells( i.e. the reuse-3
subchannels cannot be reused by directly neighboring cells). On the Secondary Segment cell acts as a
guest, and occupying secondary subchannels is actually reuse the primary subchannels belonging to the
directly neighboring cells, thus reuse on the Secondary Segment by each cell should conform to two rules:
• monitor before use
• resource reuse based on SINR estimation
Each cell listens on every secondary subchannel all the time. And before occupation, it makes SINR
evaluation according to the gathered channel quality information (CQI) and chooses resources with best
estimation values for reuse. If CQI value for RBG is above configured threshold for some user,
transmission for this user can be performed using this RBG.
In [ZXie2009] scheduling process is described, it consist of three steps and two scheduling polices.
Since none of currently implemented schedulers allow for this behaviour, some simplification were
applied. In our implementation reuse-1 subchannels can be used only by cell center users. Reuse-3
subchannels can be used by edge users, and only if there is no edge user, transmission for cell center
users can be served in reuse-3 subchannels.
Figure Enhanced Fractional Fractional Frequency Reuse scheme below presents frequency and power plan for
Enhanced Fractional Frequency Reuse.
[image] Enhanced Fractional Fractional Frequency Reuse scheme.UNINDENT
Distributed Fractional Frequency Reuse
This Distributed Fractional Frequency Reuse Algorithm was presented in [DKimura2012]. It automatically
optimizes cell-edge sub-bands by focusing on user distribution (in particular, receive-power
distribution). This algorithm adaptively selects RBs for cell-edge sub-band on basis of coordination
information from adjecent cells and notifies the base stations of the adjacent cells, which RBs it
selected to use in edge sub-band. The base station of each cell uses the received information and the
following equation to compute cell-edge-band metric A_{k} for each RB.
A_{k} = \sum_{j\in J}w_{j}X_{j,k}
where J is a set of neighbor cells, X_{j,k}=\{0,1\} is the RNTP from the j-th neighbor cell. It takes a
value of 1 when the k-th RB in the j-th neighbor cell is used as a cell-edge sub-band and 0 otherwise.
The symbol w_{j} denotes weight with respect to adjacent cell j, that is, the number of users for which
the difference between the power of the signal received from the serving cell i and the power of the
signal received from the adjacent cell j is less than a threshold value (i.e., the number of users near
the cell edge in the service cell). A large received power difference means that cell-edge users in the
i-th cell suffer strong interference from the j-th cell.
The RB for which metric A_{k} is smallest is considered to be least affected by interference from another
cell. Serving cell selects a configured number of RBs as cell-edge sub-band in ascending order of A_{k}.
As a result, the RBs in which a small number of cell-edge users receive high interference from adjacent
base stations are selected.
The updated RNTP is then sent to all the neighbor cells. In order to avoid the meaningless oscillation of
cell-edge-band selection, a base station ignores an RNTP from another base station that has larger cell
ID than the base station.
Repeating this process across all cells enables the allocation of RBs to cell-edge areas to be optimized
over the system and to be adjusted with changes in user distribution.
Figure Sequence diagram of Distributed Frequency Reuse Scheme below presents sequence diagram of
Distributed Fractional Frequency Reuse Scheme.
[image] Sequence diagram of Distributed Frequency Reuse Scheme.UNINDENT
Carrier Aggregation
Overview
This section describes the ns-3 support for Carrier Aggregation. The references in the standard are
[TS36211], [TS36213] and [TS36331].
Note: Carrier Aggregation was introduced in release 3.27 and currently, only works in downlink.
3GPP standardizes, in release R10, the Carrier Aggregation (CA) technology.
This technology consists of the possibility, to aggregate radio resources belonging to different
carriers, in order to have more bandwidth available, and to achieve a higher throughput. Carrier
Aggregation as defined by 3GPP can be used with both TDD and FDD. Since ns-3 only supports FDD LTE
implementation, we will consider only this case in this section. Each aggregated carrier is referred to
as a component carrier, CC. The component carrier can have a bandwidth of 1.4, 3, 5, 10, 15 or 20 MHz
and a maximum of five component carriers can be aggregated, hence the maximum aggregated bandwidth is 100
MHz. In FDD the number of aggregated carriers can be different in DL and UL. However, the number of UL
component carriers is always equal to or lower than the number of DL component carriers. The individual
component carriers can also be of different bandwidths. When carrier aggregation is used there are a
number of serving cells, one for each component carrier. The coverage of the serving cells may differ,
for example due to that CCs on different frequency bands will experience different pathloss. The RRC
connection is only handled by one cell, the Primary serving cell, served by the Primary component carrier
(DL and UL PCC). It is also on the DL PCC that the UE receives NAS information, such as security
parameters.
3GPP defines three different CA bandwidth classes in releases 10 and 11 (where ATBC is Aggregated
Transmission Bandwidth Configuration):
Class A: ATBC \leq 100, maximum number of CC = 1
Class B: ATBC \leq 100, maximum number of CC = 2
Class C: 100 \leq ATBC \leq 200, maximum number of CC = 2
Figure CA impact on different layers of LTE protocol stack (from 3gpp.org) (from 3gpp.org) shows the main
impact of CA technology on the different layers of the LTE protocol stack. Introduction of carrier
aggregation influences mainly the MAC and new RRC messages are introduced. In order to keep R8/R9
compatibility the protocol changes will be kept to a minimum. Basically each component carrier is treated
as an R8 carrier. However some changes are required, such as new RRC messages in order to handle the
secondary component carrier (SCC), and MAC must be able to handle scheduling on a number of CCs. In the
following we describe the impact of the carrier aggregation implementation on the different layers of the
LTE protocol stack in ns-3.
[image] CA impact on different layers of LTE protocol stack (from 3gpp.org).UNINDENT
Impact on RRC layer
The main impacts on the RRC layer are related to secondary carrier configuration and measurements
reporting. To enable these features we have enhanced the already existing procedures for the RRC
Connection Reconfiguration and UE RRC Measurements Model.
The carrier aggregation enabling procedure is shown in figure A schematic overview of the secondary
carrier enabling procedure. As per 3GPP definition, the secondary cell is a cell, operating on a
secondary frequency, which may be configured once an RRC connection is established and which may be used
to provide additional radio resources. Hence, the procedure starts when the UE is in the
CONNECTED_NORMALLY state (see the RRC state machine description). This part of the procedure is the same
as in the previous architecture. In order to simplify the implementation, the UE Capability Inquiry and
UE Capability Information are not implemented. This implies to assume that each UE can support the
carrier aggregation, and any specific configuration provided by the eNB to which is attached. The eNB RRC
sends to the UE the secondary carrier configuration parameters through the RRC Connection Reconfiguration
procedure. This procedure may be used for various purposes related to modifications of the RRC
connection, e.g. to establish, modify or release RBs, to perform handover, to setup, modify or release
measurements, to add, modify and release secondary cells (SCells). At UE side, the RRC is extended to
configure the lower layers, in such a way that the SCell(s) are considered. Once the carriers are
configured, the Reconfiguration Completed message is sent back to the eNB RRC, informing the eNB RRC and
CCM that the secondary carriers have been properly configured. The RRC layer at both the UE and the eNB
sides is extended to allow measurement reporting for the secondary carriers. Finally, in order to allow
the procedures for configuration and measurement reporting, the RRC is enhanced to support serialization
and deserialization of RRC message structures that carry information related to the secondary carriers,
e.g., if the RRCConnectionReconfiguration message includes sCellToAddModList structure, SCell addition or
modification will be performed, or, if it contains measConfig the measurement reporting will be
configured. To allow transmission of this information the following structures are implemented for the
sCell: RadioResourceConfigCommonSCell, RadioResourceConfigDedicatedSCell and PhysicalConfigDedicatedSCell
and NonCriticalExtensionConfiguration. RadioResourceConfigCommonSCell and
RadioResourceConfigDedicatedSCell are used for SCell addition and modification (see TS 36.331,
5.3.10.3b). PhysicalConfigDedicatedSCell is used for physical channel reconfiguration (see TS 36.331,
5.3.10.6). Finally, NonCriticalExtensionConfiguration is used to carry information of sCellToAddModeList
and sCellToReleaseList, which is a modified structure comparing to TS 36.331, 6.6.2, according to which
these are directly in the root of RRCConnectionReconfiguration message. Measurement reporting is extended
with measResultSCell structure to include RSRP and RSRQ measurements for each configured SCell. However,
the measurement report triggering event A6 (neighbour becomes offset better than SCell) is not
implemented yet.
[image] A schematic overview of the secondary carrier enabling procedure.UNINDENT
Impact on PCDCP layer
There is no impact on PDCP layer.
Impact on RLC layer
The impact on the RLC layer is relatively small. There is some impact on configuration of the buffer and
the usage of SAP interfaces between RLC and MAC. Since the capacity of the lower layers increases with
the carrier aggregation it is necessary to accordingly adjust the size of the RLC buffer. The impact on
the implementation of the RLC layer is very small thanks to the design choice that allows the CCM manager
to serve the different RLC instances through the LteMacSapProvider interface. Thanks to this design
choice, the RLC is using the same interface as in the earlier LTE module architecture, the
LteMacSapProvider, but the actual SAP provider in the new architecture is the CCM (some class that
inherits LteEnbComponentCarrierManager). The CCM acts as a proxy, it receives function calls that are
meant for the MAC, and forwards them to the MAC of the different component carriers. Additionally, it
uses the information of the UEs and the logical channels for its own functionalities.
Impact on MAC layer
The impact on the MAC layer depends on the CA scheduling scheme in use. Two different scheduling schemes
are proposed in R10 and are shown in figure CA scheduling schemes (from 3gpp.org).
[image] CA scheduling schemes (from 3gpp.org).UNINDENT
The CIF (Carrier Indicator Field) on PDCCH (represented by the red area) indicates on which carrier the
scheduled resource is located. In the following we describe both the schemes:
a. scheduling grant and resources on the same carrier. One PDCCH is supported per carrier.
b. cross-carrier scheduling: it is used to schedule resources on the secondary carrier without PDCCH.
Current implementation covers only option 1, so there is no cross-carrier scheduling. The MAC layer of
the eNodeB has suffered minor changes and they are mainly related to addition of component carrier
information in message exchange between layers.
Impact on PHY layer
The impact on PHY layer is minor. There is an instance of PHY layer per each component carrier and the
SAP interface functions remain unchanged. As shown in CA scheduling schemes (from 3gpp.org) the
difference is that since there are multiple PHY instances, there are also multiple instances of PDCCH,
HARQ, ACK/NACK and CSI per carrier. So, at the eNB PHY, the changes are related to the addition of the
component carrier id information, while at the UE PHY the information of the Component Carrier is used
for some functionalities that depend on the Component Carrier to which the PHY instance belongs. For
example, the UE PHY is extended to allow disabling of the sounding reference signal (SRS) at the
secondary carriers. This is necessary because there is one UE PHY instance per component carrier, but
according to CA scheduling schemes (from 3gpp.org), only a single carrier is used and the uplink traffic
is transmitted only over the primary carrier.
Code Structure Design
This section briefly introduces the software design and implementation of the carrier aggregation
functionality.
Both LteEnbNetDevice and LteUeNetDevice are created by the LteHelper using the method
InstallSingleEnbDevice and InstallSingleUeDevice. These functions are now extended to allow the carrier
aggregation configuration. In the following we explain the main differences comparing to the previous
architecture.
Figure Changes in LteEnbNetDevice to support CA shows the attributes and associations of the
LteEnbNetDevice that are affected by the implementation, or are created in order to support the carrier
aggregation functionality. Since LteEnbNetDevice may have several component carriers, the attributes that
were formerly part of the LteEnbNetDevice and are carrier specific are migrated to the ComponentCarrier
class, e.g. physical layer configuration parameters. The attributes that are specific for the eNB
component carrier are migrated to ComponentCarrierEnb, e.g. pointers to MAC, PHY, scheduler, fractional
frequency reuse instances. LteEnbNetDevice can contain pointers to several ComponentCarrierEnb
instances. This architecture allows that each CC may have its own configuration for PHY, MAC, scheduling
algorithm and franctional frequency reuse algorithm. These attributes are currently maintained also in
the LteEnbNetDevice for backward compatibility purpose. By default the LteEnbNetDevice attributes are
the same as the primary carrier attributes.
[image] Changes in LteEnbNetDevice to support CA.UNINDENT
Figure Changes in LteUeNetDevice to support CA shows the attributes and associations of LteUeNetDevice
that are affected by the carrier aggregation implementation. Similarly, to the changes in
LteEnbNetDevice, pointers that are specific to UE component carrier are migrated to the
ComponentCarrierUe class. LteUeNetDevice has maintained m_dlEarfcn for initial cell selection
purposes.
[image] Changes in LteUeNetDevice to support CA.UNINDENT
CA impact on data plane of eNodeB
Figure eNB Data Plane Architecture shows the class diagram of the data plane at the eNB.
The main impact is the insertion of the LteEnbComponentCarrierManager class in the middle of the LTE
protocol stack. During the design phase it was decided to keep the same SAP interfaces design that
existed between MAC and RLC in order to avoid unnecessary changes in these parts of protocol stack. To
achieve this the LteEnbComponentCarrierManager implements all functions that were previously exposed by
RLC to MAC through LteMacSapUser interface. It also implements functions that were previously exposed by
MAC to RLC through the LteMacSapProvider interface. In this way, the carrier aggregation is transparent
to upper and lower layers. The only difference is that the MAC instance sees now only one LteMacSapUser,
whereas formerly it was seeing only one LteMacSapUser per RLC instance.
The LteEnbComponentCarrierManager is responsible for the forwarding messages in both directions. In the
current implementation, a PDCP and a RLC instances are activated each time a new data radio bearer is
configured. The correspondence between a new data radio bearer and a RLC instance is one to one. In
order to maintain the same behavior, when a new logical channel is activated, the logical channel
configurations is propagated to each MAC layer object in “as is” fashion.
[image] eNB Data Plane Architecture.UNINDENT
Figure Sequence Diagram of downlink buffer status reporting (BSR) with CA shows a sequence diagram of
downlink buffer status reporting with a carrier aggregation implementation of only one secondary
carrier. Each time that an RLC instance sends a buffer status report (BSR), the
LteEnbComponentCarrierManager propagates the BSR to the MAC instances. The
LteEnbComponentCarrierManager may modify a BSR before sending it to the MAC instances. This
modification depends on the traffic split algorithm implemented in CCM class that inherits
LteEnbComponentCarrierManager.
[image] Sequence Diagram of downlink buffer status reporting (BSR) with CA.UNINDENT
CA impact on control plane of eNodeB
Figure eNB Control Plane Architecture shows the class diagram of the control plane at the eNB. During the
design phase it was decided to maintain the same hooks as in the former architecture. To do so, at each
component carrier the PHY and the MAC are directly associated to the RRC instance. However, the RRC
instance is additionally connected to the LteEnbComponentCarrierManager, which is responsible for
enabling and disabling the component carriers. When the simulation starts, the number of component
carrier is fixed, but only the primary carrier component is enabled. Depending on the
LteEnbComponentCarrierManager algorithm the other carrier components could be activated or not.
[image] eNB Control Plane Architecture.UNINDENT
Figure Sequence Diagram of Data Radio Bearer Setup shows how the Radio Bearer are configured.
[image] Sequence Diagram of Data Radio Bearer Setup.UNINDENT
CA impact on data plane of UE
Figure UE Data Plane Architecture shows the relation between the different classes related to the UE data
plane. The UE data plane architecture is similar to the eNB data plane implementation. The
LteUeComponentCarrierManager is responsible to (re)map each MacSapUserProvider to the corresponding RLC
instance or to the proper MAC instance. The channel remapping depends on algorithm used as
LteUeComponentCarrierManager. A particular case is represented by the UE buffer status report (BSR) to
eNB. Since, i) the standard does not specify how the BSR has to be reported on each component carrier
and ii) it is decided to map one-to-one the logical channel to each MAC layer, the only way to send BSRs
to the eNB is through the primary carrier. Figure Uplink buffer status reporting with CA shows the
sequence diagram. Each time a BSR is generated, the LteUeComponentCarrierManager sends it through the
primary carrier component. When the primary component carrier at the eNB receives the BSR, it sends it to
LteEnbComponentCarrierManager. The latter, according to algorithm dependent policies, forwards a BSR to
component carriers. The communication between the LteEnbMac and the LteEnbComponentCarrierManager is
done through a specific set of SAP functions which are implemented in the LteUlCcmRrcSapUser and the
LteUlCcmRrcSapProvider.
[image] UE Data Plane Architecture.UNINDENT
[image] Uplink buffer status reporting with CA.UNINDENT
CA impact on control plane of UE
Figure UE Control Plane Architecture shows the relation between the different classes associated to the
UE control plane. The control plane implementation at the UE is basically the same as the eNB control
plane implementation. Each component carrier control SAP (both for PHY and MAC layer objects) is linked
in a one-to-one fashion directly to the RRC instance. The Ue RRC instance is then connected to the
LteUeComponentCarrierManager in the same way as in the eNB.
[image] UE Control Plane Architecture.UNINDENT
CCHelper is the class that is implemented to help the configuration of the physical layer parameters,
such as uplink and downlink,bandwidth and EARFCN of each carrier.
CCM RRC MAC interfaces
The Component carrier manager (CCM) is also developed by using the SAP interface design. The following
SAP interfaces are implemented for CCM and MAC:
• the LteCcmMacSapUser part is provided by MAC and is used by the CCM
• the LteCcmMacSapProvider part is provided by CCM and is used by the MAC layer
When the primary component carrier receives an uplink BSR it uses the LteCcmMacSapUser to forward it
to the CCM, which should decide how to split the traffic corresponding to this BSR among carriers.
Once this decision is made, the CCM uses the LteCcmMacSapProvider interface to send back an uplink BSR
to some of the MAC instances. Additionally, the LteCcmMacSapUser can be used by the MAC to notify
about the PRB occupancy in the downlink to the CCM. This information may be used by the CCM to decide
how to split the traffic and whether to use the secondary carriers.
CCM RRC SAP interfaces
The following SAP interfaces are implemented for CCM and RRC:
• the LteCcmRrcSapProvider is provided by the CCM and is used by the RRC layer
• the LteCcmRrcSapUser is provided by RRC and is used by the CCM
By using the LteCcmRrcSapUser the CCM may request a specific measurement reporting configuration to be
fulfilled by the UEs attached to the eNB. When a UE measurement report is received, as a result of this
configuration, the eNB RRC entity shall forward this report to the CCM through the
LteCcmRrcSapProvider::ReportUeMeas SAP function. Additionally, the LteCcmRrcSapProvider offers different
functions to the RRC that can be used to add and remove of UEs, setup or release of radio bearer,
configuration of the signalling bearer, etc.
Component carrier managers
Currently, there are two component carrier manager implementations available. The first one is the
NoOpComponentCarrierManager, which is the default CCM choice. When this CCM is used the carrier
aggregation feature is disabled. This CCM forwards all traffic, the uplink and the downlink, over the
primary carrier, and does not use secondary carriers. Another implementation is the
RrComponentCarrierManager, which splits the traffic equally among carriers, by diving the buffer status
report among different carriers. SRB0 and SRB1 flows will be forwarded only over primary carrier.
Helpers
Two helper objects are used to setup simulations and configure the various components. These objects are:
• LteHelper, which takes care of the configuration of the LTE radio access network, as well as of
coordinating the setup and release of EPS bearers. The LteHelper class provides both the API
definition and its implementation.
• EpcHelper, which takes care of the configuration of the Evolved Packet Core. The EpcHelper class is
an abstract base class, which only provides the API definition; the implementation is delegated to
the child classes in order to allow for different EPC network models.
A third helper object is used to configure the Carrier Aggregation functionality:
• CcHelper, which takes care of the configuration of the LteEnbComponentCarrierMap, basically, it
creates a user specified number of LteEnbComponentCarrier. LteUeComponentCarrierMap is currently
created starting from the LteEnbComponentCarrierMap. LteHelper:InstallSingleUeDevice, in this
implementation, is needed to invoke after the LteHelper:InstallSingleEnbDevice to ensure that the
LteEnbComponentCarrierMap is properly initialized.
It is possible to create a simple LTE-only simulations by using the LteHelper alone, or to create
complete LTE-EPC simulations by using both LteHelper and EpcHelper. When both helpers are used, they
interact in a master-slave fashion, with the LteHelper being the Master that interacts directly with the
user program, and the EpcHelper working “under the hood” to configure the EPC upon explicit methods
called by the LteHelper. The exact interactions are displayed in the Figure Sequence diagram of the
interaction between LteHelper and EpcHelper..
[image] Sequence diagram of the interaction between LteHelper and EpcHelper..UNINDENT
User Documentation
Background
We assume the reader is already familiar with how to use the ns-3 simulator to run generic simulation
programs. If this is not the case, we strongly recommend the reader to consult [ns3tutorial].
Usage Overview
The ns-3 LTE model is a software library that allows the simulation of LTE networks, optionally including
the Evolved Packet Core (EPC). The process of performing such simulations typically involves the
following steps:
1. Define the scenario to be simulated
2. Write a simulation program that recreates the desired scenario topology/architecture. This is done
accessing the ns-3 LTE model library using the ns3::LteHelper API defined in
src/lte/helper/lte-helper.h.
3. Specify configuration parameters of the objects that are being used for the simulation. This can be
done using input files (via the ns3::ConfigStore) or directly within the simulation program.
4. Configure the desired output to be produced by the simulator
5. Run the simulation.
All these aspects will be explained in the following sections by means of practical examples.
Basic simulation program
Here is the minimal simulation program that is needed to do an LTE-only simulation (without EPC).
1. Initial boilerplate:
#include <ns3/core-module.h>
#include <ns3/network-module.h>
#include <ns3/mobility-module.h>
#include <ns3/lte-module.h>
using namespace ns3;
int main (int argc, char *argv[])
{
// the rest of the simulation program follows
2. Create an LteHelper object:
Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
This will instantiate some common objects (e.g., the Channel object) and provide the methods to add
eNBs and UEs and configure them.
3. Create Node objects for the eNB(s) and the UEs:
NodeContainer enbNodes;
enbNodes.Create (1);
NodeContainer ueNodes;
ueNodes.Create (2);
Note that the above Node instances at this point still don’t have an LTE protocol stack installed;
they’re just empty nodes.
4. Configure the Mobility model for all the nodes:
MobilityHelper mobility;
mobility.SetMobilityModel ("ns3::ConstantPositionMobilityModel");
mobility.Install (enbNodes);
mobility.SetMobilityModel ("ns3::ConstantPositionMobilityModel");
mobility.Install (ueNodes);
The above will place all nodes at the coordinates (0,0,0). Please refer to the documentation of the
ns-3 mobility model for how to set your own position or configure node movement.
5. Install an LTE protocol stack on the eNB(s):
NetDeviceContainer enbDevs;
enbDevs = lteHelper->InstallEnbDevice (enbNodes);
6. Install an LTE protocol stack on the UEs:
NetDeviceContainer ueDevs;
ueDevs = lteHelper->InstallUeDevice (ueNodes);
7. Attach the UEs to an eNB. This will configure each UE according to the eNB configuration, and create
an RRC connection between them:
lteHelper->Attach (ueDevs, enbDevs.Get (0));
8. Activate a data radio bearer between each UE and the eNB it is attached to:
enum EpsBearer::Qci q = EpsBearer::GBR_CONV_VOICE;
EpsBearer bearer (q);
lteHelper->ActivateDataRadioBearer (ueDevs, bearer);
this method will also activate two saturation traffic generators for that bearer, one in uplink and
one in downlink.
9. Set the stop time:
Simulator::Stop (Seconds (0.005));
This is needed otherwise the simulation will last forever, because (among others) the
start-of-subframe event is scheduled repeatedly, and the ns-3 simulator scheduler will hence never
run out of events.
10. Run the simulation:
Simulator::Run ();
11. Cleanup and exit:
Simulator::Destroy ();
return 0;
}
For how to compile and run simulation programs, please refer to [ns3tutorial].
Configuration of LTE model parameters
All the relevant LTE model parameters are managed through the ns-3 attribute system. Please refer to the
[ns3tutorial] and [ns3manual] for detailed information on all the possible methods to do it
(environmental variables, C++ API, GtkConfigStore…).
In the following, we just briefly summarize how to do it using input files together with the ns-3
ConfigStore. First of all, you need to put the following in your simulation program, right after main ()
starts:
CommandLine cmd (__FILE__);
cmd.Parse (argc, argv);
ConfigStore inputConfig;
inputConfig.ConfigureDefaults ();
// parse again so you can override default values from the command line
cmd.Parse (argc, argv);
for the above to work, make sure you also #include "ns3/config-store.h". Now create a text file named
(for example) input-defaults.txt specifying the new default values that you want to use for some
attributes:
default ns3::LteHelper::Scheduler "ns3::PfFfMacScheduler"
default ns3::LteHelper::PathlossModel "ns3::FriisSpectrumPropagationLossModel"
default ns3::LteEnbNetDevice::UlBandwidth "25"
default ns3::LteEnbNetDevice::DlBandwidth "25"
default ns3::LteEnbNetDevice::DlEarfcn "100"
default ns3::LteEnbNetDevice::UlEarfcn "18100"
default ns3::LteUePhy::TxPower "10"
default ns3::LteUePhy::NoiseFigure "9"
default ns3::LteEnbPhy::TxPower "30"
default ns3::LteEnbPhy::NoiseFigure "5"
Supposing your simulation program is called src/lte/examples/lte-sim-with-input, you can now pass these
settings to the simulation program in the following way:
./waf --command-template="%s --ns3::ConfigStore::Filename=input-defaults.txt
--ns3::ConfigStore::Mode=Load --ns3::ConfigStore::FileFormat=RawText"
--run src/lte/examples/lte-sim-with-input
Furthermore, you can generate a template input file with the following command:
./waf --command-template="%s --ns3::ConfigStore::Filename=input-defaults.txt
--ns3::ConfigStore::Mode=Save --ns3::ConfigStore::FileFormat=RawText"
--run src/lte/examples/lte-sim-with-input
note that the above will put in the file input-defaults.txt all the default values that are registered in
your particular build of the simulator, including lots of non-LTE attributes.
Configure LTE MAC Scheduler
There are several types of LTE MAC scheduler user can choose here. User can use following codes to define
scheduler type:
Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
lteHelper->SetSchedulerType ("ns3::FdMtFfMacScheduler"); // FD-MT scheduler
lteHelper->SetSchedulerType ("ns3::TdMtFfMacScheduler"); // TD-MT scheduler
lteHelper->SetSchedulerType ("ns3::TtaFfMacScheduler"); // TTA scheduler
lteHelper->SetSchedulerType ("ns3::FdBetFfMacScheduler"); // FD-BET scheduler
lteHelper->SetSchedulerType ("ns3::TdBetFfMacScheduler"); // TD-BET scheduler
lteHelper->SetSchedulerType ("ns3::FdTbfqFfMacScheduler"); // FD-TBFQ scheduler
lteHelper->SetSchedulerType ("ns3::TdTbfqFfMacScheduler"); // TD-TBFQ scheduler
lteHelper->SetSchedulerType ("ns3::PssFfMacScheduler"); //PSS scheduler
TBFQ and PSS have more parameters than other schedulers. Users can define those parameters in following
way:
* TBFQ scheduler::
Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
lteHelper->SetSchedulerAttribute("DebtLimit", IntegerValue(yourvalue)); // default value -625000 bytes (-5Mb)
lteHelper->SetSchedulerAttribute("CreditLimit", UintegerValue(yourvalue)); // default value 625000 bytes (5Mb)
lteHelper->SetSchedulerAttribute("TokenPoolSize", UintegerValue(yourvalue)); // default value 1 byte
lteHelper->SetSchedulerAttribute("CreditableThreshold", UintegerValue(yourvalue)); // default value 0
* PSS scheduler::
Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
lteHelper->SetSchedulerAttribute("nMux", UIntegerValue(yourvalue)); // the maximum number of UE selected by TD scheduler
lteHelper->SetSchedulerAttribute("PssFdSchedulerType", StringValue("CoItA")); // PF scheduler type in PSS
In TBFQ, default values of debt limit and credit limit are set to -5Mb and 5Mb respectively based on
paper [FABokhari2009]. Current implementation does not consider credit threshold (C = 0). In PSS, if
user does not define nMux, PSS will set this value to half of total UE. The default FD scheduler is
PFsch.
In addition, token generation rate in TBFQ and target bit rate in PSS need to be configured by Guarantee
Bit Rate (GBR) or Maximum Bit Rate (MBR) in epc bearer QoS parameters. Users can use following codes to
define GBR and MBR in both downlink and uplink:
Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
enum EpsBearer::Qci q = EpsBearer::yourvalue; // define Qci type
GbrQosInformation qos;
qos.gbrDl = yourvalue; // Downlink GBR
qos.gbrUl = yourvalue; // Uplink GBR
qos.mbrDl = yourvalue; // Downlink MBR
qos.mbrUl = yourvalue; // Uplink MBR
EpsBearer bearer (q, qos);
lteHelper->ActivateDedicatedEpsBearer (ueDevs, bearer, EpcTft::Default ());
In PSS, TBR is obtained from GBR in bearer level QoS parameters. In TBFQ, token generation rate is
obtained from the MBR setting in bearer level QoS parameters, which therefore needs to be configured
consistently. For constant bit rate (CBR) traffic, it is suggested to set MBR to GBR. For variance bit
rate (VBR) traffic, it is suggested to set MBR k times larger than GBR in order to cover the peak traffic
rate. In current implementation, k is set to three based on paper [FABokhari2009]. In addition, current
version of TBFQ does not consider RLC header and PDCP header length in MBR and GBR. Another parameter in
TBFQ is packet arrival rate. This parameter is calculated within scheduler and equals to the past average
throughput which is used in PF scheduler.
Many useful attributes of the LTE-EPC model will be described in the following subsections. Still, there
are many attributes which are not explicitly mentioned in the design or user documentation, but which are
clearly documented using the ns-3 attribute system. You can easily print a list of the attributes of a
given object together with their description and default value passing --PrintAttributes= to a simulation
program, like this:
./waf --run lena-simple --command-template="%s --PrintAttributes=ns3::LteHelper"
You can try also with other LTE and EPC objects, like this:
./waf --run lena-simple --command-template="%s --PrintAttributes=ns3::LteEnbNetDevice"
./waf --run lena-simple --command-template="%s --PrintAttributes=ns3::LteEnbMac"
./waf --run lena-simple --command-template="%s --PrintAttributes=ns3::LteEnbPhy"
./waf --run lena-simple --command-template="%s --PrintAttributes=ns3::LteUePhy"
./waf --run lena-simple --command-template="%s --PrintAttributes=ns3::PointToPointEpcHelper"
Simulation Output
The ns-3 LTE model currently supports the output to file of PHY, MAC, RLC and PDCP level Key Performance
Indicators (KPIs). You can enable it in the following way:
Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
// configure all the simulation scenario here...
lteHelper->EnablePhyTraces ();
lteHelper->EnableMacTraces ();
lteHelper->EnableRlcTraces ();
lteHelper->EnablePdcpTraces ();
Simulator::Run ();
RLC and PDCP KPIs are calculated over a time interval and stored on ASCII files, two for RLC KPIs and two
for PDCP KPIs, in each case one for uplink and one for downlink. The time interval duration can be
controlled using the attribute ns3::RadioBearerStatsCalculator::EpochDuration.
The columns of the RLC KPI files is the following (the same for uplink and downlink):
1. start time of measurement interval in seconds since the start of simulation
2. end time of measurement interval in seconds since the start of simulation
3. Cell ID
4. unique UE ID (IMSI)
5. cell-specific UE ID (RNTI)
6. Logical Channel ID
7. Number of transmitted RLC PDUs
8. Total bytes transmitted.
9. Number of received RLC PDUs
10. Total bytes received
11. Average RLC PDU delay in seconds
12. Standard deviation of the RLC PDU delay
13. Minimum value of the RLC PDU delay
14. Maximum value of the RLC PDU delay
15. Average RLC PDU size, in bytes
16. Standard deviation of the RLC PDU size
17. Minimum RLC PDU size
18. Maximum RLC PDU size
Similarly, the columns of the PDCP KPI files is the following (again, the same for uplink and downlink):
1. start time of measurement interval in seconds since the start of simulation
2. end time of measurement interval in seconds since the start of simulation
3. Cell ID
4. unique UE ID (IMSI)
5. cell-specific UE ID (RNTI)
6. Logical Channel ID
7. Number of transmitted PDCP PDUs
8. Total bytes transmitted.
9. Number of received PDCP PDUs
10. Total bytes received
11. Average PDCP PDU delay in seconds
12. Standard deviation of the PDCP PDU delay
13. Minimum value of the PDCP PDU delay
14. Maximum value of the PDCP PDU delay
15. Average PDCP PDU size, in bytes
16. Standard deviation of the PDCP PDU size
17. Minimum PDCP PDU size
18. Maximum PDCP PDU size
Note: The PDCP traces for data radio bearers are not generated when sec-sm-rlc is used.
MAC KPIs are basically a trace of the resource allocation reported by the scheduler upon the start of
every subframe. They are stored in ASCII files. For downlink MAC KPIs the format is the following:
1. Simulation time in seconds at which the allocation is indicated by the scheduler
2. Cell ID
3. unique UE ID (IMSI)
4. Frame number
5. Subframe number
6. cell-specific UE ID (RNTI)
7. MCS of TB 1
8. size of TB 1
9. MCS of TB 2 (0 if not present)
10. size of TB 2 (0 if not present)
while for uplink MAC KPIs the format is:
1. Simulation time in seconds at which the allocation is indicated by the scheduler
2. Cell ID
3. unique UE ID (IMSI)
4. Frame number
5. Subframe number
6. cell-specific UE ID (RNTI)
7. MCS of TB
8. size of TB
The names of the files used for MAC KPI output can be customized via the ns-3 attributes
ns3::MacStatsCalculator::DlOutputFilename and ns3::MacStatsCalculator::UlOutputFilename.
PHY KPIs are distributed in seven different files, configurable through the attributes
1. ns3::PhyStatsCalculator::DlRsrpSinrFilename
2. ns3::PhyStatsCalculator::UeSinrFilename
3. ns3::PhyStatsCalculator::InterferenceFilename
4. ns3::PhyStatsCalculator::DlTxOutputFilename
5. ns3::PhyStatsCalculator::UlTxOutputFilename
6. ns3::PhyStatsCalculator::DlRxOutputFilename
7. ns3::PhyStatsCalculator::UlRxOutputFilename
In the RSRP/SINR file, the following content is available:
1. Simulation time in seconds at which the allocation is indicated by the scheduler
2. Cell ID
3. unique UE ID (IMSI)
4. RSRP
5. Linear average over all RBs of the downlink SINR in linear units
The contents in the UE SINR file are:
1. Simulation time in seconds at which the allocation is indicated by the scheduler
2. Cell ID
3. unique UE ID (IMSI)
4. uplink SINR in linear units for the UE
In the interference filename the content is:
1. Simulation time in seconds at which the allocation is indicated by the scheduler
2. Cell ID
3. List of interference values per RB
In UL and DL transmission files the parameters included are:
1. Simulation time in milliseconds
2. Cell ID
3. unique UE ID (IMSI)
4. RNTI
5. Layer of transmission
6. MCS
7. size of the TB
8. Redundancy version
9. New Data Indicator flag
And finally, in UL and DL reception files the parameters included are:
1. Simulation time in milliseconds
2. Cell ID
3. unique UE ID (IMSI)
4. RNTI
5. Transmission Mode
6. Layer of transmission
7. MCS
8. size of the TB
9. Redundancy version
10. New Data Indicator flag
11. Correctness in the reception of the TB
Note: The traces generated by simulating the scenarios involving the RLF will have a discontinuity in
time from the moment of the RLF event until the UE connects again to an eNB.
Fading Trace Usage
In this section we will describe how to use fading traces within LTE simulations.
Fading Traces Generation
It is possible to generate fading traces by using a dedicated matlab script provided with the code
(/lte/model/fading-traces/fading-trace-generator.m). This script already includes the typical taps
configurations for three 3GPP scenarios (i.e., pedestrian, vehicular and urban as defined in Annex B.2 of
[TS36104]); however users can also introduce their specific configurations. The list of the configurable
parameters is provided in the following:
• fc : the frequency in use (it affects the computation of the doppler speed).
• v_km_h : the speed of the users
• traceDuration : the duration in seconds of the total length of the trace.
• numRBs : the number of the resource block to be evaluated.
• tag : the tag to be applied to the file generated.
The file generated contains ASCII-formatted real values organized in a matrix fashion: every row
corresponds to a different RB, and every column correspond to a different temporal fading trace sample.
It has to be noted that the ns-3 LTE module is able to work with any fading trace file that complies with
the above described ASCII format. Hence, other external tools can be used to generate custom fading
traces, such as for example other simulators or experimental devices.
Fading Traces Usage
When using a fading trace, it is of paramount importance to specify correctly the trace parameters in the
simulation, so that the fading model can load and use it correctly. The parameters to be configured are:
• TraceFilename : the name of the trace to be loaded (absolute path, or relative path w.r.t. the path
from where the simulation program is executed);
• TraceLength : the trace duration in seconds;
• SamplesNum : the number of samples;
• WindowSize : the size of the fading sampling window in seconds;
It is important to highlight that the sampling interval of the fading trace has to be 1 ms or greater,
and in the latter case it has to be an integer multiple of 1 ms in order to be correctly processed by the
fading module.
The default configuration of the matlab script provides a trace 10 seconds long, made of 10,000 samples
(i.e., 1 sample per TTI=1ms) and used with a windows size of 0.5 seconds amplitude. These are also the
default values of the parameters above used in the simulator; therefore their settage can be avoided in
case the fading trace respects them.
In order to activate the fading module (which is not active by default) the following code should be
included in the simulation program:
Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
lteHelper->SetFadingModel("ns3::TraceFadingLossModel");
And for setting the parameters:
lteHelper->SetFadingModelAttribute ("TraceFilename", StringValue ("src/lte/model/fading-traces/fading_trace_EPA_3kmph.fad"));
lteHelper->SetFadingModelAttribute ("TraceLength", TimeValue (Seconds (10.0)));
lteHelper->SetFadingModelAttribute ("SamplesNum", UintegerValue (10000));
lteHelper->SetFadingModelAttribute ("WindowSize", TimeValue (Seconds (0.5)));
lteHelper->SetFadingModelAttribute ("RbNum", UintegerValue (100));
It has to be noted that, TraceFilename does not have a default value, therefore is has to be always set
explicitly.
The simulator provide natively three fading traces generated according to the configurations defined in
in Annex B.2 of [TS36104]. These traces are available in the folder src/lte/model/fading-traces/). An
excerpt from these traces is represented in the following figures.
[image: Fading trace 3 kmph] [image] Excerpt of the fading trace included in the simulator for a
pedestrian scenario (speed of 3 kmph)..UNINDENT
[image: Fading trace 60 kmph] [image] Excerpt of the fading trace included in the simulator for a
vehicular scenario (speed of 60 kmph)..UNINDENT
[image: Fading trace 3 kmph] [image] Excerpt of the fading trace included in the simulator for an urban
scenario (speed of 3 kmph)..UNINDENT
Mobility Model with Buildings
We now explain by examples how to use the buildings model (in particular, the MobilityBuildingInfo and
the BuildingPropagationModel classes) in an ns-3 simulation program to setup an LTE simulation scenario
that includes buildings and indoor nodes.
1. Header files to be included:
#include <ns3/mobility-building-info.h>
#include <ns3/buildings-propagation-loss-model.h>
#include <ns3/building.h>
2. Pathloss model selection:
Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
lteHelper->SetAttribute ("PathlossModel", StringValue ("ns3::BuildingsPropagationLossModel"));
3. EUTRA Band Selection
The selection of the working frequency of the propagation model has to be done with the standard ns-3
attribute system as described in the correspond section (“Configuration of LTE model parameters”) by
means of the DlEarfcn and UlEarfcn parameters, for instance:
lteHelper->SetEnbDeviceAttribute ("DlEarfcn", UintegerValue (100));
lteHelper->SetEnbDeviceAttribute ("UlEarfcn", UintegerValue (18100));
It is to be noted that using other means to configure the frequency used by the propagation model (i.e.,
configuring the corresponding BuildingsPropagationLossModel attributes directly) might generates
conflicts in the frequencies definition in the modules during the simulation, and is therefore not
advised.
1. Mobility model selection:
MobilityHelper mobility;
mobility.SetMobilityModel ("ns3::ConstantPositionMobilityModel");
It is to be noted that any mobility model can be used.
2. Building creation:
double x_min = 0.0;
double x_max = 10.0;
double y_min = 0.0;
double y_max = 20.0;
double z_min = 0.0;
double z_max = 10.0;
Ptr<Building> b = CreateObject <Building> ();
b->SetBoundaries (Box (x_min, x_max, y_min, y_max, z_min, z_max));
b->SetBuildingType (Building::Residential);
b->SetExtWallsType (Building::ConcreteWithWindows);
b->SetNFloors (3);
b->SetNRoomsX (3);
b->SetNRoomsY (2);
This will instantiate a residential building with base of 10 x 20 meters and height of 10 meters whose
external walls are of concrete with windows; the building has three floors and has an internal 3 x 2
grid of rooms of equal size.
3. Node creation and positioning:
ueNodes.Create (2);
mobility.Install (ueNodes);
BuildingsHelper::Install (ueNodes);
NetDeviceContainer ueDevs;
ueDevs = lteHelper->InstallUeDevice (ueNodes);
Ptr<ConstantPositionMobilityModel> mm0 = enbNodes.Get (0)->GetObject<ConstantPositionMobilityModel> ();
Ptr<ConstantPositionMobilityModel> mm1 = enbNodes.Get (1)->GetObject<ConstantPositionMobilityModel> ();
mm0->SetPosition (Vector (5.0, 5.0, 1.5));
mm1->SetPosition (Vector (30.0, 40.0, 1.5));
4. Finalize the building and mobility model configuration:
BuildingsHelper::MakeMobilityModelConsistent ();
See the documentation of the buildings module for more detailed information.
PHY Error Model
The Physical error model consists of the data error model and the downlink control error model, both of
them active by default. It is possible to deactivate them with the ns3 attribute system, in detail:
Config::SetDefault ("ns3::LteSpectrumPhy::CtrlErrorModelEnabled", BooleanValue (false));
Config::SetDefault ("ns3::LteSpectrumPhy::DataErrorModelEnabled", BooleanValue (false));
MIMO Model
Is this subsection we illustrate how to configure the MIMO parameters. LTE defines 7 types of
transmission modes:
• Transmission Mode 1: SISO.
• Transmission Mode 2: MIMO Tx Diversity.
• Transmission Mode 3: MIMO Spatial Multiplexity Open Loop.
• Transmission Mode 4: MIMO Spatial Multiplexity Closed Loop.
• Transmission Mode 5: MIMO Multi-User.
• Transmission Mode 6: Closer loop single layer precoding.
• Transmission Mode 7: Single antenna port 5.
According to model implemented, the simulator includes the first three transmission modes types. The
default one is the Transmission Mode 1 (SISO). In order to change the default Transmission Mode to be
used, the attribute DefaultTransmissionMode of the LteEnbRrc can be used, as shown in the following:
Config::SetDefault ("ns3::LteEnbRrc::DefaultTransmissionMode", UintegerValue (0)); // SISO
Config::SetDefault ("ns3::LteEnbRrc::DefaultTransmissionMode", UintegerValue (1)); // MIMO Tx diversity (1 layer)
Config::SetDefault ("ns3::LteEnbRrc::DefaultTransmissionMode", UintegerValue (2)); // MIMO Spatial Multiplexity (2 layers)
For changing the transmission mode of a certain user during the simulation a specific interface has been
implemented in both standard schedulers:
void TransmissionModeConfigurationUpdate (uint16_t rnti, uint8_t txMode);
This method can be used both for developing transmission mode decision engine (i.e., for optimizing the
transmission mode according to channel condition and/or user’s requirements) and for manual switching
from simulation script. In the latter case, the switching can be done as shown in the following:
Ptr<LteEnbNetDevice> lteEnbDev = enbDevs.Get (0)->GetObject<LteEnbNetDevice> ();
PointerValue ptrval;
enbNetDev->GetAttribute ("FfMacScheduler", ptrval);
Ptr<RrFfMacScheduler> rrsched = ptrval.Get<RrFfMacScheduler> ();
Simulator::Schedule (Seconds (0.2), &RrFfMacScheduler::TransmissionModeConfigurationUpdate, rrsched, rnti, 1);
Finally, the model implemented can be reconfigured according to different MIMO models by updating the
gain values (the only constraints is that the gain has to be constant during simulation run-time and
common for the layers). The gain of each Transmission Mode can be changed according to the standard ns3
attribute system, where the attributes are: TxMode1Gain, TxMode2Gain, TxMode3Gain, TxMode4Gain,
TxMode5Gain, TxMode6Gain and TxMode7Gain. By default only TxMode1Gain, TxMode2Gain and TxMode3Gain have a
meaningful value, that are the ones derived by _[CatreuxMIMO] (i.e., respectively 0.0, 4.2 and -2.8 dB).
Use of AntennaModel
We now show how associate a particular AntennaModel with an eNB device in order to model a sector of a
macro eNB. For this purpose, it is convenient to use the CosineAntennaModel provided by the ns-3 antenna
module. The configuration of the eNB is to be done via the LteHelper instance right before the creation
of the EnbNetDevice, as shown in the following:
lteHelper->SetEnbAntennaModelType ("ns3::CosineAntennaModel");
lteHelper->SetEnbAntennaModelAttribute ("Orientation", DoubleValue (0));
lteHelper->SetEnbAntennaModelAttribute ("Beamwidth", DoubleValue (60));
lteHelper->SetEnbAntennaModelAttribute ("MaxGain", DoubleValue (0.0));
the above code will generate an antenna model with a 60 degrees beamwidth pointing along the X axis. The
orientation is measured in degrees from the X axis, e.g., an orientation of 90 would point along the Y
axis, and an orientation of -90 would point in the negative direction along the Y axis. The beamwidth is
the -3 dB beamwidth, e.g, for a 60 degree beamwidth the antenna gain at an angle of \pm 30 degrees from
the direction of orientation is -3 dB.
To create a multi-sector site, you need to create different ns-3 nodes placed at the same position, and
to configure separate EnbNetDevice with different antenna orientations to be installed on each node.
Radio Environment Maps
By using the class RadioEnvironmentMapHelper it is possible to output to a file a Radio Environment Map
(REM), i.e., a uniform 2D grid of values that represent the Signal-to-noise ratio in the downlink with
respect to the eNB that has the strongest signal at each point. It is possible to specify if REM should
be generated for data or control channel. Also user can set the RbId, for which REM will be generated.
Default RbId is -1, what means that REM will generated with averaged Signal-to-noise ratio from all RBs.
To do this, you just need to add the following code to your simulation program towards the end, right
before the call to Simulator::Run ():
Ptr<RadioEnvironmentMapHelper> remHelper = CreateObject<RadioEnvironmentMapHelper> ();
remHelper->SetAttribute ("Channel", PointerValue (lteHelper->GetDownlinkSpectrumChannel ()));
remHelper->SetAttribute ("OutputFile", StringValue ("rem.out"));
remHelper->SetAttribute ("XMin", DoubleValue (-400.0));
remHelper->SetAttribute ("XMax", DoubleValue (400.0));
remHelper->SetAttribute ("XRes", UintegerValue (100));
remHelper->SetAttribute ("YMin", DoubleValue (-300.0));
remHelper->SetAttribute ("YMax", DoubleValue (300.0));
remHelper->SetAttribute ("YRes", UintegerValue (75));
remHelper->SetAttribute ("Z", DoubleValue (0.0));
remHelper->SetAttribute ("UseDataChannel", BooleanValue (true));
remHelper->SetAttribute ("RbId", IntegerValue (10));
remHelper->Install ();
By configuring the attributes of the RadioEnvironmentMapHelper object as shown above, you can tune the
parameters of the REM to be generated. Note that each RadioEnvironmentMapHelper instance can generate
only one REM; if you want to generate more REMs, you need to create one separate instance for each REM.
Note that the REM generation is very demanding, in particular:
• the run-time memory consumption is approximately 5KB per pixel. For example, a REM with a resolution
of 500x500 would need about 1.25 GB of memory, and a resolution of 1000x1000 would need needs about
5 GB (too much for a regular PC at the time of this writing). To overcome this issue, the REM is
generated at successive steps, with each step evaluating at most a number of pixels determined by
the value of the the attribute RadioEnvironmentMapHelper::MaxPointsPerIteration.
• if you generate a REM at the beginning of a simulation, it will slow down the execution of the rest
of the simulation. If you want to generate a REM for a program and also use the same program to get
simulation result, it is recommended to add a command-line switch that allows to either generate the
REM or run the complete simulation. For this purpose, note that there is an attribute
RadioEnvironmentMapHelper::StopWhenDone (default: true) that will force the simulation to stop right
after the REM has been generated.
The REM is stored in an ASCII file in the following format:
• column 1 is the x coordinate
• column 2 is the y coordinate
• column 3 is the z coordinate
• column 4 is the SINR in linear units
A minimal gnuplot script that allows you to plot the REM is given below:
set view map;
set xlabel "X"
set ylabel "Y"
set cblabel "SINR (dB)"
unset key
plot "rem.out" using ($1):($2):(10*log10($4)) with image
As an example, here is the REM that can be obtained with the example program lena-dual-stripe, which
shows a three-sector LTE macrocell in a co-channel deployment with some residential femtocells randomly
deployed in two blocks of apartments.
[image] REM obtained from the lena-dual-stripe example.UNINDENT
Note that the lena-dual-stripe example program also generate gnuplot-compatible output files containing
information about the positions of the UE and eNB nodes as well as of the buildings, respectively in
the files ues.txt, enbs.txt and buildings.txt. These can be easily included when using gnuplot. For
example, assuming that your gnuplot script (e.g., the minimal gnuplot script described above) is saved
in a file named my_plot_script, running the following command would plot the location of UEs, eNBs and
buildings on top of the REM:
gnuplot -p enbs.txt ues.txt buildings.txt my_plot_script
AMC Model and CQI Calculation
The simulator provides two possible schemes for what concerns the selection of the MCSs and
correspondingly the generation of the CQIs. The first one is based on the GSoC module [Piro2011] and
works per RB basis. This model can be activated with the ns3 attribute system, as presented in the
following:
Config::SetDefault ("ns3::LteAmc::AmcModel", EnumValue (LteAmc::PiroEW2010));
While, the solution based on the physical error model can be controlled with:
Config::SetDefault ("ns3::LteAmc::AmcModel", EnumValue (LteAmc::MiErrorModel));
Finally, the required efficiency of the PiroEW2010 AMC module can be tuned thanks to the Ber attribute
(), for instance:
Config::SetDefault ("ns3::LteAmc::Ber", DoubleValue (0.00005));
Evolved Packet Core (EPC)
We now explain how to write a simulation program that allows to simulate the EPC in addition to the LTE
radio access network. The use of EPC allows to use IPv4 and IPv6 networking with LTE devices. In other
words, you will be able to use the regular ns-3 applications and sockets over IPv4 and IPv6 over LTE, and
also to connect an LTE network to any other IPv4 and IPv6 network you might have in your simulation.
First of all, in addition to LteHelper that we already introduced in Basic simulation program, you need
to use an additional EpcHelper class, which will take care of creating the EPC entities and network
topology. Note that you can’t use EpcHelper directly, as it is an abstract base class; instead, you need
to use one of its child classes, which provide different EPC topology implementations. In this example we
will consider PointToPointEpcHelper, which implements an EPC based on point-to-point links. To use it,
you need first to insert this code in your simulation program:
Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
Ptr<PointToPointEpcHelper> epcHelper = CreateObject<PointToPointEpcHelper> ();
Then, you need to tell the LTE helper that the EPC will be used:
lteHelper->SetEpcHelper (epcHelper);
the above step is necessary so that the LTE helper will trigger the appropriate EPC configuration in
correspondence with some important configuration, such as when a new eNB or UE is added to the
simulation, or an EPS bearer is created. The EPC helper will automatically take care of the necessary
setup, such as S1 link creation and S1 bearer setup. All this will be done without the intervention of
the user.
Calling lteHelper->SetEpcHelper (epcHelper) enables the use of EPC, and has the side effect that any new
LteEnbRrc that is created will have the EpsBearerToRlcMapping attribute set to RLC_UM_ALWAYS instead of
RLC_SM_ALWAYS if the latter was the default; otherwise, the attribute won’t be changed (e.g., if you
changed the default to RLC_AM_ALWAYS, it won’t be touched).
It is to be noted that the EpcHelper will also automatically create the PGW node and configure it so that
it can properly handle traffic from/to the LTE radio access network. Still, you need to add some
explicit code to connect the PGW to other IPv4/IPv6 networks (e.g., the internet, another EPC). Here is a
very simple example about how to connect a single remote host (IPv4 type) to the PGW via a point-to-point
link:
Ptr<Node> pgw = epcHelper->GetPgwNode ();
// Create a single RemoteHost
NodeContainer remoteHostContainer;
remoteHostContainer.Create (1);
Ptr<Node> remoteHost = remoteHostContainer.Get (0);
InternetStackHelper internet;
internet.Install (remoteHostContainer);
// Create the internet
PointToPointHelper p2ph;
p2ph.SetDeviceAttribute ("DataRate", DataRateValue (DataRate ("100Gb/s")));
p2ph.SetDeviceAttribute ("Mtu", UintegerValue (1500));
p2ph.SetChannelAttribute ("Delay", TimeValue (Seconds (0.010)));
NetDeviceContainer internetDevices = p2ph.Install (pgw, remoteHost);
Ipv4AddressHelper ipv4h;
ipv4h.SetBase ("1.0.0.0", "255.0.0.0");
Ipv4InterfaceContainer internetIpIfaces = ipv4h.Assign (internetDevices);
// interface 0 is localhost, 1 is the p2p device
Ipv4Address remoteHostAddr = internetIpIfaces.GetAddress (1);
Ipv4StaticRoutingHelper ipv4RoutingHelper;
Ptr<Ipv4StaticRouting> remoteHostStaticRouting;
remoteHostStaticRouting = ipv4RoutingHelper.GetStaticRouting (remoteHost->GetObject<Ipv4> ());
remoteHostStaticRouting->AddNetworkRouteTo (epcHelper->GetEpcIpv4NetworkAddress (),
Ipv4Mask ("255.255.0.0"), 1);
Now, you should go on and create LTE eNBs and UEs as explained in the previous sections. You can of
course configure other LTE aspects such as pathloss and fading models. Right after you created the UEs,
you should also configure them for IP networking. This is done as follows. We assume you have a container
for UE and eNodeB nodes like this:
NodeContainer ueNodes;
NodeContainer enbNodes;
to configure an LTE-only simulation, you would then normally do something like this:
NetDeviceContainer ueLteDevs = lteHelper->InstallUeDevice (ueNodes);
lteHelper->Attach (ueLteDevs, enbLteDevs.Get (0));
in order to configure the UEs for IP networking, you just need to additionally do like this:
// we install the IP stack on the UEs
InternetStackHelper internet;
internet.Install (ueNodes);
// assign IP address to UEs
for (uint32_t u = 0; u < ueNodes.GetN (); ++u)
{
Ptr<Node> ue = ueNodes.Get (u);
Ptr<NetDevice> ueLteDevice = ueLteDevs.Get (u);
Ipv4InterfaceContainer ueIpIface;
ueIpIface = epcHelper->AssignUeIpv4Address (NetDeviceContainer (ueLteDevice));
// set the default gateway for the UE
Ptr<Ipv4StaticRouting> ueStaticRouting;
ueStaticRouting = ipv4RoutingHelper.GetStaticRouting (ue->GetObject<Ipv4> ());
ueStaticRouting->SetDefaultRoute (epcHelper->GetUeDefaultGatewayAddress (), 1);
}
The activation of bearers is done in a slightly different way with respect to what done for an LTE-only
simulation. First, the method ActivateDataRadioBearer is not to be used when the EPC is used. Second,
when EPC is used, the default EPS bearer will be activated automatically when you call LteHelper::Attach
(). Third, if you want to setup dedicated EPS bearer, you can do so using the method
LteHelper::ActivateDedicatedEpsBearer (). This method takes as a parameter the Traffic Flow Template
(TFT), which is a struct that identifies the type of traffic that will be mapped to the dedicated EPS
bearer. Here is an example for how to setup a dedicated bearer for an application at the UE communicating
on port 1234:
Ptr<EpcTft> tft = Create<EpcTft> ();
EpcTft::PacketFilter pf;
pf.localPortStart = 1234;
pf.localPortEnd = 1234;
tft->Add (pf);
lteHelper->ActivateDedicatedEpsBearer (ueLteDevs,
EpsBearer (EpsBearer::NGBR_VIDEO_TCP_DEFAULT),
tft);
you can of course use custom EpsBearer and EpcTft configurations, please refer to the doxygen
documentation for how to do it.
Finally, you can install applications on the LTE UE nodes that communicate with remote applications over
the internet. This is done following the usual ns-3 procedures. Following our simple example with a
single remoteHost, here is how to setup downlink communication, with an UdpClient application on the
remote host, and a PacketSink on the LTE UE (using the same variable names of the previous code snippets)
uint16_t dlPort = 1234;
PacketSinkHelper packetSinkHelper ("ns3::UdpSocketFactory",
InetSocketAddress (Ipv4Address::GetAny (), dlPort));
ApplicationContainer serverApps = packetSinkHelper.Install (ue);
serverApps.Start (Seconds (0.01));
UdpClientHelper client (ueIpIface.GetAddress (0), dlPort);
ApplicationContainer clientApps = client.Install (remoteHost);
clientApps.Start (Seconds (0.01));
That’s all! You can now start your simulation as usual:
Simulator::Stop (Seconds (10.0));
Simulator::Run ();
Using the EPC with emulation mode
In the previous section we used PointToPoint links for the connection between the eNBs and the SGW (S1-U
interface) and among eNBs (X2-U and X2-C interfaces). The LTE module supports using emulated links
instead of PointToPoint links. This is achieved by just replacing the creation of LteHelper and EpcHelper
with the following code:
Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
Ptr<EmuEpcHelper> epcHelper = CreateObject<EmuEpcHelper> ();
lteHelper->SetEpcHelper (epcHelper);
epcHelper->Initialize ();
The attributes ns3::EmuEpcHelper::sgwDeviceName and ns3::EmuEpcHelper::enbDeviceName are used to set the
name of the devices used for transporting the S1-U, X2-U and X2-C interfaces at the SGW and eNB,
respectively. We will now show how this is done in an example where we execute the example program
lena-simple-epc-emu using two virtual ethernet interfaces.
First of all we build ns-3 appropriately:
# configure
./waf configure --enable-sudo --enable-modules=lte,fd-net-device --enable-examples
# build
./waf
Then we setup two virtual ethernet interfaces, and start wireshark to look at the traffic going through:
# note: you need to be root
# create two paired veth devices
ip link add name veth0 type veth peer name veth1
ip link show
# enable promiscuous mode
ip link set veth0 promisc on
ip link set veth1 promisc on
# bring interfaces up
ip link set veth0 up
ip link set veth1 up
# start wireshark and capture on veth0
wireshark &
We can now run the example program with the simulated clock:
./waf --run lena-simple-epc-emu --command="%s --ns3::EmuEpcHelper::sgwDeviceName=veth0
--ns3::EmuEpcHelper::enbDeviceName=veth1"
Using wireshark, you should see ARP resolution first, then some GTP packets exchanged both in uplink and
downlink.
The default setting of the example program is 1 eNB and 1UE. You can change this via command line
parameters, e.g.:
./waf --run lena-simple-epc-emu --command="%s --ns3::EmuEpcHelper::sgwDeviceName=veth0
--ns3::EmuEpcHelper::enbDeviceName=veth1 --nEnbs=2 --nUesPerEnb=2"
To get a list of the available parameters:
./waf --run lena-simple-epc-emu --command="%s --PrintHelp"
To run with the realtime clock: it turns out that the default debug build is too slow for realtime.
Softening the real time constraints with the BestEffort mode is not a good idea: something can go wrong
(e.g., ARP can fail) and, if so, you won’t get any data packets out. So you need a decent hardware and
the optimized build with statically linked modules:
./waf configure -d optimized --enable-static --enable-modules=lte --enable-examples
--enable-sudo
Then run the example program like this:
./waf --run lena-simple-epc-emu --command="%s --ns3::EmuEpcHelper::sgwDeviceName=veth0
--ns3::EmuEpcHelper::enbDeviceName=veth1
--SimulatorImplementationType=ns3::RealtimeSimulatorImpl
--ns3::RealtimeSimulatorImpl::SynchronizationMode=HardLimit"
note the HardLimit setting, which will cause the program to terminate if it cannot keep up with real
time.
The approach described in this section can be used with any type of net device. For instance, [Baldo2014]
describes how it was used to run an emulated LTE-EPC network over a real multi-layer packet-optical
transport network.
Custom Backhaul
In the previous sections, Evolved Packet Core (EPC), we explained how to write a simulation program using
EPC with a predefined backhaul network between the RAN and the EPC. We used the PointToPointEpcHelper.
This EpcHelper creates point-to-point links between the eNBs and the SGW.
We now explain how to write a simulation program that allows the simulator user to create any kind of
backhaul network in the simulation program.
First of all, in addition to LteHelper, you need to use the NoBackhaulEpcHelper class, which implements
an EPC but without connecting the eNBs with the core network. It just creates the network elements of the
core network:
Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
Ptr<NoBackhaulEpcHelper> epcHelper = CreateObject<NoBackhaulEpcHelper> ();
Then, as usual, you need to tell the LTE helper that the EPC will be used:
lteHelper->SetEpcHelper (epcHelper);
Now, you should create the backhaul network. Here we create point-to-point links as it is done by the
PointToPointEpcHelper. We assume you have a container for eNB nodes like this:
NodeContainer enbNodes;
We get the SGW node:
Ptr<Node> sgw = epcHelper->GetSgwNode ();
And we connect every eNB from the container with the SGW with a point-to-point link. We also assign IPv4
addresses to the interfaces of eNB and SGW with s1uIpv4AddressHelper.Assign (sgwEnbDevices) and finally
we tell the EpcHelper that this enb has a new S1 interface with epcHelper->AddS1Interface (enb,
enbS1uAddress, sgwS1uAddress), where enbS1uAddress and sgwS1uAddress are the IPv4 addresses of the eNB
and the SGW, respectively:
Ipv4AddressHelper s1uIpv4AddressHelper;
// Create networks of the S1 interfaces
s1uIpv4AddressHelper.SetBase ("10.0.0.0", "255.255.255.252");
for (uint16_t i = 0; i < enbNodes.GetN (); ++i)
{
Ptr<Node> enb = enbNodes.Get (i);
// Create a point to point link between the eNB and the SGW with
// the corresponding new NetDevices on each side
PointToPointHelper p2ph;
DataRate s1uLinkDataRate = DataRate ("10Gb/s");
uint16_t s1uLinkMtu = 2000;
Time s1uLinkDelay = Time (0);
p2ph.SetDeviceAttribute ("DataRate", DataRateValue (s1uLinkDataRate));
p2ph.SetDeviceAttribute ("Mtu", UintegerValue (s1uLinkMtu));
p2ph.SetChannelAttribute ("Delay", TimeValue (s1uLinkDelay));
NetDeviceContainer sgwEnbDevices = p2ph.Install (sgw, enb);
Ipv4InterfaceContainer sgwEnbIpIfaces = s1uIpv4AddressHelper.Assign (sgwEnbDevices);
s1uIpv4AddressHelper.NewNetwork ();
Ipv4Address sgwS1uAddress = sgwEnbIpIfaces.GetAddress (0);
Ipv4Address enbS1uAddress = sgwEnbIpIfaces.GetAddress (1);
// Create S1 interface between the SGW and the eNB
epcHelper->AddS1Interface (enb, enbS1uAddress, sgwS1uAddress);
}
This is just an example how to create a custom backhaul network. In this other example, we connect all
eNBs and the SGW to the same CSMA network:
// Create networks of the S1 interfaces
s1uIpv4AddressHelper.SetBase ("10.0.0.0", "255.255.255.0");
NodeContainer sgwEnbNodes;
sgwEnbNodes.Add (sgw);
sgwEnbNodes.Add (enbNodes);
CsmaHelper csmah;
NetDeviceContainer sgwEnbDevices = csmah.Install (sgwEnbNodes);
Ptr<NetDevice> sgwDev = sgwEnbDevices.Get (0);
Ipv4InterfaceContainer sgwEnbIpIfaces = s1uIpv4AddressHelper.Assign (sgwEnbDevices);
Ipv4Address sgwS1uAddress = sgwEnbIpIfaces.GetAddress (0);
for (uint16_t i = 0; i < enbNodes.GetN (); ++i)
{
Ptr<Node> enb = enbNodes.Get (i);
Ipv4Address enbS1uAddress = sgwEnbIpIfaces.GetAddress (i + 1);
// Create S1 interface between the SGW and the eNB
epcHelper->AddS1Interface (enb, enbS1uAddress, sgwS1uAddress);
}
As you can see, apart from how you create the backhaul network, i.e. the point-to-point links or the CSMA
network, the important point is to tell the EpcHelper that an eNB has a new S1 interface.
Now, you should continue configuring your simulation program as it is explained in Evolved Packet Core
(EPC) subsection. This configuration includes: the internet, installing the LTE eNBs and possibly
configuring other LTE aspects, installing the LTE UEs and configuring them as IP nodes, activation of the
dedicated EPS bearers and installing applications on the LTE UEs and on the remote hosts.
Network Attachment
As shown in the basic example in section Basic simulation program, attaching a UE to an eNodeB is done by
calling LteHelper::Attach function.
There are 2 possible ways of network attachment. The first method is the “manual” one, while the second
one has a more “automatic” sense on it. Each of them will be covered in this section.
Manual attachment
This method uses the LteHelper::Attach function mentioned above. It has been the only available network
attachment method in earlier versions of LTE module. It is typically invoked before the simulation
begins:
lteHelper->Attach (ueDevs, enbDev); // attach one or more UEs to a single eNodeB
LteHelper::InstallEnbDevice and LteHelper::InstallUeDevice functions must have been called before
attaching. In an EPC-enabled simulation, it is also required to have IPv4/IPv6 properly pre-installed in
the UE.
This method is very simple, but requires you to know exactly which UE belongs to to which eNodeB before
the simulation begins. This can be difficult when the UE initial position is randomly determined by the
simulation script.
One may choose the distance between the UE and the eNodeB as a criterion for selecting the appropriate
cell. It is quite simple (at least from the simulator’s point of view) and sometimes practical. But it is
important to note that sometimes distance does not make a single correct criterion. For instance, the
eNodeB antenna directivity should be considered as well. Besides that, one should also take into account
the channel condition, which might be fluctuating if there is fading or shadowing in effect. In these
kind of cases, network attachment should not be based on distance alone.
In real life, UE will automatically evaluate certain criteria and select the best cell to attach to,
without manual intervention from the user. Obviously this is not the case in this LteHelper::Attach
function. The other network attachment method uses more “automatic” approach to network attachment, as
will be described next.
Automatic attachment using Idle mode cell selection procedure
The strength of the received signal is the standard criterion used for selecting the best cell to attach
to. The use of this criterion is implemented in the initial cell selection process, which can be invoked
by calling another version of the LteHelper::Attach function, as shown below:
lteHelper->Attach (ueDevs); // attach one or more UEs to a strongest cell
The difference with the manual method is that the destination eNodeB is not specified. The procedure will
find the best cell for the UEs, based on several criteria, including the strength of the received signal
(RSRP).
After the method is called, the UE will spend some time to measure the neighbouring cells, and then
attempt to attach to the best one. More details can be found in section sec-initial-cell-selection of the
Design Documentation.
It is important to note that this method only works in EPC-enabled simulations. LTE-only simulations
must resort to manual attachment method.
Closed Subscriber Group
An interesting use case of the initial cell selection process is to setup a simulation environment with
Closed Subscriber Group (CSG).
For example, a certain eNodeB, typically a smaller version such as femtocell, might belong to a private
owner (e.g. a household or business), allowing access only to some UEs which have been previously
registered by the owner. The eNodeB and the registered UEs altogether form a CSG.
The access restriction can be simulated by “labeling” the CSG members with the same CSG ID. This is done
through the attributes in both eNodeB and UE, for example using the following LteHelper functions:
// label the following eNodeBs with CSG identity of 1 and CSG indication enabled
lteHelper->SetEnbDeviceAttribute ("CsgId", UintegerValue (1));
lteHelper->SetEnbDeviceAttribute ("CsgIndication", BooleanValue (true));
// label one or more UEs with CSG identity of 1
lteHelper->SetUeDeviceAttribute ("CsgId", UintegerValue (1));
// install the eNodeBs and UEs
NetDeviceContainer csgEnbDevs = lteHelper->InstallEnbDevice (csgEnbNodes);
NetDeviceContainer csgUeDevs = lteHelper->InstallUeDevice (csgUeNodes);
Then enable the initial cell selection procedure on the UEs:
lteHelper->Attach (csgUeDevs);
This is necessary because the CSG restriction only works with automatic method of network attachment, but
not in the manual method.
Note that setting the CSG indication of an eNodeB as false (the default value) will disable the
restriction, i.e., any UEs can connect to this eNodeB.
Configure UE measurements
The active UE measurement configuration in a simulation is dictated by the selected so called
“consumers”, such as handover algorithm. Users may add their own configuration into action, and there are
several ways to do so:
1. direct configuration in eNodeB RRC entity;
2. configuring existing handover algorithm; and
3. developing a new handover algorithm.
This section will cover the first method only. The second method is covered in Automatic handover
trigger, while the third method is explained in length in Section sec-handover-algorithm of the Design
Documentation.
Direct configuration in eNodeB RRC works as follows. User begins by creating a new
LteRrcSap::ReportConfigEutra instance and pass it to the LteEnbRrc::AddUeMeasReportConfig function. The
function will return the measId (measurement identity) which is a unique reference of the configuration
in the eNodeB instance. This function must be called before the simulation begins. The measurement
configuration will be active in all UEs attached to the eNodeB throughout the duration of the simulation.
During the simulation, user can capture the measurement reports produced by the UEs by listening to the
existing LteEnbRrc::RecvMeasurementReport trace source.
The structure ReportConfigEutra is in accord with 3GPP specification. Definition of the structure and
each member field can be found in Section 6.3.5 of [TS36331].
The code sample below configures Event A1 RSRP measurement to every eNodeB within the container devs:
LteRrcSap::ReportConfigEutra config;
config.eventId = LteRrcSap::ReportConfigEutra::EVENT_A1;
config.threshold1.choice = LteRrcSap::ThresholdEutra::THRESHOLD_RSRP;
config.threshold1.range = 41;
config.triggerQuantity = LteRrcSap::ReportConfigEutra::RSRP;
config.reportInterval = LteRrcSap::ReportConfigEutra::MS480;
std::vector<uint8_t> measIdList;
NetDeviceContainer::Iterator it;
for (it = devs.Begin (); it != devs.End (); it++)
{
Ptr<NetDevice> dev = *it;
Ptr<LteEnbNetDevice> enbDev = dev->GetObject<LteEnbNetDevice> ();
Ptr<LteEnbRrc> enbRrc = enbDev->GetRrc ();
uint8_t measId = enbRrc->AddUeMeasReportConfig (config);
measIdList.push_back (measId); // remember the measId created
enbRrc->TraceConnect ("RecvMeasurementReport",
"context",
MakeCallback (&RecvMeasurementReportCallback));
}
Note that thresholds are expressed as range. In the example above, the range 41 for RSRP corresponds to
-100 dBm. The conversion from and to the range format is due to Section 9.1.4 and 9.1.7 of [TS36133]. The
EutranMeasurementMapping class has several static functions that can be used for this purpose.
The corresponding callback function would have a definition similar as below:
void
RecvMeasurementReportCallback (std::string context,
uint64_t imsi,
uint16_t cellId,
uint16_t rnti,
LteRrcSap::MeasurementReport measReport);
This method will register the callback function as a consumer of UE measurements. In the case where there
are more than one consumers in the simulation (e.g. handover algorithm), the measurements intended for
other consumers will also be captured by this callback function. Users may utilize the the measId field,
contained within the LteRrcSap::MeasurementReport argument of the callback function, to tell which
measurement configuration has triggered the report.
In general, this mechanism prevents one consumer to unknowingly intervene with another consumer’s
reporting configuration.
Note that only the reporting configuration part (i.e. LteRrcSap::ReportConfigEutra) of the UE
measurements parameter is open for consumers to configure, while the other parts are kept hidden. The
intra-frequency limitation is the main motivation behind this API implementation decision:
• there is only one, unambiguous and definitive measurement object, thus there is no need to configure
it;
• measurement identities are kept hidden because of the fact that there is one-to-one mapping between
reporting configuration and measurement identity, thus a new measurement identity is set up
automatically when a new reporting configuration is created;
• quantity configuration is configured elsewhere, see sec-performing-measurements; and
• measurement gaps are not supported, because it is only applicable for inter-frequency settings;
X2-based handover
As defined by 3GPP, handover is a procedure for changing the serving cell of a UE in CONNECTED mode. The
two eNodeBs involved in the process are typically called the source eNodeB and the target eNodeB.
In order to enable the execution of X2-based handover in simulation, there are two requirements that must
be met. Firstly, EPC must be enabled in the simulation (see Evolved Packet Core (EPC)).
Secondly, an X2 interface must be configured between the two eNodeBs, which needs to be done explicitly
within the simulation program:
lteHelper->AddX2Interface (enbNodes);
where enbNodes is a NodeContainer that contains the two eNodeBs between which the X2 interface is to be
configured. If the container has more than two eNodeBs, the function will create an X2 interface between
every pair of eNodeBs in the container.
Lastly, the target eNodeB must be configured as “open” to X2 HANDOVER REQUEST. Every eNodeB is open by
default, so no extra instruction is needed in most cases. However, users may set the eNodeB to “closed”
by setting the boolean attribute LteEnbRrc::AdmitHandoverRequest to false. As an example, you can run the
lena-x2-handover program and setting the attribute in this way:
NS_LOG=EpcX2:LteEnbRrc ./waf --run lena-x2-handover --command="%s --ns3::LteEnbRrc::AdmitHandoverRequest=false"
After the above three requirements are fulfilled, the handover procedure can be triggered manually or
automatically. Each will be presented in the following subsections.
Manual handover trigger
Handover event can be triggered “manually” within the simulation program by scheduling an explicit
handover event. The LteHelper object provides a convenient method for the scheduling of a handover event.
As an example, let us assume that ueLteDevs is a NetDeviceContainer that contains the UE that is to be
handed over, and that enbLteDevs is another NetDeviceContainer that contains the source and the target
eNB. Then, a handover at 0.1s can be scheduled like this:
lteHelper->HandoverRequest (Seconds (0.100),
ueLteDevs.Get (0),
enbLteDevs.Get (0),
enbLteDevs.Get (1));
Note that the UE needs to be already connected to the source eNB, otherwise the simulation will terminate
with an error message.
For an example with full source code, please refer to the lena-x2-handover example program.
Automatic handover trigger
Handover procedure can also be triggered “automatically” by the serving eNodeB of the UE. The logic
behind the trigger depends on the handover algorithm currently active in the eNodeB RRC entity. Users may
select and configure the handover algorithm that will be used in the simulation, which will be explained
shortly in this section. Users may also opt to write their own implementation of handover algorithm, as
described in Section sec-handover-algorithm of the Design Documentation.
Selecting a handover algorithm is done via the LteHelper object and its SetHandoverAlgorithmType method
as shown below:
Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
lteHelper->SetHandoverAlgorithmType ("ns3::A2A4RsrqHandoverAlgorithm");
The selected handover algorithm may also provide several configurable attributes, which can be set as
follows:
lteHelper->SetHandoverAlgorithmAttribute ("ServingCellThreshold",
UintegerValue (30));
lteHelper->SetHandoverAlgorithmAttribute ("NeighbourCellOffset",
UintegerValue (1));
Three options of handover algorithm are included in the LTE module. The A2-A4-RSRQ handover algorithm
(named as ns3::A2A4RsrqHandoverAlgorithm) is the default option, and the usage has already been shown
above.
Another option is the strongest cell handover algorithm (named as ns3::A3RsrpHandoverAlgorithm), which
can be selected and configured by the following code:
lteHelper->SetHandoverAlgorithmType ("ns3::A3RsrpHandoverAlgorithm");
lteHelper->SetHandoverAlgorithmAttribute ("Hysteresis",
DoubleValue (3.0));
lteHelper->SetHandoverAlgorithmAttribute ("TimeToTrigger",
TimeValue (MilliSeconds (256)));
The last option is a special one, called the no-op handover algorithm, which basically disables automatic
handover trigger. This is useful for example in cases where manual handover trigger need an exclusive
control of all handover decision. It does not have any configurable attributes. The usage is as follows:
lteHelper->SetHandoverAlgorithmType ("ns3::NoOpHandoverAlgorithm");
For more information on each handover algorithm’s decision policy and their attributes, please refer to
their respective subsections in Section sec-handover-algorithm of the Design Documentation.
Finally, the InstallEnbDevice function of LteHelper will instantiate one instance of the selected
handover algorithm for each eNodeB device. In other words, make sure to select the right handover
algorithm before finalizing it in the following line of code:
NetDeviceContainer enbLteDevs = lteHelper->InstallEnbDevice (enbNodes);
Example with full source code of using automatic handover trigger can be found in the
lena-x2-handover-measures example program.
Tuning simulation with handover
As mentioned in the Design Documentation, the current implementation of handover model may produce
unpredicted behaviour when handover failure occurs. This subsection will focus on the steps that should
be taken into account by users if they plan to use handover in their simulations.
The major cause of handover failure that we will tackle is the error in transmitting handover-related
signaling messages during the execution of a handover procedure. As apparent from the Figure
fig-x2-based-handover-seq-diagram from the Design Documentation, there are many of them and they use
different interfaces and protocols. For the sake of simplicity, we can safely assume that the X2
interface (between the source eNodeB and the target eNodeB) and the S1 interface (between the target
eNodeB and the SGW/PGW) are quite stable. Therefore we will focus our attention to the RRC protocol
(between the UE and the eNodeBs) and the Random Access procedure, which are normally transmitted through
the air and susceptible to degradation of channel condition.
A general tips to reduce transmission error is to ensure high enough SINR level in every UE. This can be
done by a proper planning of the network topology that minimizes network coverage hole. If the topology
has a known coverage hole, then the UE should be configured not to venture to that area.
Another approach to keep in mind is to avoid too-late handovers. In other words, handover should happen
before the UE’s SINR becomes too low, otherwise the UE may fail to receive the handover command from the
source eNodeB. Handover algorithms have the means to control how early or late a handover decision is
made. For example, A2-A4-RSRQ handover algorithm can be configured with a higher threshold to make it
decide a handover earlier. Similarly, smaller hysteresis and/or shorter time-to-trigger in the strongest
cell handover algorithm typically results in earlier handovers. In order to find the right values for
these parameters, one of the factors that should be considered is the UE movement speed. Generally, a
faster moving UE requires the handover to be executed earlier. Some research work have suggested
recommended values, such as in [Lee2010].
The above tips should be enough in normal simulation uses, but in the case some special needs arise then
an extreme measure can be taken into consideration. For instance, users may consider disabling the
channel error models. This will ensure that all handover-related signaling messages will be transmitted
successfully, regardless of distance and channel condition. However, it will also affect all other data
or control packets not related to handover, which may be an unwanted side effect. Otherwise, it can be
done as follows:
Config::SetDefault ("ns3::LteSpectrumPhy::CtrlErrorModelEnabled", BooleanValue (false));
Config::SetDefault ("ns3::LteSpectrumPhy::DataErrorModelEnabled", BooleanValue (false));
By using the above code, we disable the error model in both control and data channels and in both
directions (downlink and uplink). This is necessary because handover-related signaling messages are
transmitted using these channels. An exception is when the simulation uses the ideal RRC protocol. In
this case, only the Random Access procedure is left to be considered. The procedure consists of control
messages, therefore we only need to disable the control channel’s error model.
Handover traces
The RRC model, in particular the LteEnbRrc and LteUeRrc objects, provide some useful traces which can be
hooked up to some custom functions so that they are called upon start and end of the handover execution
phase at both the UE and eNB side. As an example, in your simulation program you can declare the
following methods:
void
NotifyHandoverStartUe (std::string context,
uint64_t imsi,
uint16_t cellId,
uint16_t rnti,
uint16_t targetCellId)
{
std::cout << Simulator::Now ().GetSeconds () << " " << context
<< " UE IMSI " << imsi
<< ": previously connected to CellId " << cellId
<< " with RNTI " << rnti
<< ", doing handover to CellId " << targetCellId
<< std::endl;
}
void
NotifyHandoverEndOkUe (std::string context,
uint64_t imsi,
uint16_t cellId,
uint16_t rnti)
{
std::cout << Simulator::Now ().GetSeconds () << " " << context
<< " UE IMSI " << imsi
<< ": successful handover to CellId " << cellId
<< " with RNTI " << rnti
<< std::endl;
}
void
NotifyHandoverStartEnb (std::string context,
uint64_t imsi,
uint16_t cellId,
uint16_t rnti,
uint16_t targetCellId)
{
std::cout << Simulator::Now ().GetSeconds () << " " << context
<< " eNB CellId " << cellId
<< ": start handover of UE with IMSI " << imsi
<< " RNTI " << rnti
<< " to CellId " << targetCellId
<< std::endl;
}
void
NotifyHandoverEndOkEnb (std::string context,
uint64_t imsi,
uint16_t cellId,
uint16_t rnti)
{
std::cout << Simulator::Now ().GetSeconds () << " " << context
<< " eNB CellId " << cellId
<< ": completed handover of UE with IMSI " << imsi
<< " RNTI " << rnti
<< std::endl;
}
Then, you can hook up these methods to the corresponding trace sources like this:
Config::Connect ("/NodeList/*/DeviceList/*/LteEnbRrc/HandoverStart",
MakeCallback (&NotifyHandoverStartEnb));
Config::Connect ("/NodeList/*/DeviceList/*/LteUeRrc/HandoverStart",
MakeCallback (&NotifyHandoverStartUe));
Config::Connect ("/NodeList/*/DeviceList/*/LteEnbRrc/HandoverEndOk",
MakeCallback (&NotifyHandoverEndOkEnb));
Config::Connect ("/NodeList/*/DeviceList/*/LteUeRrc/HandoverEndOk",
MakeCallback (&NotifyHandoverEndOkUe));
The example program src/lte/examples/lena-x2-handover.cc illustrates how the all above instructions can
be integrated in a simulation program. You can run the program like this:
./waf --run lena-x2-handover
and it will output the messages printed by the custom handover trace hooks. In order additionally
visualize some meaningful logging information, you can run the program like this:
NS_LOG=LteEnbRrc:LteUeRrc:EpcX2 ./waf --run lena-x2-handover
Frequency Reuse Algorithms
In this section we will describe how to use Frequency Reuse Algorithms in eNb within LTE simulations.
There are two possible ways of configuration. The first approach is the “manual” one, it requires more
parameters to be configured, but allow user to configure FR algorithm as he/she needs. The second
approach is more “automatic”. It is very convenient, because is the same for each FR algorithm, so user
can switch FR algorithm very quickly by changing only type of FR algorithm. One drawback is that
“automatic” approach uses only limited set of configurations for each algorithm, what make it less
flexible, but is sufficient for most of cases.
These two approaches will be described more in following sub-section.
If user do not configure Frequency Reuse algorithm, default one (i.e. LteFrNoOpAlgorithm) is installed in
eNb. It acts as if FR algorithm was disabled.
One thing that should be mentioned is that most of implemented FR algorithms work with cell bandwidth
greater or equal than 15 RBs. This limitation is caused by requirement that at least three continuous RBs
have to be assigned to UE for transmission.
Manual configuration
Frequency reuse algorithm can be configured “manually” within the simulation program by setting type of
FR algorithm and all its attributes. Currently, seven FR algorithms are implemented:
• ns3::LteFrNoOpAlgorithm
• ns3::LteFrHardAlgorithm
• ns3::LteFrStrictAlgorithm
• ns3::LteFrSoftAlgorithm
• ns3::LteFfrSoftAlgorithm
• ns3::LteFfrEnhancedAlgorithm
• ns3::LteFfrDistributedAlgorithm
Selecting a FR algorithm is done via the LteHelper object and its SetFfrAlgorithmType method as shown
below:
Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
lteHelper->SetFfrAlgorithmType ("ns3::LteFrHardAlgorithm");
Each implemented FR algorithm provide several configurable attributes. Users do not have to care about UL
and DL bandwidth configuration, because it is done automatically during cell configuration. To change
bandwidth for FR algorithm, configure required values for LteEnbNetDevice:
uint8_t bandwidth = 100;
lteHelper->SetEnbDeviceAttribute ("DlBandwidth", UintegerValue (bandwidth));
lteHelper->SetEnbDeviceAttribute ("UlBandwidth", UintegerValue (bandwidth));
Now, each FR algorithms configuration will be described.
Hard Frequency Reuse Algorithm
As described in Section sec-fr-hard-algorithm of the Design Documentation ns3::LteFrHardAlgorithm uses
one sub-band. To configure this sub-band user need to specify offset and bandwidth for DL and UL in
number of RBs.
Hard Frequency Reuse Algorithm provides following attributes:
• DlSubBandOffset: Downlink Offset in number of Resource Block Groups
• DlSubBandwidth: Downlink Transmission SubBandwidth Configuration in number of Resource Block Groups
• UlSubBandOffset: Uplink Offset in number of Resource Block Groups
• UlSubBandwidth: Uplink Transmission SubBandwidth Configuration in number of Resource Block Groups
Example configuration of LteFrHardAlgorithm can be done in following way:
lteHelper->SetFfrAlgorithmType ("ns3::LteFrHardAlgorithm");
lteHelper->SetFfrAlgorithmAttribute ("DlSubBandOffset", UintegerValue (8));
lteHelper->SetFfrAlgorithmAttribute ("DlSubBandwidth", UintegerValue (8));
lteHelper->SetFfrAlgorithmAttribute ("UlSubBandOffset", UintegerValue (8));
lteHelper->SetFfrAlgorithmAttribute ("UlSubBandwidth", UintegerValue (8));
NetDeviceContainer enbDevs = lteHelper->InstallEnbDevice (enbNodes.Get(0));
Above example allow eNB to use only RBs from 8 to 16 in DL and UL, while entire cell bandwidth is 25.
Strict Frequency Reuse Algorithm
Strict Frequency Reuse Algorithm uses two sub-bands: one common for each cell and one private. There is
also RSRQ threshold, which is needed to decide within which sub-band UE should be served. Moreover the
power transmission in these sub-bands can be different.
Strict Frequency Reuse Algorithm provides following attributes:
• UlCommonSubBandwidth: Uplink Common SubBandwidth Configuration in number of Resource Block Groups
• UlEdgeSubBandOffset: Uplink Edge SubBand Offset in number of Resource Block Groups
• UlEdgeSubBandwidth: Uplink Edge SubBandwidth Configuration in number of Resource Block Groups
• DlCommonSubBandwidth: Downlink Common SubBandwidth Configuration in number of Resource Block Groups
• DlEdgeSubBandOffset: Downlink Edge SubBand Offset in number of Resource Block Groups
• DlEdgeSubBandwidth: Downlink Edge SubBandwidth Configuration in number of Resource Block Groups
• RsrqThreshold: If the RSRQ of is worse than this threshold, UE should be served in edge sub-band
• CenterPowerOffset: PdschConfigDedicated::Pa value for center sub-band, default value dB0
• EdgePowerOffset: PdschConfigDedicated::Pa value for edge sub-band, default value dB0
• CenterAreaTpc: TPC value which will be set in DL-DCI for UEs in center area, Absolute mode is used,
default value 1 is mapped to -1 according to TS36.213 Table 5.1.1.1-2
• EdgeAreaTpc: TPC value which will be set in DL-DCI for UEs in edge area, Absolute mode is used,
default value 1 is mapped to -1 according to TS36.213 Table 5.1.1.1-2
Example below allow eNB to use RBs from 0 to 6 as common sub-band and from 12 to 18 as private sub-band
in DL and UL, RSRQ threshold is 20 dB, power in center area equals LteEnbPhy::TxPower - 3dB, power in
edge area equals LteEnbPhy::TxPower + 3dB:
lteHelper->SetFfrAlgorithmType ("ns3::LteFrStrictAlgorithm");
lteHelper->SetFfrAlgorithmAttribute ("DlCommonSubBandwidth", UintegerValue (6));
lteHelper->SetFfrAlgorithmAttribute ("UlCommonSubBandwidth", UintegerValue (6));
lteHelper->SetFfrAlgorithmAttribute ("DlEdgeSubBandOffset", UintegerValue (6));
lteHelper->SetFfrAlgorithmAttribute ("DlEdgeSubBandwidth", UintegerValue (6));
lteHelper->SetFfrAlgorithmAttribute ("UlEdgeSubBandOffset", UintegerValue (6));
lteHelper->SetFfrAlgorithmAttribute ("UlEdgeSubBandwidth", UintegerValue (6));
lteHelper->SetFfrAlgorithmAttribute ("RsrqThreshold", UintegerValue (20));
lteHelper->SetFfrAlgorithmAttribute ("CenterPowerOffset",
UintegerValue (LteRrcSap::PdschConfigDedicated::dB_3));
lteHelper->SetFfrAlgorithmAttribute ("EdgePowerOffset",
UintegerValue (LteRrcSap::PdschConfigDedicated::dB3));
lteHelper->SetFfrAlgorithmAttribute ("CenterAreaTpc", UintegerValue (1));
lteHelper->SetFfrAlgorithmAttribute ("EdgeAreaTpc", UintegerValue (2));
NetDeviceContainer enbDevs = lteHelper->InstallEnbDevice (enbNodes.Get(0));
Soft Frequency Reuse Algorithm
With Soft Frequency Reuse Algorithm, eNb uses entire cell bandwidth, but there are two sub-bands, within
UEs are served with different power level.
Soft Frequency Reuse Algorithm provides following attributes:
• UlEdgeSubBandOffset: Uplink Edge SubBand Offset in number of Resource Block Groups
• UlEdgeSubBandwidth: Uplink Edge SubBandwidth Configuration in number of Resource Block Groups
• DlEdgeSubBandOffset: Downlink Edge SubBand Offset in number of Resource Block Groups
• DlEdgeSubBandwidth: Downlink Edge SubBandwidth Configuration in number of Resource Block Groups
• AllowCenterUeUseEdgeSubBand: If true center UEs can receive on edge sub-band RBGs, otherwise edge
sub-band is allowed only for edge UEs, default value is true
• RsrqThreshold: If the RSRQ of is worse than this threshold, UE should be served in edge sub-band
• CenterPowerOffset: PdschConfigDedicated::Pa value for center sub-band, default value dB0
• EdgePowerOffset: PdschConfigDedicated::Pa value for edge sub-band, default value dB0
• CenterAreaTpc: TPC value which will be set in DL-DCI for UEs in center area, Absolute mode is used,
default value 1 is mapped to -1 according to TS36.213 Table 5.1.1.1-2
• EdgeAreaTpc: TPC value which will be set in DL-DCI for UEs in edge area, Absolute mode is used,
default value 1 is mapped to -1 according to TS36.213 Table 5.1.1.1-2
Example below configures RBs from 8 to 16 to be used by cell edge UEs and this sub-band is not available
for cell center users. RSRQ threshold is 20 dB, power in center area equals LteEnbPhy::TxPower, power in
edge area equals LteEnbPhy::TxPower + 3dB:
lteHelper->SetFfrAlgorithmType ("ns3::LteFrSoftAlgorithm");
lteHelper->SetFfrAlgorithmAttribute ("DlEdgeSubBandOffset", UintegerValue (8));
lteHelper->SetFfrAlgorithmAttribute ("DlEdgeSubBandwidth", UintegerValue (8));
lteHelper->SetFfrAlgorithmAttribute ("UlEdgeSubBandOffset", UintegerValue (8));
lteHelper->SetFfrAlgorithmAttribute ("UlEdgeSubBandwidth", UintegerValue (8));
lteHelper->SetFfrAlgorithmAttribute ("AllowCenterUeUseEdgeSubBand", BooleanValue (false));
lteHelper->SetFfrAlgorithmAttribute ("RsrqThreshold", UintegerValue (20));
lteHelper->SetFfrAlgorithmAttribute ("CenterPowerOffset",
UintegerValue (LteRrcSap::PdschConfigDedicated::dB0));
lteHelper->SetFfrAlgorithmAttribute ("EdgePowerOffset",
UintegerValue (LteRrcSap::PdschConfigDedicated::dB3));
NetDeviceContainer enbDevs = lteHelper->InstallEnbDevice (enbNodes.Get(0));
Soft Fractional Frequency Reuse Algorithm
Soft Fractional Frequency Reuse (SFFR) uses three sub-bands: center, medium (common) and edge. User have
to configure only two of them: common and edge. Center sub-band will be composed from the remaining
bandwidth. Each sub-band can be served with different transmission power. Since there are three
sub-bands, two RSRQ thresholds needs to be configured.
Soft Fractional Frequency Reuse Algorithm provides following attributes:
• UlCommonSubBandwidth: Uplink Common SubBandwidth Configuration in number of Resource Block Groups
• UlEdgeSubBandOffset: Uplink Edge SubBand Offset in number of Resource Block Groups
• UlEdgeSubBandwidth: Uplink Edge SubBandwidth Configuration in number of Resource Block Groups
• DlCommonSubBandwidth: Downlink Common SubBandwidth Configuration in number of Resource Block Groups
• DlEdgeSubBandOffset: Downlink Edge SubBand Offset in number of Resource Block Groups
• DlEdgeSubBandwidth: Downlink Edge SubBandwidth Configuration in number of Resource Block Groups
• CenterRsrqThreshold: If the RSRQ of is worse than this threshold, UE should be served in medium
sub-band
• EdgeRsrqThreshold: If the RSRQ of is worse than this threshold, UE should be served in edge sub-band
• CenterAreaPowerOffset: PdschConfigDedicated::Pa value for center sub-band, default value dB0
• MediumAreaPowerOffset: PdschConfigDedicated::Pa value for medium sub-band, default value dB0
• EdgeAreaPowerOffset: PdschConfigDedicated::Pa value for edge sub-band, default value dB0
• CenterAreaTpc: TPC value which will be set in DL-DCI for UEs in center area, Absolute mode is used,
default value 1 is mapped to -1 according to TS36.213 Table 5.1.1.1-2
• MediumAreaTpc: TPC value which will be set in DL-DCI for UEs in medium area, Absolute mode is used,
default value 1 is mapped to -1 according to TS36.213 Table 5.1.1.1-2
• EdgeAreaTpc: TPC value which will be set in DL-DCI for UEs in edge area, Absolute mode is used,
default value 1 is mapped to -1 according to TS36.213 Table 5.1.1.1-2
In example below RBs from 0 to 6 will be used as common (medium) sub-band, RBs from 6 to 12 will be used
as edge sub-band and RBs from 12 to 24 will be used as center sub-band (it is composed with remaining
RBs). RSRQ threshold between center and medium area is 28 dB, RSRQ threshold between medium and edge area
is 18 dB. Power in center area equals LteEnbPhy::TxPower - 3dB, power in medium area equals
LteEnbPhy::TxPower + 3dB, power in edge area equals LteEnbPhy::TxPower + 3dB:
lteHelper->SetFfrAlgorithmType ("ns3::LteFfrSoftAlgorithm");
lteHelper->SetFfrAlgorithmAttribute ("UlCommonSubBandwidth", UintegerValue (6));
lteHelper->SetFfrAlgorithmAttribute ("DlCommonSubBandwidth", UintegerValue (6));
lteHelper->SetFfrAlgorithmAttribute ("DlEdgeSubBandOffset", UintegerValue (0));
lteHelper->SetFfrAlgorithmAttribute ("DlEdgeSubBandwidth", UintegerValue (6));
lteHelper->SetFfrAlgorithmAttribute ("UlEdgeSubBandOffset", UintegerValue (0));
lteHelper->SetFfrAlgorithmAttribute ("UlEdgeSubBandwidth", UintegerValue (6));
lteHelper->SetFfrAlgorithmAttribute ("CenterRsrqThreshold", UintegerValue (28));
lteHelper->SetFfrAlgorithmAttribute ("EdgeRsrqThreshold", UintegerValue (18));
lteHelper->SetFfrAlgorithmAttribute ("CenterAreaPowerOffset",
UintegerValue (LteRrcSap::PdschConfigDedicated::dB_3));
lteHelper->SetFfrAlgorithmAttribute ("MediumAreaPowerOffset",
UintegerValue (LteRrcSap::PdschConfigDedicated::dB0));
lteHelper->SetFfrAlgorithmAttribute ("EdgeAreaPowerOffset",
UintegerValue (LteRrcSap::PdschConfigDedicated::dB3));
NetDeviceContainer enbDevs = lteHelper->InstallEnbDevice (enbNodes.Get(0));
Enhanced Fractional Frequency Reuse Algorithm
Enhanced Fractional Frequency Reuse (EFFR) reserve part of system bandwidth for each cell (typically
there are 3 cell types and each one gets 1/3 of system bandwidth). Then part of this subbandwidth it used
as Primary Segment with reuse factor 3 and as Secondary Segment with reuse factor 1. User has to
configure (for DL and UL) offset of the cell subbandwidth in number of RB, number of RB which will be
used as Primary Segment and number of RB which will be used as Secondary Segment. Primary Segment is used
by cell at will, but RBs from Secondary Segment can be assigned to UE only is CQI feedback from this UE
have higher value than configured CQI threshold. UE is considered as edge UE when its RSRQ is lower than
RsrqThreshold.
Since each eNb needs to know where are Primary and Secondary of other cell types, it will calculate them
assuming configuration is the same for each cell and only subbandwidth offsets are different. So it is
important to divide available system bandwidth equally to each cell and apply the same configuration of
Primary and Secondary Segments to them.
Enhanced Fractional Frequency Reuse Algorithm provides following attributes:
• UlSubBandOffset: Uplink SubBand Offset for this cell in number of Resource Block Groups
• UlReuse3SubBandwidth: Uplink Reuse 3 SubBandwidth Configuration in number of Resource Block Groups
• UlReuse1SubBandwidth: Uplink Reuse 1 SubBandwidth Configuration in number of Resource Block Groups
• DlSubBandOffset: Downlink SubBand Offset for this cell in number of Resource Block Groups
• DlReuse3SubBandwidth: Downlink Reuse 3 SubBandwidth Configuration in number of Resource Block Groups
• DlReuse1SubBandwidth: Downlink Reuse 1 SubBandwidth Configuration in number of Resource Block Groups
• RsrqThreshold: If the RSRQ of is worse than this threshold, UE should be served in edge sub-band
• CenterAreaPowerOffset: PdschConfigDedicated::Pa value for center sub-band, default value dB0
• EdgeAreaPowerOffset: PdschConfigDedicated::Pa value for edge sub-band, default value dB0
• DlCqiThreshold: If the DL-CQI for RBG of is higher than this threshold, transmission on RBG is
possible
• UlCqiThreshold: If the UL-CQI for RBG of is higher than this threshold, transmission on RBG is
possible
• CenterAreaTpc: TPC value which will be set in DL-DCI for UEs in center area, Absolute mode is used,
default value 1 is mapped to -1 according to TS36.213 Table 5.1.1.1-2
• EdgeAreaTpc: TPC value which will be set in DL-DCI for UEs in edge area, Absolute mode is used,
default value 1 is mapped to -1 according to TS36.213 Table 5.1.1.1-2
In example below offset in DL and UL is 0 RB, 4 RB will be used in Primary Segment and Secondary Segment.
RSRQ threshold between center and edge area is 25 dB. DL and UL CQI thresholds are set to value of 10.
Power in center area equals LteEnbPhy::TxPower - 6dB, power in edge area equals LteEnbPhy::TxPower + 0dB:
lteHelper->SetFfrAlgorithmType("ns3::LteFfrEnhancedAlgorithm");
lteHelper->SetFfrAlgorithmAttribute("RsrqThreshold", UintegerValue (25));
lteHelper->SetFfrAlgorithmAttribute("DlCqiThreshold", UintegerValue (10));
lteHelper->SetFfrAlgorithmAttribute("UlCqiThreshold", UintegerValue (10));
lteHelper->SetFfrAlgorithmAttribute("CenterAreaPowerOffset",
UintegerValue (LteRrcSap::PdschConfigDedicated::dB_6));
lteHelper->SetFfrAlgorithmAttribute("EdgeAreaPowerOffset",
UintegerValue (LteRrcSap::PdschConfigDedicated::dB0));
lteHelper->SetFfrAlgorithmAttribute("UlSubBandOffset", UintegerValue (0));
lteHelper->SetFfrAlgorithmAttribute("UlReuse3SubBandwidth", UintegerValue (4));
lteHelper->SetFfrAlgorithmAttribute("UlReuse1SubBandwidth", UintegerValue (4));
lteHelper->SetFfrAlgorithmAttribute("DlSubBandOffset", UintegerValue (0));
lteHelper->SetFfrAlgorithmAttribute("DlReuse3SubBandwidth", UintegerValue (4));
lteHelper->SetFfrAlgorithmAttribute("DlReuse1SubBandwidth", UintegerValue (4));
Distributed Fractional Frequency Reuse Algorithm
Distributed Fractional Frequency Reuse requires X2 interface between all eNB to be installed. X2
interfaces can be installed only when EPC is configured, so this FFR scheme can be used only with EPC
scenarios.
With Distributed Fractional Frequency Reuse Algorithm, eNb uses entire cell bandwidth and there can be
two sub-bands: center sub-band and edge sub-band . Within these sub-bands UEs can be served with
different power level. Algorithm adaptively selects RBs for cell-edge sub-band on basis of coordination
information (i.e. RNTP) from adjecent cells and notifies the base stations of the adjacent cells, which
RBs it selected to use in edge sub-band. If there are no UE classified as edge UE in cell, eNB will not
use any RBs as edge sub-band.
Distributed Fractional Frequency Reuse Algorithm provides following attributes:
• CalculationInterval: Time interval between calculation of Edge sub-band, Default value 1 second
• RsrqThreshold: If the RSRQ of is worse than this threshold, UE should be served in edge sub-band
• RsrpDifferenceThreshold: If the difference between the power of the signal received by UE from the
serving cell and the power of the signal received from the adjacent cell is less than a
RsrpDifferenceThreshold value, the cell weight is incremented
• CenterPowerOffset: PdschConfigDedicated::Pa value for edge sub-band, default value dB0
• EdgePowerOffset: PdschConfigDedicated::Pa value for edge sub-band, default value dB0
• EdgeRbNum: Number of RB that can be used in edge sub-band
• CenterAreaTpc: TPC value which will be set in DL-DCI for UEs in center area, Absolute mode is used,
default value 1 is mapped to -1 according to TS36.213 Table 5.1.1.1-2
• EdgeAreaTpc: TPC value which will be set in DL-DCI for UEs in edge area, Absolute mode is used,
default value 1 is mapped to -1 according to TS36.213 Table 5.1.1.1-2
In example below calculation interval is 500 ms. RSRQ threshold between center and edge area is 25. RSRP
Difference Threshold is set to be 5. In DL and UL 6 RB will be used by each cell in edge sub-band. Power
in center area equals LteEnbPhy::TxPower - 0dB, power in edge area equals LteEnbPhy::TxPower + 3dB:
lteHelper->SetFfrAlgorithmType("ns3::LteFfrDistributedAlgorithm");
lteHelper->SetFfrAlgorithmAttribute("CalculationInterval", TimeValue(MilliSeconds(500)));
lteHelper->SetFfrAlgorithmAttribute ("RsrqThreshold", UintegerValue (25));
lteHelper->SetFfrAlgorithmAttribute ("RsrpDifferenceThreshold", UintegerValue (5));
lteHelper->SetFfrAlgorithmAttribute ("EdgeRbNum", UintegerValue (6));
lteHelper->SetFfrAlgorithmAttribute ("CenterPowerOffset",
UintegerValue (LteRrcSap::PdschConfigDedicated::dB0));
lteHelper->SetFfrAlgorithmAttribute ("EdgePowerOffset",
UintegerValue (LteRrcSap::PdschConfigDedicated::dB3));
Automatic configuration
Frequency Reuse algorithms can also be configured in more “automatic” way by setting only the bandwidth
and FrCellTypeId. During initialization of FR instance, configuration for set bandwidth and FrCellTypeId
will be taken from configuration table. It is important that only sub-bands will be configured,
thresholds and transmission power will be set to default values. If one wants, he/she can change
thresholds and transmission power as show in previous sub-section.
There are three FrCellTypeId : 1, 2, 3, which correspond to three different configurations for each
bandwidth. Three configurations allow to have different configurations in neighbouring cells in hexagonal
eNB layout. If user needs to have more different configuration for neighbouring cells, he/she need to use
manual configuration.
Example below show automatic FR algorithm configuration:
lteHelper->SetFfrAlgorithmType("ns3::LteFfrSoftAlgorithm");
lteHelper->SetFfrAlgorithmAttribute("FrCellTypeId", UintegerValue (1));
NetDeviceContainer enbDevs = lteHelper->InstallEnbDevice (enbNodes.Get(0));
Uplink Power Control
Uplink Power Control functionality is enabled by default. User can disable it by setting the boolean
attribute ns3::LteUePhy::EnableUplinkPowerControl to true.
User can switch between Open Loop Power Control and Closed Loop Power Control mechanisms by setting the
boolean attribute ns3::LteUePowerControl::ClosedLoop. By default Closed Loop Power Control with
Accumulation Mode is enabled.
Path-loss is key component of Uplink Power Control. It is computed as difference between filtered RSRP
and ReferenceSignalPower parameter. ReferenceSignalPower is sent with SIB2.
Attributes available in Uplink Power Control:
• ClosedLoop: if true Closed Loop Uplink Power Control mode is enabled and Open Loop Power Control
otherwise, default value is false
• AccumulationEnabled: if true Accumulation Mode is enabled and Absolute mode otherwise, default value
is false
• Alpha: the path loss compensation factor, default value is 1.0
• Pcmin: minimal UE TxPower, default value is -40 dBm
• Pcmax: maximal UE TxPower, default value is 23 dBm
• PoNominalPusch: this parameter should be set by higher layers, but currently it needs to be
configured by attribute system, possible values are integers in range (-126 … 24), Default value is
-80
• PoUePusch: this parameter should be set by higher layers, but currently it needs to be configured by
attribute system, possible values are integers in range (-8 … 7), Default value is 0
• PsrsOffset: this parameter should be set by higher layers, but currently it needs to be configured
by attribute system, possible values are integers in range (0 … 15), Default value is 7, what gives
P_Srs_Offset_Value = 0
Traced values in Uplink Power Control:
• ReportPuschTxPower: Current UE TxPower for PUSCH
• ReportPucchTxPower: Current UE TxPower for PUCCH
• ReportSrsTxPower: Current UE TxPower for SRS
Example configuration is presented below:
Config::SetDefault ("ns3::LteUePhy::EnableUplinkPowerControl", BooleanValue (true));
Config::SetDefault ("ns3::LteEnbPhy::TxPower", DoubleValue (30));
Config::SetDefault ("ns3::LteUePowerControl::ClosedLoop", BooleanValue (true));
Config::SetDefault ("ns3::LteUePowerControl::AccumulationEnabled", BooleanValue (true));
As an example, user can take a look and run the lena-uplink-power-control program.
Examples Programs
The directory src/lte/examples/ contains some example simulation programs that show how to simulate
different LTE scenarios.
Reference scenarios
There is a vast amount of reference LTE simulation scenarios which can be found in the literature. Here
we list some of them:
• The system simulation scenarios mentioned in section A.2 of [TR36814].
• The dual stripe model [R4-092042], which is partially implemented in the example program
src/lte/examples/lena-dual-stripe.cc. This example program features a lot of configurable parameters
which can be customized by changing the corresponding global variables. To get a list of all these
global variables, you can run this command:
./waf --run lena-dual-stripe --command-template="%s --PrintGlobals"
The following subsection presents an example of running a simulation campaign using this example
program.
Handover simulation campaign
In this subsection, we will demonstrate an example of running a simulation campaign using the LTE module
of ns-3. The objective of the campaign is to compare the effect of each built-in handover algorithm of
the LTE module.
The campaign will use the lena-dual-stripe example program. First, we have to modify the example program
to produce the output that we need. In this occasion, we want to produce the number of handovers, user
average throughput, and average SINR.
The number of handovers can be obtained by counting the number of times the HandoverEndOk Handover traces
is fired. Then the user average throughput can be obtained by enabling the RLC Simulation Output.
Finally, SINR can be obtained by enabling the PHY simulation output. The following sample code snippet
shows one possible way to obtain the above:
void
NotifyHandoverEndOkUe (std::string context, uint64_t imsi,
uint16_t cellId, uint16_t rnti)
{
std::cout << "Handover IMSI " << imsi << std::endl;
}
int
main (int argc, char *argv[])
{
/*** SNIP ***/
Config::Connect ("/NodeList/*/DeviceList/*/LteUeRrc/HandoverEndOk",
MakeCallback (&NotifyHandoverEndOkUe));
lteHelper->EnablePhyTraces ();
lteHelper->EnableRlcTraces ();
Ptr<RadioBearerStatsCalculator> rlcStats = lteHelper->GetRlcStats ();
rlcStats->SetAttribute ("StartTime", TimeValue (Seconds (0)));
rlcStats->SetAttribute ("EpochDuration", TimeValue (Seconds (simTime)));
Simulator::Run ();
Simulator::Destroy ();
return 0;
}
Then we have to configure the parameters of the program to suit our simulation needs. We are looking for
the following assumptions in our simulation:
• 7 sites of tri-sectored macro eNodeBs (i.e. 21 macrocells) deployed in hexagonal layout with 500 m
inter-site distance.
• Although lena-dual-stripe is originally intended for a two-tier (macrocell and femtocell)
simulation, we will simplify our simulation to one-tier (macrocell) simulation only.
• UEs are randomly distributed around the sites and attach to the network automatically using Idle
mode cell selection. After that, UE will roam the simulation environment with 60 kmph movement
speed.
• 50 seconds simulation duration, so UEs would have traveled far enough to trigger some handovers.
• 46 dBm macrocell Tx power and 10 dBm UE Tx power.
• EPC mode will be used because the X2 handover procedure requires it to be enabled.
• Full-buffer downlink and uplink traffic, both in 5 MHz bandwidth, using TCP protocol and
Proportional Fair scheduler.
• Ideal RRC protocol.
Table lena-dual-stripe parameter configuration for handover campaign below shows how we configure the
parameters of lena-dual-stripe to achieve the above assumptions.
lena-dual-stripe parameter configuration for handover campaign
┌───────────────────┬─────────┬──────────────────────────────┐
│Parameter name │ Value │ Description │
├───────────────────┼─────────┼──────────────────────────────┤
│simTime │ 50 │ 50 seconds simulation │
│ │ │ duration │
├───────────────────┼─────────┼──────────────────────────────┤
│nBlocks │ 0 │ Disabling apartment │
│ │ │ buildings and femtocells │
├───────────────────┼─────────┼──────────────────────────────┤
│nMacroEnbSites │ 7 │ Number of macrocell sites │
│ │ │ (each site has 3 cells) │
├───────────────────┼─────────┼──────────────────────────────┤
│nMacroEnbSitesX │ 2 │ The macrocell sites will be │
│ │ │ positioned in a 2-3-2 │
│ │ │ formation │
├───────────────────┼─────────┼──────────────────────────────┤
│interSiteDistance │ 500 │ 500 m distance between │
│ │ │ adjacent macrocell sites │
├───────────────────┼─────────┼──────────────────────────────┤
│macroEnbTxPowerDbm │ 46 │ 46 dBm Tx power for each │
│ │ │ macrocell │
├───────────────────┼─────────┼──────────────────────────────┤
│epc │ 1 │ Enable EPC mode │
├───────────────────┼─────────┼──────────────────────────────┤
│epcDl │ 1 │ Enable full-buffer DL │
│ │ │ traffic │
├───────────────────┼─────────┼──────────────────────────────┤
│epcUl │ 1 │ Enable full-buffer UL │
│ │ │ traffic │
├───────────────────┼─────────┼──────────────────────────────┤
│useUdp │ 0 │ Disable UDP traffic and │
│ │ │ enable TCP instead │
├───────────────────┼─────────┼──────────────────────────────┤
│macroUeDensity │ 0.00002 │ Determines number of UEs │
│ │ │ (translates to 48 UEs in our │
│ │ │ simulation) │
├───────────────────┼─────────┼──────────────────────────────┤
│outdoorUeMinSpeed │ 16.6667 │ Minimum UE movement speed in │
│ │ │ m/s (60 kmph) │
├───────────────────┼─────────┼──────────────────────────────┤
│outdoorUeMaxSpeed │ 16.6667 │ Maximum UE movement speed in │
│ │ │ m/s (60 kmph) │
├───────────────────┼─────────┼──────────────────────────────┤
│macroEnbBandwidth │ 25 │ 5 MHz DL and UL bandwidth │
├───────────────────┼─────────┼──────────────────────────────┤
│generateRem │ 1 │ (Optional) For plotting the │
│ │ │ Radio Environment Map │
└───────────────────┴─────────┴──────────────────────────────┘
Some of the required assumptions are not available as parameters of lena-dual-stripe. In this case, we
override the default attributes, as shown in Table Overriding default attributes for handover campaign
below.
Overriding default attributes for handover campaign
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Default value name Value Description
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
ns3::LteHelper::HandoverAlgorithm ns3::NoOpHandoverAlgorithm, Choice of handover algorithm
ns3::A3RsrpHandoverAlgorithm,
or
ns3::A2A4RsrqHandoverAlgorithm
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
ns3::LteHelper::Scheduler ns3::PfFfMacScheduler Proportional Fair scheduler
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
ns3::LteHelper::UseIdealRrc 1 Ideal RRC protocol
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
ns3::RadioBearerStatsCalculator::DlRlcOutputFilename <run>-DlRlcStats.txt File name for DL RLC trace
output
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
ns3::RadioBearerStatsCalculator::UlRlcOutputFilename <run>-UlRlcStats.txt File name for UL RLC trace
output
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
ns3::PhyStatsCalculator::DlRsrpSinrFilename <run>-DlRsrpSinrStats.txt File name for DL PHY
RSRP/SINR trace output
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
ns3::PhyStatsCalculator::UlSinrFilename <run>-UlSinrStats.txt File name for UL PHY SINR
trace output
┌─────────────────────────────────────────────────────┬────────────────────────────────┬──────────────────────────────┐
│ │ │ │
--
WI-FI MESH MODULE DOCUMENTATION
Design Documentation
Overview
The ns-3 mesh module extends the ns-3 wifi module to provide mesh networking capabilities according to
the IEEE 802.11s standard [ieee80211s].
The basic purpose of IEEE 802.11s is to define a mode of operation for Wi-Fi that permits frames to be
forwarded over multiple radio hops transparent to higher layer protocols such as IP. To accomplish this,
mesh-capable stations form a Mesh Basic Service Set (MBSS) by running a pair-wise peering protocol to
establish forwarding associations, and by running a routing protocol to find paths through the network.
A special gateway device called a mesh gate allows a MBSS to interconnect with a Distribution System
(DS).
The basic enhancements defined by IEEE 802.11s include:
• discovery services
• peering management
• security
• beaconing and synchronization
• the Mesh Coordination Function (MCF)
• power management
• channel switching
• extended frame formats
• path selection and forwarding
• interworking (proxy mesh gateways)
• intra-mesh congestion control, and
• emergency service support.
The ns-3 models implement only a subset of the above service extensions, focusing mainly on those items
related to peering and routing/forwarding of data frames through the mesh.
The Mesh NetDevice based on 802.11s D3.0 draft standard was added in ns-3.6 and includes the Mesh Peering
Management Protocol and HWMP (routing) protocol implementations. An overview presentation by Kirill
Andreev was published at the Workshop on ns-3 in 2009 [And09]. An overview paper is available at
[And10].
As of ns-3.23 release, the model has been updated to the 802.11s-2012 standard [ieee80211s] with regard
to packet formats, based on the contribution in [Hep15].
These changes include:
• Category codes and the categories compliant to IEEE-802.11-2012 Table 8-38—Category values.
• Information Elements (An adjustment of the element ID values was needed according to Table 8-54 of
IEEE-802.11-2012).
• Mesh Peering Management element format changed according to IEEE-802.11-2012 Figure 8-370.
• Mesh Configuration element format changed according to IEEE-802.11-2012 Figure 8-363.
• PERR element format changed according to IEEE-802.11-2012 Figure 8-394.
With these changes the messages of the Peering Management Protocol and Hybrid Wireless Mesh Protocol will
be transmitted compliant to IEEE802.11-2012 and the resulting pcap trace files can be analyzed by
Wireshark.
The multi-interface mesh points are supported as an extension of IEEE draft version 3.0. Note that
corresponding ns-3 mesh device helper creates a single interface station by default.
Overview of IEEE 802.11s
The implementation of the 802.11s extension consists of two main parts: the Peer Management Protocol
(PMP) and Hybrid Wireless Mesh Protocol (HWMP).
The tasks of the peer management protocol are the following:
• opening links, detecting beacons, and starting peer link finite state machine, and
• closing peer links due to transmission failures or beacon loss.
If a peer link between the sender and receiver does not exist, a frame will be dropped. So, the plug-in
to the peer management protocol (PMP) is the first in the list of ns3::MeshWifiInterfaceMacPlugins to be
used.
Peer management protocol
The peer management protocol consists of three main parts:
• the protocol itself, ns3::dot11s::PeerManagementProtocol, which keeps all active peer links on
interfaces, handles all changes of their states and notifies the routing protocol about link failures.
• the MAC plug-in, ns3::dot11s::PeerManagementProtocolMac, which drops frames if there is no peer link,
and peeks all needed information from management frames and information elements from beacons.
• the peer link, ns3::dot11s::PeerLink, which keeps finite state machine of each peer link, keeps beacon
loss counter and counter of successive transmission failures.
The procedure of closing a peer link is not described in detail in the standard, so in the model the link
may be closed by:
• beacon loss (see an appropriate attribute of ns3::dot11s::PeerLink class)
• transmission failure – when a predefined number of successive packets have failed to transmit, the link
will be closed.
The peer management protocol is also responsible for beacon collision avoidance, because it keeps beacon
timing elements from all neighbours. Note that the PeerManagementProtocol is not attached to the
MeshPointDevice as a routing protocol, but the structure is similar: the upper tier of the protocol is
ns3::dot11s::PeerManagementProtocol and its plug-in is ns3::dot11s::PeerManagementProtocolMac.
Hybrid Wireless Mesh Protocol
HWMP is implemented in both modes, reactive and proactive, although path maintenance is not implemented
(so active routes may time out and need to be rebuilt, causing packet loss). Also the model implements an
ability to transmit broadcast data and management frames as unicasts (see appropriate attributes). This
feature is disabled at a station when the number of neighbors of the station is more than a threshold
value.
Scope and Limitations
Supported features
• Peering Management Protocol (PMP), including link close heuristics and beacon collision avoidance.
• Hybrid Wireless Mesh Protocol (HWMP), including proactive and reactive modes, unicast/broadcast
propagation of management traffic, multi-radio extensions.
• 802.11e compatible airtime link metric.
Verification
• Comes with the custom Wireshark dissector.
• Linux kernel mac80211 layer compatible message formats.
Unsupported features
• Mesh Coordinated Channel Access (MCCA).
• Internetworking: mesh access point and mesh portal.
• Security.
• Power save.
• Path maintenance (sending PREQ proactively before a path expires)
• Though multi-radio operation is supported, no channel assignment protocol is proposed for now. (Correct
channel switching is not implemented)
Models yet to be created
• Mesh access point (QoS + non-QoS?)
• Mesh portal (QoS + non-QoS?)
Open issues
A bug exists in the Wi-Fi module that manifests itself as performance degradation in large mesh networks,
due to incorrect duplicate frame detection for QoS data frames (bug 2326).
Mesh does not work for 802.11n/ac stations (bug 2276).
Energy module can not be used on mesh devices (bug 2265).
IE11S_MESH_PEERING_PROTOCOL_VERSION should be removed as per standard. Protocol ID should actually be
part of the Mesh Peering Management IE (bug 2600).
Node packet processing times are not modeled; some evaluation of the impact of packet processing delays
is discussed in [Hep16].
User Documentation
Using the MeshNetDevice
Testing Documentation
References
[And09]
K. Andreev, Realization of IEEE802.11s draft standard in NS-3.
[And10]
K. Andreev and P. Boyko, IEEE 802.11s Mesh Networking NS-3 Model.
[Hep15]
C. Hepner and A. Witt and R. Muenzner, Validation of the ns-3 802.11s model and proposed changes
compliant to IEEE 802.11-2012, Poster at 2015 Workshop on ns-3, May 2015.
[Hep16]
C. Hepner and S. Moll and R. Muenzner, Influence of Processing Delays on the VoIP Performance for
IEEE 802.11s Multihop Wireless Mesh Networks: Comparison of ns-3 Network Simulations with Hardware
Measurements, Proceedings of SIMUTOOLS 16, August, 2016.
[ieee80211s]
IEEE Standard for Information Technology, Telecommunications and information exchange between
systems, Local and metropolitan area networks, Specific requirements, Part 11: Wireless LAN Medium
Access Control (MAC) and Physical Layer (PHY) specifications, Amendment 10: Mesh Networking, 10
September 2011.
MPI FOR DISTRIBUTED SIMULATION
Parallel and distributed discrete event simulation allows the execution of a single simulation program on
multiple processors. By splitting up the simulation into logical processes, LPs, each LP can be executed
by a different processor. This simulation methodology enables very large-scale simulations by leveraging
increased processing power and memory availability. In order to ensure proper execution of a distributed
simulation, message passing between LPs is required. To support distributed simulation in ns-3, the
standard Message Passing Interface (MPI) is used, along with a new distributed simulator class.
Currently, dividing a simulation for distributed purposes in ns-3 can only occur across point-to-point
links.
Current Implementation Details
During the course of a distributed simulation, many packets must cross simulator boundaries. In other
words, a packet that originated on one LP is destined for a different LP, and in order to make this
transition, a message containing the packet contents must be sent to the remote LP. Upon receiving this
message, the remote LP can rebuild the packet and proceed as normal. The process of sending an receiving
messages between LPs is handled easily by the new MPI interface in ns-3.
Along with simple message passing between LPs, a distributed simulator is used on each LP to determine
which events to process. It is important to process events in time-stamped order to ensure proper
simulation execution. If a LP receives a message containing an event from the past, clearly this is an
issue, since this event could change other events which have already been executed. To address this
problem, two conservative synchronization algorithm with lookahead are used in ns-3. For more information
on different synchronization approaches and parallel and distributed simulation in general, please refer
to “Parallel and Distributed Simulation Systems” by Richard Fujimoto.
The default parallel synchronization strategy implemented in the DistributedSimulatorImpl class is based
on a globally synchronized algorithm using an MPI collective operation to synchronize simulation time
across all LPs. A second synchronization strategy based on local communication and null messages is
implemented in the NullMessageSimulatorImpl class, For the null message strategy the global all to all
gather is not required; LPs only need to communication with LPs that have shared point-to-point links.
The algorithm to use is controlled by which the ns-3 global value SimulatorImplementationType.
The best algorithm to use is dependent on the communication and event scheduling pattern for the
application. In general, null message synchronization algorithms will scale better due to local
communication scaling better than a global all-to-all gather that is required by
DistributedSimulatorImpl. There are two known cases where the global synchronization performs better.
The first is when most LPs have point-to-point link with most other LPs, in other words the LPs are
nearly fully connected. In this case the null message algorithm will generate more message passing
traffic than the all-to-all gather. A second case where the global all-to-all gather is more efficient
is when there are long periods of simulation time when no events are occurring. The all-to-all gather
algorithm is able to quickly determine then next event time globally. The nearest neighbor behavior of
the null message algorithm will require more communications to propagate that knowledge; each LP is only
aware of neighbor next event times.
Remote point-to-point links
As described in the introduction, dividing a simulation for distributed purposes in ns-3 currently can
only occur across point-to-point links; therefore, the idea of remote point-to-point links is very
important for distributed simulation in ns-3. When a point-to-point link is installed, connecting two
nodes, the point-to-point helper checks the system id, or rank, of both nodes. The rank should be
assigned during node creation for distributed simulation and is intended to signify on which LP a node
belongs. If the two nodes are on the same rank, a regular point-to-point link is created. If, however,
the two nodes are on different ranks, then these nodes are intended for different LPs, and a remote
point-to-point link is used. If a packet is to be sent across a remote point-to-point link, MPI is used
to send the message to the remote LP.
Distributing the topology
Currently, the full topology is created on each rank, regardless of the individual node system ids. Only
the applications are specific to a rank. For example, consider node 1 on LP 1 and node 2 on LP 2, with a
traffic generator on node 1. Both node 1 and node 2 will be created on both LP1 and LP2; however, the
traffic generator will only be installed on LP1. While this is not optimal for memory efficiency, it does
simplify routing, since all current routing implementations in ns-3 will work with distributed
simulation.
Running Distributed Simulations
Prerequisites
Ensure that MPI is installed, as well as mpic++. In Ubuntu repositories, these are openmpi-bin,
openmpi-common, openmpi-doc, libopenmpi-dev. In Fedora, these are openmpi and openmpi-devel.
Note:
There is a conflict on some Fedora systems between libotf and openmpi. A possible “quick-fix” is to yum
remove libotf before installing openmpi. This will remove conflict, but it will also remove emacs.
Alternatively, these steps could be followed to resolve the conflict:
1. Rename the tiny otfdump which emacs says it needs:
$ mv /usr/bin/otfdump /usr/bin/otfdump.emacs-version
2. Manually resolve openmpi dependencies:
$ sudo yum install libgfortran libtorque numactl
3. Download rpm packages:
openmpi-1.3.1-1.fc11.i586.rpm
openmpi-devel-1.3.1-1.fc11.i586.rpm
openmpi-libs-1.3.1-1.fc11.i586.rpm
openmpi-vt-1.3.1-1.fc11.i586.rpm
from http://mirrors.kernel.org/fedora/releases/11/Everything/i386/os/Packages/
4. Force the packages in:
$ sudo rpm -ivh --force \
openmpi-1.3.1-1.fc11.i586.rpm \
openmpi-libs-1.3.1-1.fc11.i586.rpm \
openmpi-devel-1.3.1-1.fc11.i586.rpm \
openmpi-vt-1.3.1-1.fc11.i586.rpm
Also, it may be necessary to add the openmpi bin directory to PATH in order to execute mpic++ and mpirun
from the command line. Alternatively, the full path to these executables can be used. Finally, if openmpi
complains about the inability to open shared libraries, such as libmpi_cxx.so.0, it may be necessary to
add the openmpi lib directory to LD_LIBRARY_PATH.
Here is an example of setting up PATH and LD_LIBRARY_PATH using a bash shell:
• For a 32-bit Linux distribution:
$ export PATH=$PATH:/usr/lib/openmpi/bin
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/openmpi/lib
For a 64-bit Linux distribution:
$ export PATH=$PATH:/usr/lib64/openmpi/bin
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib64/openmpi/lib
These lines can be added into ~/.bash_profile or ~/.bashrc to avoid having to retype them when a new
shell is opened.
Note 2: There is a separate issue on recent Fedora distributions, which is that the libraries are built
with AVX instructions. On older machines or some virtual machines, this results in an illegal
instruction being thrown. This is not an ns-3 issue, a simple MPI test case will also fail. The AVX
instructions are being called during initialization.
The symptom of this is that attempts to run an ns-3 MPI program will fail with the error: terminated with
signal SIGILL. To check if this is the problem, run:
$ grep avx /proc/cpuinfo
and it will not return anything if AVX is not present.
If AVX is not supported, it is recommended to switch to a different MPI implementation such as MPICH:
$ dnf remove openmpi openmpi-devel
$ dnf install mpich mpich-devel environment-modules
$ module load mpi/mpich-x86_64
Building and Running Examples
If you already built ns-3 without MPI enabled, you must re-build:
$ ./waf distclean
Configure ns-3 with the –enable-mpi option:
$ ./waf -d debug configure --enable-examples --enable-tests --enable-mpi
Ensure that MPI is enabled by checking the optional features shown from the output of configure.
Next, build ns-3:
$ ./waf
After building ns-3 with mpi enabled, the example programs are now ready to run with mpiexec. It is
advised to avoid running Waf directly with mpiexec; two options that should be more robust are to either
use the –command-template way of running the mpiexec program, or to use ./waf shell and run the
executables directly on the command line. Here are a few examples (from the root ns-3 directory):
$ ./waf --command-template="mpiexec -np 2 %s" --run simple-distributed
$ ./waf --command-template="mpiexec -np 2 -machinefile mpihosts %s --nix=0" --run nms-p2p-nix-distributed
An example using the null message synchronization algorithm:
$ ./waf --command-template="mpiexec -np 2 %s --nullmsg" --run simple-distributed
The np switch is the number of logical processors to use. The machinefile switch is which machines to
use. In order to use machinefile, the target file must exist (in this case mpihosts). This can simply
contain something like:
localhost
localhost
localhost
...
Or if you have a cluster of machines, you can name them.
The other alternative to command-template is to use ./waf shell. Here are the equivalent examples to the
above (assuming optimized build profile):
$ ./waf shell
$ cd build/src/mpi/examples
$ mpiexec -np 2 ns3-dev-simple-distributed-optimized
$ mpiexec -np 2 -machinefile mpihosts ns3-dev-nms-p2p-nix-distributed-optimized --nix=0
$ mpiexec -np 2 ns3-dev-simple-distributed-optimized --nullmsg
Setting synchronization algorithm to use
The global value SimulatorImplementationType is used to set the synchronization algorithm to use. This
value must be set before the MpiInterface::Enable method is invoked if the default
DistributedSimulatorImpl is not used. Here is an example code snippet showing how to add a command line
argument to control the synchronization algorithm choice::
cmd.AddValue ("nullmsg", "Enable the use of null-message synchronization", nullmsg);
if(nullmsg)
{
GlobalValue::Bind ("SimulatorImplementationType",
StringValue ("ns3::NullMessageSimulatorImpl"));
}
else
{
GlobalValue::Bind ("SimulatorImplementationType",
StringValue ("ns3::DistributedSimulatorImpl"));
}
// Enable parallel simulator with the command line arguments
MpiInterface::Enable (&argc, &argv);
Creating custom topologies
The example programs in src/mpi/examples give a good idea of how to create different topologies for
distributed simulation. The main points are assigning system ids to individual nodes, creating
point-to-point links where the simulation should be divided, and installing applications only on the LP
associated with the target node.
Assigning system ids to nodes is simple and can be handled two different ways. First, a NodeContainer
can be used to create the nodes and assign system ids:
NodeContainer nodes;
nodes.Create (5, 1); // Creates 5 nodes with system id 1.
Alternatively, nodes can be created individually, assigned system ids, and added to a NodeContainer. This
is useful if a NodeContainer holds nodes with different system ids:
NodeContainer nodes;
Ptr<Node> node1 = CreateObject<Node> (0); // Create node1 with system id 0
Ptr<Node> node2 = CreateObject<Node> (1); // Create node2 with system id 1
nodes.Add (node1);
nodes.Add (node2);
Next, where the simulation is divided is determined by the placement of point-to-point links. If a
point-to-point link is created between two nodes with different system ids, a remote point-to-point link
is created, as described in Current Implementation Details.
Finally, installing applications only on the LP associated with the target node is very important. For
example, if a traffic generator is to be placed on node 0, which is on LP0, only LP0 should install this
application. This is easily accomplished by first checking the simulator system id, and ensuring that it
matches the system id of the target node before installing the application.
Tracing During Distributed Simulations
Depending on the system id (rank) of the simulator, the information traced will be different, since
traffic originating on one simulator is not seen by another simulator until it reaches nodes specific to
that simulator. The easiest way to keep track of different traces is to just name the trace files or
pcaps differently, based on the system id of the simulator. For example, something like this should work
well, assuming all of these local variables were previously defined:
if (MpiInterface::GetSystemId () == 0)
{
pointToPoint.EnablePcapAll ("distributed-rank0");
phy.EnablePcap ("distributed-rank0", apDevices.Get (0));
csma.EnablePcap ("distributed-rank0", csmaDevices.Get (0), true);
}
else if (MpiInterface::GetSystemId () == 1)
{
pointToPoint.EnablePcapAll ("distributed-rank1");
phy.EnablePcap ("distributed-rank1", apDevices.Get (0));
csma.EnablePcap ("distributed-rank1", csmaDevices.Get (0), true);
}
MOBILITY
The mobility support in ns-3 includes:
• a set of mobility models which are used to track and maintain the current cartesian position and speed
of an object.
• a “course change notifier” trace source which can be used to register listeners to the course changes
of a mobility model
• a number of helper classes which are used to place nodes and setup mobility models (including parsers
for some mobility definition formats).
Model Description
The source code for mobility lives in the directory src/mobility.
Design
The design includes mobility models, position allocators, and helper functions.
In ns-3, MobilityModel` objects track the evolution of position with respect to a (cartesian) coordinate
system. The mobility model is typically aggregated to an ns3::Node object and queried using
GetObject<MobilityModel> (). The base class ns3::MobilityModel is subclassed for different motion
behaviors.
The initial position of objects is typically set with a PositionAllocator. These types of objects will
lay out the position on a notional canvas. Once the simulation starts, the position allocator may no
longer be used, or it may be used to pick future mobility “waypoints” for such mobility models.
Most users interact with the mobility system using mobility helper classes. The MobilityHelper combines
a mobility model and position allocator, and can be used with a node container to install a similar
mobility capability on a set of nodes.
We first describe the coordinate system and issues surrounding multiple coordinate systems.
Coordinate system
There are many possible coordinate systems and possible translations between them. ns-3 uses the
Cartesian coordinate system only, at present.
The question has arisen as to how to use the mobility models (supporting Cartesian coordinates) with
different coordinate systems. This is possible if the user performs conversion between the ns-3
Cartesian and the other coordinate system. One possible library to assist is the proj4 library for
projections and reverse projections.
If we support converting between coordinate systems, we must adopt a reference. It has been suggested to
use the geocentric Cartesian coordinate system as a reference. Contributions are welcome in this regard.
The question has arisen about adding a new mobility model whose motion is natively implemented in a
different coordinate system (such as an orbital mobility model implemented using spherical coordinate
system). We advise to create a subclass with the APIs desired (such as Get/SetSphericalPosition), and
new position allocators, and implement the motion however desired, but must also support the conversion
to cartesian (by supporting the cartesian Get/SetPosition).
Coordinates
The base class for a coordinate is called ns3::Vector. While positions are normally described as
coordinates and not vectors in the literature, it is possible to reuse the same data structure to
represent position (x,y,z) and velocity (magnitude and direction from the current position). ns-3 uses
class Vector for both.
There are also some additional related structures used to support mobility models.
• Rectangle
• Box
• Waypoint
MobilityModel
Describe base class
• GetPosition ()
• Position and Velocity attributes
• GetDistanceFrom ()
• CourseChangeNotification
MobilityModel Subclasses
• ConstantPosition
• ConstantVelocity
• ConstantAcceleration
• GaussMarkov
• Hierarchical
• RandomDirection2D
• RandomWalk2D
• RandomWaypoint
• SteadyStateRandomWaypoint
• Waypoint
PositionAllocator
Position allocators usually used only at beginning, to lay out the nodes initial position. However, some
mobility models (e.g. RandomWaypoint) will use a position allocator to pick new waypoints.
• ListPositionAllocator
• GridPositionAllocator
• RandomRectanglePositionAllocator
• RandomBoxPositionAllocator
• RandomDiscPositionAllocator
• UniformDiscPositionAllocator
Helper
A special mobility helper is provided that is mainly aimed at supporting the installation of mobility to
a Node container (when using containers at the helper API level). The MobilityHelper class encapsulates
a MobilityModel factory object and a PositionAllocator used for initial node layout.
Group mobility is also configurable via a GroupMobilityHelper object. Group mobility reuses the
HierarchicalMobilityModel allowing one to define a reference (parent) mobility model and child (member)
mobility models, with the position being the vector sum of the two mobility model positions (i.e., the
child position is defined as an offset to the parent position). In the GroupMobilityHelper, the parent
mobility model is not associated with any node, and is used as the parent mobility model for all
(distinct) child mobility models. The reference point group mobility model [Camp2002] is the basis for
this ns-3 model.
ns-2 MobilityHelper
The ns-2 mobility format is a widely used mobility trace format. The documentation is available at:
http://www.isi.edu/nsnam/ns/doc/node172.html
Valid trace files use the following ns-2 statements:
$node set X_ x1
$node set Y_ y1
$node set Z_ z1
$ns at $time $node setdest x2 y2 speed
$ns at $time $node set X_ x1
$ns at $time $node set Y_ Y1
$ns at $time $node set Z_ Z1
In the above, the initial positions are set using the set statements. Also, this set can be specified
for a future time, such as in the last three statements above.
The command setdest instructs the simulation to start moving the specified node towards the coordinate
(x2, y2) at the specified time. Note that the node may never get to the destination, but will proceed
towards the destination at the specified speed until it either reaches the destination (where it will
pause), is set to a new position (via set), or sent on another course change (via setdest).
Note that in ns-3, movement along the Z dimension is not supported.
Some examples of external tools that can export in this format include:
• BonnMotion
• Installation instructions and
• Documentation for using BonnMotion with ns-3
• SUMO
• TraNS
• ns-2 setdest utility
A special Ns2MobilityHelper object can be used to parse these files and convert the statements into ns-3
mobility events. The underlying ConstantVelocityMobilityModel is used to model these movements.
See below for additional usage instructions on this helper.
Scope and Limitations
• only cartesian coordinates are presently supported
References
[Camp2002]
T. Camp, J. Boleng, V. Davies. “A survey of mobility models for ad hoc network research”, in
Wireless Communications and Mobile Computing, 2002: vol. 2, pp. 2483-2502.
Usage
Most ns-3 program authors typically interact with the mobility system only at configuration time.
However, various ns-3 objects interact with mobility objects repeatedly during runtime, such as a
propagation model trying to determine the path loss between two mobile nodes.
Helper
A typical usage pattern can be found in the third.cc program in the tutorial.
First, the user instantiates a MobilityHelper object and sets some Attributes controlling the “position
allocator” functionality.
MobilityHelper mobility;
mobility.SetPositionAllocator ("ns3::GridPositionAllocator",
"MinX", DoubleValue (0.0),
"MinY", DoubleValue (0.0),
"DeltaX", DoubleValue (5.0),
"DeltaY", DoubleValue (10.0),
"GridWidth", UintegerValue (3),
"LayoutType", StringValue ("RowFirst"));
This code tells the mobility helper to use a two-dimensional grid to initially place the nodes. The
first argument is an ns-3 TypeId specifying the type of mobility model; the remaining attribute/value
pairs configure this position allocator.
Next, the user typically sets the MobilityModel subclass; e.g.:
mobility.SetMobilityModel ("ns3::RandomWalk2dMobilityModel",
"Bounds", RectangleValue (Rectangle (-50, 50, -50, 50)));
Once the helper is configured, it is typically passed a container, such as:
mobility.Install (wifiStaNodes);
A MobilityHelper object may be reconfigured and reused for different NodeContainers during the
configuration of an ns-3 scenario.
Ns2MobilityHelper
Two example programs are provided demonstrating the use of the ns-2 mobility helper:
• ns2-mobility-trace.cc
• bonnmotion-ns2-example.cc
ns2-mobility-trace
The ns2-mobility-trace.cc program is an example of loading an ns-2 trace file that specifies the
movements of two nodes over 100 seconds of simulation time. It is paired with the file
default.ns_movements.
The program behaves as follows:
• a Ns2MobilityHelper object is created, with the specified trace file.
• A log file is created, using the log file name argument.
• A node container is created with the number of nodes specified in the command line. For this
particular trace file, specify the value 2 for this argument.
• the Install() method of Ns2MobilityHelper to set mobility to nodes. At this moment, the file is read
line by line, and the movement is scheduled in the simulator.
• A callback is configured, so each time a node changes its course a log message is printed.
The example prints out messages generated by each read line from the ns2 movement trace file. For each
line, it shows if the line is correct, or of it has errors and in this case it will be ignored.
Example usage:
$ ./waf --run "ns2-mobility-trace \
--traceFile=src/mobility/examples/default.ns_movements \
--nodeNum=2 \
--duration=100.0 \
--logFile=ns2-mob.log"
Sample log file output:
+0.0ns POS: x=150, y=93.986, z=0; VEL:0, y=50.4038, z=0
+0.0ns POS: x=195.418, y=150, z=0; VEL:50.1186, y=0, z=0
+104727357.0ns POS: x=200.667, y=150, z=0; VEL:50.1239, y=0, z=0
+204480076.0ns POS: x=205.667, y=150, z=0; VEL:0, y=0, z=0
bonnmotion-ns2-example
The bonnmotion-ns2-example.cc program, which models the movement of a single mobile node for 1000 seconds
of simulation time, has a few associated files:
• bonnmotion.ns_movements is the ns-2-formatted mobility trace
• bonnmotion.params is a BonnMotion-generated file with some metadata about the mobility trace
• bonnmotion.ns_params is another BonnMotion-generated file with ns-2-related metadata.
Neither of the latter two files is used by ns-3, although they are generated as part of the BonnMotion
process to output ns-2-compatible traces.
The program bonnmotion-ns2-example.cc will output the following to stdout:
At 0.00 node 0: Position(329.82, 66.06, 0.00); Speed(0.53, -0.22, 0.00)
At 100.00 node 0: Position(378.38, 45.59, 0.00); Speed(0.00, 0.00, 0.00)
At 200.00 node 0: Position(304.52, 123.66, 0.00); Speed(-0.92, 0.97, 0.00)
At 300.00 node 0: Position(274.16, 131.67, 0.00); Speed(-0.53, -0.46, 0.00)
At 400.00 node 0: Position(202.11, 123.60, 0.00); Speed(-0.98, 0.35, 0.00)
At 500.00 node 0: Position(104.60, 158.95, 0.00); Speed(-0.98, 0.35, 0.00)
At 600.00 node 0: Position(31.92, 183.87, 0.00); Speed(0.76, -0.51, 0.00)
At 700.00 node 0: Position(107.99, 132.43, 0.00); Speed(0.76, -0.51, 0.00)
At 800.00 node 0: Position(184.06, 80.98, 0.00); Speed(0.76, -0.51, 0.00)
At 900.00 node 0: Position(250.08, 41.76, 0.00); Speed(0.60, -0.05, 0.00)
The motion of the mobile node is sampled every 100 seconds, and its position and speed are printed out.
This output may be compared to the output of a similar ns-2 program (found in the ns-2 tcl/ex/ directory
of ns-2) running from the same mobility trace.
The next file is generated from ns-2 (users will have to download and install ns-2 and run this Tcl
program to see this output). The output of the ns-2 bonnmotion-example.tcl program is shown below for
comparison (file bonnmotion-example.tr):
M 0.00000 0 (329.82, 66.06, 0.00), (378.38, 45.59), 0.57
M 100.00000 0 (378.38, 45.59, 0.00), (378.38, 45.59), 0.57
M 119.37150 0 (378.38, 45.59, 0.00), (286.69, 142.52), 1.33
M 200.00000 0 (304.52, 123.66, 0.00), (286.69, 142.52), 1.33
M 276.35353 0 (286.69, 142.52, 0.00), (246.32, 107.57), 0.70
M 300.00000 0 (274.16, 131.67, 0.00), (246.32, 107.57), 0.70
M 354.65589 0 (246.32, 107.57, 0.00), (27.38, 186.94), 1.04
M 400.00000 0 (202.11, 123.60, 0.00), (27.38, 186.94), 1.04
M 500.00000 0 (104.60, 158.95, 0.00), (27.38, 186.94), 1.04
M 594.03719 0 (27.38, 186.94, 0.00), (241.02, 42.45), 0.92
M 600.00000 0 (31.92, 183.87, 0.00), (241.02, 42.45), 0.92
M 700.00000 0 (107.99, 132.43, 0.00), (241.02, 42.45), 0.92
M 800.00000 0 (184.06, 80.98, 0.00), (241.02, 42.45), 0.92
M 884.77399 0 (241.02, 42.45, 0.00), (309.59, 37.22), 0.60
M 900.00000 0 (250.08, 41.76, 0.00), (309.59, 37.22), 0.60
The output formatting is slightly different, and the course change times are additionally plotted, but it
can be seen that the position vectors are the same between the two traces at intervals of 100 seconds.
The mobility computations performed on the ns-2 trace file are slightly different in ns-2 and ns-3, and
floating-point arithmetic is used, so there is a chance that the position in ns-2 may be slightly
different than the respective position when using the trace file in ns-3.
Use of Random Variables
A typical use case is to evaluate protocols on a mobile topology that involves some randomness in the
motion or initial position allocation. To obtain random motion and positioning that is not affected by
the configuration of the rest of the scenario, it is recommended to use the “AssignStreams” facility of
the random number system.
Class MobilityModel and class PositionAllocator both have public API to assign streams to underlying
random variables:
/**
* Assign a fixed random variable stream number to the random variables
* used by this model. Return the number of streams (possibly zero) that
* have been assigned.
*
* \param stream first stream index to use
* \return the number of stream indices assigned by this model
*/
int64_t AssignStreams (int64_t stream);
The class MobilityHelper also provides this API. The typical usage pattern when using the helper is:
int64_t streamIndex = /*some positive integer */
MobilityHelper mobility;
... (configure mobility)
mobility.Install (wifiStaNodes);
int64_t streamsUsed = mobility.AssignStreams (wifiStaNodes, streamIndex);
If AssignStreams is called before Install, it will not have any effect.
Advanced Usage
A number of external tools can be used to generate traces read by the Ns2MobilityHelper.
ns-2 scengen
TBD
BonnMotion
http://net.cs.uni-bonn.de/wg/cs/applications/bonnmotion/
SUMO
http://sourceforge.net/apps/mediawiki/sumo/index.php?title=Main_Page
TraNS
http://trans.epfl.ch/
Examples
• main-random-topology.cc
• main-random-walk.cc
• main-grid-topology.cc
• ns2-mobility-trace.cc
• ns2-bonnmotion.cc
reference-point-group-mobility-example.cc
The reference point group mobility model ([Camp2002]) is demonstrated in the example program
reference-point-group-mobility-example.cc. This example runs a short simulation that illustrates a
parent WaypointMobilityModel traversing a rectangular course within a bounding box, and three member
nodes independently execute a two-dimensional random walk around the parent position, within a small
bounding box. The example illustrates configuration using the GroupMobilityHelper and manual
configuration without a helper; the configuration option is selectable by command-line argument.
The example outputs two mobility trace files, a course change trace and a time-series trace of node
position. The latter trace file can be parsed by a Bash script
(reference-point-group-mobility-animate.sh) to create PNG images at one-second intervals, which can then
be combined using an image processing program such as ImageMagick to form a basic animated gif of the
mobility. The example and animation program files have further instructions on how to run them.
Validation
TBD
NETWORK MODULE
Packets
The design of the Packet framework of ns was heavily guided by a few important use-cases:
• avoid changing the core of the simulator to introduce new types of packet headers or trailers
• maximize the ease of integration with real-world code and systems
• make it easy to support fragmentation, defragmentation, and, concatenation which are important,
especially in wireless systems.
• make memory management of this object efficient
• allow actual application data or dummy application bytes for emulated applications
Each network packet contains a byte buffer, a set of byte tags, a set of packet tags, and metadata.
The byte buffer stores the serialized content of the headers and trailers added to a packet. The
serialized representation of these headers is expected to match that of real network packets bit for bit
(although nothing forces you to do this) which means that the content of a packet buffer is expected to
be that of a real packet.
Fragmentation and defragmentation are quite natural to implement within this context: since we have a
buffer of real bytes, we can split it in multiple fragments and re-assemble these fragments. We expect
that this choice will make it really easy to wrap our Packet data structure within Linux-style skb or
BSD-style mbuf to integrate real-world kernel code in the simulator. We also expect that performing a
real-time plug of the simulator to a real-world network will be easy.
One problem that this design choice raises is that it is difficult to pretty-print the packet headers
without context. The packet metadata describes the type of the headers and trailers which were serialized
in the byte buffer. The maintenance of metadata is optional and disabled by default. To enable it, you
must call Packet::EnablePrinting() and this will allow you to get non-empty output from Packet::Print and
Packet::Print.
Also, developers often want to store data in packet objects that is not found in the real packets (such
as timestamps or flow-ids). The Packet class deals with this requirement by storing a set of tags (class
Tag). We have found two classes of use cases for these tags, which leads to two different types of tags.
So-called ‘byte’ tags are used to tag a subset of the bytes in the packet byte buffer while ‘packet’ tags
are used to tag the packet itself. The main difference between these two kinds of tags is what happens
when packets are copied, fragmented, and reassembled: ‘byte’ tags follow bytes while ‘packet’ tags follow
packets. Another important difference between these two kinds of tags is that byte tags cannot be removed
and are expected to be written once, and read many times, while packet tags are expected to be written
once, read many times, and removed exactly once. An example of a ‘byte’ tag is a FlowIdTag which contains
a flow id and is set by the application generating traffic. An example of a ‘packet’ tag is a cross-layer
QoS class id set by an application and processed by a lower-level MAC layer.
Memory management of Packet objects is entirely automatic and extremely efficient: memory for the
application-level payload can be modeled by a virtual buffer of zero-filled bytes for which memory is
never allocated unless explicitly requested by the user or unless the packet is fragmented or serialized
out to a real network device. Furthermore, copying, adding, and, removing headers or trailers to a packet
has been optimized to be virtually free through a technique known as Copy On Write.
Packets (messages) are fundamental objects in the simulator and their design is important from a
performance and resource management perspective. There are various ways to design the simulation packet,
and tradeoffs among the different approaches. In particular, there is a tension between ease-of-use,
performance, and safe interface design.
Packet design overview
Unlike ns-2, in which Packet objects contain a buffer of C++ structures corresponding to protocol
headers, each network packet in ns-3 contains a byte Buffer, a list of byte Tags, a list of packet Tags,
and a PacketMetadata object:
• The byte buffer stores the serialized content of the chunks added to a packet. The serialized
representation of these chunks is expected to match that of real network packets bit for bit (although
nothing forces you to do this) which means that the content of a packet buffer is expected to be that
of a real packet. Packets can also be created with an arbitrary zero-filled payload for which no real
memory is allocated.
• Each list of tags stores an arbitrarily large set of arbitrary user-provided data structures in the
packet. Each Tag is uniquely identified by its type; only one instance of each type of data structure
is allowed in a list of tags. These tags typically contain per-packet cross-layer information or flow
identifiers (i.e., things that you wouldn’t find in the bits on the wire).
[image] Implementation overview of Packet class..UNINDENT
Figure Implementation overview of Packet class. is a high-level overview of the Packet implementation;
more detail on the byte Buffer implementation is provided later in Figure Implementation overview of a
packet’s byte Buffer.. In ns-3, the Packet byte buffer is analogous to a Linux skbuff or BSD mbuf; it
is a serialized representation of the actual data in the packet. The tag lists are containers for
extra items useful for simulation convenience; if a Packet is converted to an emulated packet and put
over an actual network, the tags are stripped off and the byte buffer is copied directly into a real
packet.
Packets are reference counted objects. They are handled with smart pointer (Ptr) objects like many of
the objects in the ns-3 system. One small difference you will see is that class Packet does not
inherit from class Object or class RefCountBase, and implements the Ref() and Unref() methods directly.
This was designed to avoid the overhead of a vtable in class Packet.
The Packet class is designed to be copied cheaply; the overall design is based on Copy on Write (COW).
When there are multiple references to a packet object, and there is an operation on one of them, only
so-called “dirty” operations will trigger a deep copy of the packet:
• ns3::Packet::AddHeader()
• ns3::Packet::AddTrailer()
• both versions of ns3::Packet::AddAtEnd()
• Packet::RemovePacketTag()
The fundamental classes for adding to and removing from the byte buffer are class Header and class
Trailer. Headers are more common but the below discussion also largely applies to protocols using
trailers. Every protocol header that needs to be inserted and removed from a Packet instance should
derive from the abstract Header base class and implement the private pure virtual methods listed below:
• ns3::Header::SerializeTo()
• ns3::Header::DeserializeFrom()
• ns3::Header::GetSerializedSize()
• ns3::Header::PrintTo()
Basically, the first three functions are used to serialize and deserialize protocol control information
to/from a Buffer. For example, one may define class TCPHeader : public Header. The TCPHeader object will
typically consist of some private data (like a sequence number) and public interface access functions
(such as checking the bounds of an input). But the underlying representation of the TCPHeader in a Packet
Buffer is 20 serialized bytes (plus TCP options). The TCPHeader::SerializeTo() function would therefore
be designed to write these 20 bytes properly into the packet, in network byte order. The last function is
used to define how the Header object prints itself onto an output stream.
Similarly, user-defined Tags can be appended to the packet. Unlike Headers, Tags are not serialized into
a contiguous buffer but are stored in lists. Tags can be flexibly defined to be any type, but there can
only be one instance of any particular object type in the Tags buffer at any time.
Using the packet interface
This section describes how to create and use the ns3::Packet object.
Creating a new packet
The following command will create a new packet with a new unique Id.:
Ptr<Packet> pkt = Create<Packet> ();
What is the Uid (unique Id)? It is an internal id that the system uses to identify packets. It can be
fetched via the following method:
uint32_t uid = pkt->GetUid ();
But please note the following. This uid is an internal uid and cannot be counted on to provide an
accurate counter of how many “simulated packets” of a particular protocol are in the system. It is not
trivial to make this uid into such a counter, because of questions such as what should the uid be when
the packet is sent over broadcast media, or when fragmentation occurs. If a user wants to trace actual
packet counts, he or she should look at e.g. the IP ID field or transport sequence numbers, or other
packet or frame counters at other protocol layers.
We mentioned above that it is possible to create packets with zero-filled payloads that do not actually
require a memory allocation (i.e., the packet may behave, when delays such as serialization or
transmission delays are computed, to have a certain number of payload bytes, but the bytes will only be
allocated on-demand when needed). The command to do this is, when the packet is created:
Ptr<Packet> pkt = Create<Packet> (N);
where N is a positive integer.
The packet now has a size of N bytes, which can be verified by the GetSize() method:
/**
* \returns the size in bytes of the packet (including the zero-filled
* initial payload)
*/
uint32_t GetSize (void) const;
You can also initialize a packet with a character buffer. The input data is copied and the input buffer
is untouched. The constructor applied is:
Packet (uint8_t const *buffer, uint32_t size);
Here is an example:
Ptr<Packet> pkt1 = Create<Packet> (reinterpret_cast<const uint8_t*> ("hello"), 5);
Packets are freed when there are no more references to them, as with all ns-3 objects referenced by the
Ptr class.
Adding and removing Buffer data
After the initial packet creation (which may possibly create some fake initial bytes of payload), all
subsequent buffer data is added by adding objects of class Header or class Trailer. Note that, even if
you are in the application layer, handling packets, and want to write application data, you write it as
an ns3::Header or ns3::Trailer. If you add a Header, it is prepended to the packet, and if you add a
Trailer, it is added to the end of the packet. If you have no data in the packet, then it makes no
difference whether you add a Header or Trailer. Since the APIs and classes for header and trailer are
pretty much identical, we’ll just look at class Header here.
The first step is to create a new header class. All new Header classes must inherit from class Header,
and implement the following methods:
• Serialize ()
• Deserialize ()
• GetSerializedSize ()
• Print ()
To see a simple example of how these are done, look at the UdpHeader class headers
src/internet/model/udp-header.cc. There are many other examples within the source code.
Once you have a header (or you have a preexisting header), the following Packet API can be used to add or
remove such headers.:
/**
* Add header to this packet. This method invokes the
* Header::GetSerializedSize and Header::Serialize
* methods to reserve space in the buffer and request the
* header to serialize itself in the packet buffer.
*
* \param header a reference to the header to add to this packet.
*/
void AddHeader (const Header & header);
/**
* Deserialize and remove the header from the internal buffer.
*
* This method invokes Header::Deserialize (begin) and should be used for
* fixed-length headers.
*
* \param header a reference to the header to remove from the internal buffer.
* \returns the number of bytes removed from the packet.
*/
uint32_t RemoveHeader (Header &header);
/**
* Deserialize but does _not_ remove the header from the internal buffer.
* This method invokes Header::Deserialize.
*
* \param header a reference to the header to read from the internal buffer.
* \returns the number of bytes read from the packet.
*/
uint32_t PeekHeader (Header &header) const;
For instance, here are the typical operations to add and remove a UDP header.:
// add header
Ptr<Packet> packet = Create<Packet> ();
UdpHeader udpHeader;
// Fill out udpHeader fields appropriately
packet->AddHeader (udpHeader);
...
// remove header
UdpHeader udpHeader;
packet->RemoveHeader (udpHeader);
// Read udpHeader fields as needed
If the header is variable-length, then another variant of RemoveHeader() is needed:
/**
* \brief Deserialize and remove the header from the internal buffer.
*
* This method invokes Header::Deserialize (begin, end) and should be
* used for variable-length headers (where the size is determined somehow
* by the caller).
*
* \param header a reference to the header to remove from the internal buffer.
* \param size number of bytes to deserialize
* \returns the number of bytes removed from the packet.
*/
uint32_t RemoveHeader (Header &header, uint32_t size);
In this case, the caller must figure out and provide the right ‘size’ as an argument (the Deserialization
routine may not know when to stop). An example of this type of header would be a series of
Type-Length-Value (TLV) information elements, where the ending point of the series of TLVs can be deduced
from the packet length.
Adding and removing Tags
There is a single base class of Tag that all packet tags must derive from. They are used in two different
tag lists in the packet; the lists have different semantics and different expected use cases.
As the names imply, ByteTags follow bytes and PacketTags follow packets. What this means is that when
operations are done on packets, such as fragmentation, concatenation, and appending or removing headers,
the byte tags keep track of which packet bytes they cover. For instance, if a user creates a TCP segment,
and applies a ByteTag to the segment, each byte of the TCP segment will be tagged. However, if the next
layer down inserts an IPv4 header, this ByteTag will not cover those bytes. The converse is true for the
PacketTag; it covers a packet despite the operations on it.
Each tag type must subclass ns3::Tag, and only one instance of each Tag type may be in each tag list.
Here are a few differences in the behavior of packet tags and byte tags.
• Fragmentation: As mentioned above, when a packet is fragmented, each packet fragment (which is a new
packet) will get a copy of all packet tags, and byte tags will follow the new packet boundaries (i.e.
if the fragmented packets fragment across a buffer region covered by the byte tag, both packet
fragments will still have the appropriate buffer regions byte tagged).
• Concatenation: When packets are combined, two different buffer regions will become one. For byte tags,
the byte tags simply follow the respective buffer regions. For packet tags, only the tags on the first
packet survive the merge.
• Finding and Printing: Both classes allow you to iterate over all of the tags and print them.
• Removal: Users can add and remove the same packet tag multiple times on a single packet (AddPacketTag
() and RemovePacketTag ()). The packet However, once a byte tag is added, it can only be removed by
stripping all byte tags from the packet. Removing one of possibly multiple byte tags is not supported
by the current API.
If a user wants to take an existing packet object and reuse it as a new packet, he or she should remove
all byte tags and packet tags before doing so. An example is the UdpEchoServer class, which takes the
received packet and “turns it around” to send back to the echo client.
The Packet API for byte tags is given below.:
/**
* \param tag the new tag to add to this packet
*
* Tag each byte included in this packet with the
* new tag.
*
* Note that adding a tag is a const operation which is pretty
* un-intuitive. The rationale is that the content and behavior of
* a packet is _not_ changed when a tag is added to a packet: any
* code which was not aware of the new tag is going to work just
* the same if the new tag is added. The real reason why adding a
* tag was made a const operation is to allow a trace sink which gets
* a packet to tag the packet, even if the packet is const (and most
* trace sources should use const packets because it would be
* totally evil to allow a trace sink to modify the content of a
* packet).
*/
void AddByteTag (const Tag &tag) const;
/**
* \returns an iterator over the set of byte tags included in this packet.
*/
ByteTagIterator GetByteTagIterator (void) const;
/**
* \param tag the tag to search in this packet
* \returns true if the requested tag type was found, false otherwise.
*
* If the requested tag type is found, it is copied in the user's
* provided tag instance.
*/
bool FindFirstMatchingByteTag (Tag &tag) const;
/**
* Remove all the tags stored in this packet.
*/
void RemoveAllByteTags (void);
/**
* \param os output stream in which the data should be printed.
*
* Iterate over the tags present in this packet, and
* invoke the Print method of each tag stored in the packet.
*/
void PrintByteTags (std::ostream &os) const;
The Packet API for packet tags is given below.:
/**
* \param tag the tag to store in this packet
*
* Add a tag to this packet. This method calls the
* Tag::GetSerializedSize and, then, Tag::Serialize.
*
* Note that this method is const, that is, it does not
* modify the state of this packet, which is fairly
* un-intuitive.
*/
void AddPacketTag (const Tag &tag) const;
/**
* \param tag the tag to remove from this packet
* \returns true if the requested tag is found, false
* otherwise.
*
* Remove a tag from this packet. This method calls
* Tag::Deserialize if the tag is found.
*/
bool RemovePacketTag (Tag &tag);
/**
* \param tag the tag to search in this packet
* \returns true if the requested tag is found, false
* otherwise.
*
* Search a matching tag and call Tag::Deserialize if it is found.
*/
bool PeekPacketTag (Tag &tag) const;
/**
* Remove all packet tags.
*/
void RemoveAllPacketTags (void);
/**
* \param os the stream in which we want to print data.
*
* Print the list of 'packet' tags.
*
* \sa Packet::AddPacketTag, Packet::RemovePacketTag, Packet::PeekPacketTag,
* Packet::RemoveAllPacketTags
*/
void PrintPacketTags (std::ostream &os) const;
/**
* \returns an object which can be used to iterate over the list of
* packet tags.
*/
PacketTagIterator GetPacketTagIterator (void) const;
Here is a simple example illustrating the use of tags from the code in
src/internet/model/udp-socket-impl.cc:
Ptr<Packet> p; // pointer to a pre-existing packet
SocketIpTtlTag tag
tag.SetTtl (m_ipMulticastTtl); // Convey the TTL from UDP layer to IP layer
p->AddPacketTag (tag);
This tag is read at the IP layer, then stripped (src/internet/model/ipv4-l3-protocol.cc):
uint8_t ttl = m_defaultTtl;
SocketIpTtlTag tag;
bool found = packet->RemovePacketTag (tag);
if (found)
{
ttl = tag.GetTtl ();
}
Fragmentation and concatenation
Packets may be fragmented or merged together. For example, to fragment a packet p of 90 bytes into two
packets, one containing the first 10 bytes and the other containing the remaining 80, one may call the
following code:
Ptr<Packet> frag0 = p->CreateFragment (0, 10);
Ptr<Packet> frag1 = p->CreateFragment (10, 90);
As discussed above, the packet tags from p will follow to both packet fragments, and the byte tags will
follow the byte ranges as needed.
Now, to put them back together:
frag0->AddAtEnd (frag1);
Now frag0 should be equivalent to the original packet p. If, however, there were operations on the
fragments before being reassembled (such as tag operations or header operations), the new packet will not
be the same.
Enabling metadata
We mentioned above that packets, being on-the-wire representations of byte buffers, present a problem to
print out in a structured way unless the printing function has access to the context of the header. For
instance, consider a tcpdump-like printer that wants to pretty-print the contents of a packet.
To enable this usage, packets may have metadata enabled (disabled by default for performance reasons).
This class is used by the Packet class to record every operation performed on the packet’s buffer, and
provides an implementation of Packet::Print () method that uses the metadata to analyze the content of
the packet’s buffer.
The metadata is also used to perform extensive sanity checks at runtime when performing operations on a
Packet. For example, this metadata is used to verify that when you remove a header from a packet, this
same header was actually present at the front of the packet. These errors will be detected and will abort
the program.
To enable this operation, users will typically insert one or both of these statements at the beginning of
their programs:
Packet::EnablePrinting ();
Packet::EnableChecking ();
Sample programs
See src/network/examples/main-packet-header.cc and src/network/examples/main-packet-tag.cc.
Implementation details
Private member variables
A Packet object’s interface provides access to some private data:
Buffer m_buffer;
ByteTagList m_byteTagList;
PacketTagList m_packetTagList;
PacketMetadata m_metadata;
mutable uint32_t m_refCount;
static uint32_t m_globalUid;
Each Packet has a Buffer and two Tags lists, a PacketMetadata object, and a ref count. A static member
variable keeps track of the UIDs allocated. The actual uid of the packet is stored in the PacketMetadata.
Note: that real network packets do not have a UID; the UID is therefore an instance of data that normally
would be stored as a Tag in the packet. However, it was felt that a UID is a special case that is so
often used in simulations that it would be more convenient to store it in a member variable.
Buffer implementation
Class Buffer represents a buffer of bytes. Its size is automatically adjusted to hold any data prepended
or appended by the user. Its implementation is optimized to ensure that the number of buffer resizes is
minimized, by creating new Buffers of the maximum size ever used. The correct maximum size is learned at
runtime during use by recording the maximum size of each packet.
Authors of new Header or Trailer classes need to know the public API of the Buffer class. (add summary
here)
The byte buffer is implemented as follows:
struct BufferData {
uint32_t m_count;
uint32_t m_size;
uint32_t m_initialStart;
uint32_t m_dirtyStart;
uint32_t m_dirtySize;
uint8_t m_data[1];
};
struct BufferData *m_data;
uint32_t m_zeroAreaSize;
uint32_t m_start;
uint32_t m_size;
• BufferData::m_count: reference count for BufferData structure
• BufferData::m_size: size of data buffer stored in BufferData structure
• BufferData::m_initialStart: offset from start of data buffer where data was first inserted
• BufferData::m_dirtyStart: offset from start of buffer where every Buffer which holds a reference to
this BufferData instance have written data so far
• BufferData::m_dirtySize: size of area where data has been written so far
• BufferData::m_data: pointer to data buffer
• Buffer::m_zeroAreaSize: size of zero area which extends before m_initialStart
• Buffer::m_start: offset from start of buffer to area used by this buffer
• Buffer::m_size: size of area used by this Buffer in its BufferData structure
[image] Implementation overview of a packet’s byte Buffer..UNINDENT
This data structure is summarized in Figure Implementation overview of a packet’s byte Buffer.. Each
Buffer holds a pointer to an instance of a BufferData. Most Buffers should be able to share the same
underlying BufferData and thus simply increase the BufferData’s reference count. If they have to change
the content of a BufferData inside the Dirty Area, and if the reference count is not one, they first
create a copy of the BufferData and then complete their state-changing operation.
Tags implementation
(XXX revise me)
Tags are implemented by a single pointer which points to the start of a linked list ofTagData data
structures. Each TagData structure points to the next TagData in the list (its next pointer contains zero
to indicate the end of the linked list). Each TagData contains an integer unique id which identifies the
type of the tag stored in the TagData.:
struct TagData {
struct TagData *m_next;
uint32_t m_id;
uint32_t m_count;
uint8_t m_data[Tags::SIZE];
};
class Tags {
struct TagData *m_next;
};
Adding a tag is a matter of inserting a new TagData at the head of the linked list. Looking at a tag
requires you to find the relevant TagData in the linked list and copy its data into the user data
structure. Removing a tag and updating the content of a tag requires a deep copy of the linked list
before performing this operation. On the other hand, copying a Packet and its tags is a matter of
copying the TagData head pointer and incrementing its reference count.
Tags are found by the unique mapping between the Tag type and its underlying id. This is why at most one
instance of any Tag can be stored in a packet. The mapping between Tag type and underlying id is
performed by a registration as follows:
/* A sample Tag implementation
*/
struct MyTag {
uint16_t m_streamId;
};
Memory management
Describe dataless vs. data-full packets.
Copy-on-write semantics
The current implementation of the byte buffers and tag list is based on COW (Copy On Write). An
introduction to COW can be found in Scott Meyer’s “More Effective C++”, items 17 and 29). This design
feature and aspects of the public interface borrows from the packet design of the Georgia Tech Network
Simulator. This implementation of COW uses a customized reference counting smart pointer class.
What COW means is that copying packets without modifying them is very cheap (in terms of CPU and memory
usage) and modifying them can be also very cheap. What is key for proper COW implementations is being
able to detect when a given modification of the state of a packet triggers a full copy of the data prior
to the modification: COW systems need to detect when an operation is “dirty” and must therefore invoke a
true copy.
Dirty operations:
• ns3::Packet::AddHeader
• ns3::Packet::AddTrailer
• both versions of ns3::Packet::AddAtEnd
• ns3::Packet::RemovePacketTag
Non-dirty operations:
• ns3::Packet::AddPacketTag
• ns3::Packet::PeekPacketTag
• ns3::Packet::RemoveAllPacketTags
• ns3::Packet::AddByteTag
• ns3::Packet::FindFirstMatchingByteTag
• ns3::Packet::RemoveAllByteTags
• ns3::Packet::RemoveHeader
• ns3::Packet::RemoveTrailer
• ns3::Packet::CreateFragment
• ns3::Packet::RemoveAtStart
• ns3::Packet::RemoveAtEnd
• ns3::Packet::CopyData
Dirty operations will always be slower than non-dirty operations, sometimes by several orders of
magnitude. However, even the dirty operations have been optimized for common use-cases which means that
most of the time, these operations will not trigger data copies and will thus be still very fast.
Error Model
This section documents a few error model objects, typically associated with NetDevice models, that are
maintained as part of the network module:
• RateErrorModel
• ListErrorModel
• ReceiveListErrorModel
• BurstErrorModel
Error models are used to indicate that a packet should be considered to be errored, according to the
underlying (possibly stochastic or empirical) error model.
Model Description
The source code for error models live in the directory src/packet/utils.
Two types of error models are generally provided. The first are stochastic models. In this case,
packets are errored according to underlying random variable distributions. An example of this is the
RateErrorModel. The other type of model is a deterministic or empirical model, in which packets are
errored according to a particular prescribed pattern. An example is the ListErrorModel that allows users
to specify the list of packets to be errored, by listing the specific packet UIDs.
The ns3::RateErrorModel errors packets according to an underlying random variable distribution, which is
by default a UniformRandomVariable distributed between 0.0 and 1.0. The error rate and error units (bit,
byte, or packet) are set by the user. For instance, by setting ErrorRate to 0.1 and ErrorUnit to
“Packet”, in the long run, around 10% of the packets will be lost.
Design
Error models are ns-3 objects and can be created using the typical pattern of CreateObject<>(). They
have configuration attributes.
An ErrorModel can be applied anywhere, but are commonly deployed on NetDevice models so that artificial
losses (mimicking channel losses) can be induced.
Scope and Limitations
No known limitations. There are no existing models that try to modify the packet contents (e.g. apply
bit or byte errors to the byte buffers). This type of operation will likely be performance-expensive,
and existing Packet APIs may not easily support it.
The ns-3 spectrum model and devices that derive from it (e.g. LTE) have their own error model base class,
found in
References
The initial ns-3 error models were ported from ns-2 (queue/errmodel.{cc,h})
Usage
The base class API is as follows:
• bool ErrorModel::IsCorrupt (Ptr<Packet> pkt): Evaluate the packet and return true or false whether the
packet should be considered errored or not. Some models could potentially alter the contents of the
packet bit buffer.
• void ErrorModel::Reset (void): Reset any state.
• void ErrorModel::Enable (void): Enable the model
• void ErrorModel::Disble (void): Disable the model; IsCorrupt() will always return false.
• bool ErrorModel::IsEnabled (void) const: Return the enabled state
Many ns-3 NetDevices contain attributes holding pointers to error models. The error model is applied in
the notional physical layer processing chain of the device, and drops should show up on the PhyRxDrop
trace source of the device. The following are known to include an attribute with a pointer available to
hold this type of error model:
• SimpleNetDevice
• PointToPointNetDevice
• CsmaNetDevice
• VirtualNetDevice
However, the ErrorModel could be used anywhere where packets are used
Helpers
This model is typically not used with helpers.
Attributes
The RateErrorModel contains the following attributes:
Output
What kind of data does the model generate? What are the key trace sources? What kind of logging output
can be enabled?
Examples
Error models are used in the tutorial fifth and sixth programs.
The directory examples/error-model/ contains an example simple-error-model.cc that exercises the Rate and
List error models.
The TCP example examples/tcp/tcp-nsc-lfn.cc uses the Rate error model.
Troubleshooting
No known issues.
Validation
The error-model unit test suite provides a single test case of of a particular combination of ErrorRate
and ErrorUnit for the RateErrorModel applied to a SimpleNetDevice.
Acknowledgements
The basic ErrorModel, RateErrorModel, and ListErrorModel classes were ported from ns-2 to ns-3 in 2007.
The ReceiveListErrorModel was added at that time.
The burst error model is due to Truc Anh N. Nguyen at the University of Kansas (James P.G. Sterbenz <‐
jpgs@ittc.ku.edu>, director, ResiliNets Research Group (http://wiki.ittc.ku.edu/resilinets), Information
and Telecommunication Technology Center (ITTC) and Department of Electrical Engineering and Computer
Science, The University of Kansas Lawrence, KS USA). Work supported in part by NSF FIND (Future Internet
Design) Program under grant CNS-0626918 (Postmodern Internet Architecture), NSF grant CNS-1050226
(Multilayer Network Resilience Analysis and Experimentation on GENI), US Department of Defense (DoD), and
ITTC at The University of Kansas.
Node and NetDevices Overview
This chapter describes how ns-3 nodes are put together, and provides a walk-through of how packets
traverse an internet-based Node.
[image] High-level node architecture.UNINDENT
In ns-3, nodes are instances of ns3::Node. This class may be subclassed, but instead, the conceptual
model is that we aggregate or insert objects to it rather than define subclasses.
One might think of a bare ns-3 node as a shell of a computer, to which one may add NetDevices (cards)
and other innards including the protocols and applications. High-level node architecture illustrates
that ns3::Node objects contain a list of ns3::Application instances (initially, the list is empty), a
list of ns3::NetDevice instances (initially, the list is empty), a list of ns3::Node::ProtocolHandler
instances, a unique integer ID, and a system ID (for distributed simulation).
The design tries to avoid putting too many dependencies on the class ns3::Node, ns3::Application, or
ns3::NetDevice for the following:
• IP version, or whether IP is at all even used in the ns3::Node.
• implementation details of the IP stack.
From a software perspective, the lower interface of applications corresponds to the C-based sockets API.
The upper interface of ns3::NetDevice objects corresponds to the device independent sublayer of the Linux
stack. Everything in between can be aggregated and plumbed together as needed.
Let’s look more closely at the protocol demultiplexer. We want incoming frames at layer-2 to be delivered
to the right layer-3 protocol such as IPv4. The function of this demultiplexer is to register callbacks
for receiving packets. The callbacks are indexed based on the EtherType in the layer-2 frame.
Many different types of higher-layer protocols may be connected to the NetDevice, such as IPv4, IPv6,
ARP, MPLS, IEEE 802.1x, and packet sockets. Therefore, the use of a callback-based demultiplexer avoids
the need to use a common base class for all of these protocols, which is problematic because of the
different types of objects (including packet sockets) expected to be registered there.
Sockets APIs
The sockets API is a long-standing API used by user-space applications to access network services in the
kernel. A socket is an abstraction, like a Unix file handle, that allows applications to connect to
other Internet hosts and exchange reliable byte streams and unreliable datagrams, among other services.
ns-3 provides two types of sockets APIs, and it is important to understand the differences between them.
The first is a native ns-3 API, while the second uses the services of the native API to provide a
POSIX-like API as part of an overall application process. Both APIs strive to be close to the typical
sockets API that application writers on Unix systems are accustomed to, but the POSIX variant is much
closer to a real system’s sockets API.
ns-3 sockets API
The native sockets API for ns-3 provides an interface to various types of transport protocols (TCP, UDP)
as well as to packet sockets and, in the future, Netlink-like sockets. However, users are cautioned to
understand that the semantics are not the exact same as one finds in a real system (for an API which is
very much aligned to real systems, see the next section).
ns3::Socket is defined in src/network/model/socket.h. Readers will note that many public member
functions are aligned with real sockets function calls, and all other things being equal, we have tried
to align with a Posix sockets API. However, note that:
• ns-3 applications handle a smart pointer to a Socket object, not a file descriptor;
• there is no notion of synchronous API or a blocking API; in fact, the model for interaction between
application and socket is one of asynchronous I/O, which is not typically found in real systems (more
on this below);
• the C-style socket address structures are not used;
• the API is not a complete sockets API, such as supporting all socket options or all function variants;
• many calls use ns3::Packet class to transfer data between application and socket. This may seem
peculiar to pass Packets across a stream socket API, but think of these packets as just fancy byte
buffers at this level (more on this also below).
Basic operation and calls
[image] Implementation overview of native sockets API.UNINDENT
Creating sockets
An application that wants to use sockets must first create one. On real systems using a C-based API,
this is accomplished by calling socket()
int socket(int domain, int type, int protocol);
which creates a socket in the system and returns an integer descriptor.
In ns-3, we have no equivalent of a system call at the lower layers, so we adopt the following model.
There are certain factory objects that can create sockets. Each factory is capable of creating one type
of socket, and if sockets of a particular type are able to be created on a given node, then a factory
that can create such sockets must be aggregated to the Node:
static Ptr<Socket> CreateSocket (Ptr<Node> node, TypeId tid);
Examples of TypeIds to pass to this method are ns3::TcpSocketFactory, ns3::PacketSocketFactory, and
ns3::UdpSocketFactory.
This method returns a smart pointer to a Socket object. Here is an example:
Ptr<Node> n0;
// Do some stuff to build up the Node's internet stack
Ptr<Socket> localSocket =
Socket::CreateSocket (n0, TcpSocketFactory::GetTypeId ());
In some ns-3 code, sockets will not be explicitly created by user’s main programs, if an ns-3 application
does it. For instance, for ns3::OnOffApplication, the function ns3::OnOffApplication::StartApplication()
performs the socket creation, and the application holds the socket pointer.
Using sockets
Below is a typical sequence of socket calls for a TCP client in a real implementation:
sock = socket(PF_INET, SOCK_STREAM, IPPROTO_TCP);
bind(sock, ...);
connect(sock, ...);
send(sock, ...);
recv(sock, ...);
close(sock);
There are analogs to all of these calls in ns-3, but we will focus on two aspects here. First, most
usage of sockets in real systems requires a way to manage I/O between the application and kernel. These
models include blocking sockets, signal-based I/O, and non-blocking sockets with polling. In ns-3, we
make use of the callback mechanisms to support a fourth mode, which is analogous to POSIX asynchronous
I/O.
In this model, on the sending side, if the send() call were to fail because of insufficient buffers, the
application suspends the sending of more data until a function registered at the
ns3::Socket::SetSendCallback() callback is invoked. An application can also ask the socket how much
space is available by calling ns3::Socket::GetTxAvailable(). A typical sequence of events for sending
data (ignoring connection setup) might be:
SetSendCallback (MakeCallback(&HandleSendCallback));
Send ();
Send ();
...
// Send fails because buffer is full
// Wait until HandleSendCallback is called
// HandleSendCallback is called by socket, since space now available
Send (); // Start sending again
Similarly, on the receive side, the socket user does not block on a call to recv(). Instead, the
application sets a callback with ns3::Socket::SetRecvCallback() in which the socket will notify the
application when (and how much) there is data to be read, and the application then calls
ns3::Socket::Recv() to read the data until no more can be read.
Packet vs. buffer variants
There are two basic variants of Send() and Recv() supported:
virtual int Send (Ptr<Packet> p) = 0;
int Send (const uint8_t* buf, uint32_t size);
Ptr<Packet> Recv (void);
int Recv (uint8_t* buf, uint32_t size);
The non-Packet variants are provided for legacy API reasons. When calling the raw buffer variant of
ns3::Socket::Send(), the buffer is immediately written into a Packet and the packet variant is invoked.
Users may find it semantically odd to pass a Packet to a stream socket such as TCP. However, do not let
the name bother you; think of ns3::Packet to be a fancy byte buffer. There are a few reasons why the
Packet variants are more likely to be preferred in ns-3:
• Users can use the Tags facility of packets to, for example, encode a flow ID or other helper data at
the application layer.
• Users can exploit the copy-on-write implementation to avoid memory copies (on the receive side, the
conversion back to a uint8_t* buf may sometimes incur an additional copy).
• Use of Packet is more aligned with the rest of the ns-3 API
Sending dummy data
Sometimes, users want the simulator to just pretend that there is an actual data payload in the packet
(e.g. to calculate transmission delay) but do not want to actually produce or consume the data. This is
straightforward to support in ns-3; have applications call Create<Packet> (size); instead of
Create<Packet> (buffer, size);. Similarly, passing in a zero to the pointer argument in the raw buffer
variants has the same effect. Note that, if some subsequent code tries to read the Packet data buffer,
the fake buffer will be converted to a real (zeroed) buffer on the spot, and the efficiency will be lost
there.
Use of Send() vs. SendTo()
There are two variants of methods used to send data to the socket:
virtual int Send (Ptr<Packet> p, uint32_t flags) = 0;
virtual int SendTo (Ptr<Packet> p, uint32_t flags,
const Address &toAddress) = 0;
The first method is used if the socket has already been connected (Socket::Connect()) to a peer address.
In the case of stream-based sockets like TCP, the connect call is required to bind the socket to a peer
address, and thereafter, Send() is typically used. In the case of datagram-based sockets like UDP, the
socket is not required to be connected to a peer address before sending, and the socket may be used to
send data to different destination addresses; in this case, the SendTo() method is used to specify the
destination address for the datagram.
Socket options
ToS (Type of Service)
The native sockets API for ns-3 provides two public methods (of the Socket base class):
void SetIpTos (uint8_t ipTos);
uint8_t GetIpTos (void) const;
to set and get, respectively, the type of service associated with the socket. These methods are
equivalent to using the IP_TOS option of BSD sockets. Clearly, setting the type of service only applies
to sockets using the IPv4 protocol. However, users typically do not set the type of service associated
with a socket through ns3::Socket::SetIpTos() because sockets are normally created by application helpers
and users cannot get a pointer to the sockets. Instead, users can create an address of type
ns3::InetSocketAddress with the desired type of service value and pass it to the application helpers:
InetSocketAddress destAddress (ipv4Address, udpPort);
destAddress.SetTos (tos);
OnOffHelper onoff ("ns3::UdpSocketFactory", destAddress);
For this to work, the application must eventually call the ns3::Socket::Connect() method to connect to
the provided destAddress and the Connect method of the particular socket type must support setting the
type of service associated with a socket (by using the ns3::Socket::SetIpTos() method). Currently, the
socket types that support setting the type of service in such a way are ns3::UdpSocketImpl and
ns3::TcpSocketBase.
The type of service associated with a socket is then used to determine the value of the Type of Service
field (renamed as Differentiated Services field by RFC 2474) of the IPv4 header of the packets sent
through that socket, as detailed in the next sections.
Setting the ToS with UDP sockets
For IPv4 packets, the ToS field is set according to the following rules:
• If the socket is connected, the ToS field is set to the ToS value associated with the socket.
• If the socket is not connected, the ToS field is set to the value specified in the destination address
(of type ns3::InetSocketAddress) passed to ns3::Socket::SendTo(), and the ToS value associated with the
socket is ignored.
Setting the ToS with TCP sockets
For IPv4 packets, the ToS field is set to the ToS value associated with the socket.
Priority
The native sockets API for ns-3 provides two public methods (of the Socket base class):
void SetPriority (uint8_t priority);
uint8_t GetPriority (void) const;
to set and get, respectively, the priority associated with the socket. These methods are equivalent to
using the SO_PRIORITY option of BSD sockets. Only values in the range 0..6 can be set through the above
method.
Note that setting the type of service associated with a socket (by calling ns3::Socket::SetIpTos()) also
sets the priority for the socket to the value that the ns3::Socket::IpTos2Priority() function returns
when it is passed the type of service value. This function is implemented after the Linux rt_tos2priority
function, which takes an 8-bit value as input and returns a value which is a function of bits 3-6 (where
bit 0 is the most significant bit) of the input value:
┌─────────┬──────────────────────┐
│Bits 3-6 │ Priority │
├─────────┼──────────────────────┤
│0 to 3 │ 0 (Best Effort) │
├─────────┼──────────────────────┤
│4 to 7 │ 2 (Bulk) │
├─────────┼──────────────────────┤
│8 to 11 │ 6 (Interactive) │
├─────────┼──────────────────────┤
│12 to 15 │ 4 (Interactive Bulk) │
└─────────┴──────────────────────┘
The rationale is that bits 3-6 of the Type of Service field were interpreted as the TOS subfield by (the
obsolete) RFC 1349. Readers can refer to the doxygen documentation of ns3::Socket::IpTos2Priority() for
more information, including how DSCP values map onto priority values.
The priority set for a socket (as described above) is then used to determine the priority of the packets
sent through that socket, as detailed in the next sections. Currently, the socket types that support
setting the packet priority are ns3::UdpSocketImpl, ns3::TcpSocketBase and ns3::PacketSocket. The packet
priority is used, e.g., by queuing disciplines such as the default PfifoFastQueueDisc to classify packets
into distinct queues.
Setting the priority with UDP sockets
If the packet is an IPv4 packet and the value to be inserted in the ToS field is not null, then the
packet is assigned a priority based on such ToS value (according to the ns3::Socket::IpTos2Priority()
function). Otherwise, the priority associated with the socket is assigned to the packet.
Setting the priority with TCP sockets
Every packet is assigned a priority equal to the priority associated with the socket.
Setting the priority with packet sockets
Every packet is assigned a priority equal to the priority associated with the socket.
Socket errno
to be completed
Example programs
to be completed
POSIX-like sockets API
Simple NetDevice
Placeholder chapter
Queues
This section documents the queue object, which is typically used by NetDevices and QueueDiscs to store
packets.
Packets stored in a queue can be managed according to different policies. Currently, only the DropTail
policy is available.
Model Description
The source code for the new module lives in the directory src/network/utils.
ns3::Queue has been redesigned as a template class object to allow us to instantiate queues storing
different types of items. The unique template type parameter specifies the type of items stored in the
queue. The only requirement on the item type is that it must provide a GetSize () method which returns
the size of the packet included in the item. Currently, queue items can be objects of the following
classes:
• Packet
• QueueItem and subclasses (e.g., QueueDiscItem)
• WifiMacQueueItem
The internal queues of the queue discs are of type Queue<QueueDiscItem> (an alias of which being
InternalQueue). A number of network devices (SimpleNetDevice, PointToPointNetDevice, CsmaNetDevice) use a
Queue<Packet> to store packets to be transmitted. WifiNetDevices use instead queues of type WifiMacQueue,
which is a subclass of Queue storing objects of type WifiMacQueueItem. Other devices, such as WiMax and
LTE, use specialized queues.
Design
The Queue class derives from the QueueBase class, which is a non-template class providing all the methods
that are independent of the type of the items stored in the queue. The Queue class provides instead all
the operations that depend on the item type, such as enqueue, dequeue, peek and remove. The Queue class
also provides the ability to trace certain queue operations such as enqueuing, dequeuing, and dropping.
Queue is an abstract base class and is subclassed for specific scheduling and drop policies. Subclasses
need to define the following public methods:
• bool Enqueue (Ptr<Item> item): Enqueue a packet
• Ptr<Item> Dequeue (void): Dequeue a packet
• Ptr<Item> Remove (void): Remove a packet
• Ptr<const Item> Peek (void): Peek a packet
The Enqueue method does not allow to store a packet if the queue capacity is exceeded. Subclasses may
also define specialized public methods. For instance, the WifiMacQueue class provides a method to dequeue
a packet based on its tid and MAC address.
There are five trace sources that may be hooked:
• Enqueue
• Dequeue
• Drop
• DropBeforeEnqueue
• DropAfterDequeue
Also, the QueueBase class defines two additional trace sources:
• PacketsInQueue
• BytesInQueue
DropTail
This is a basic first-in-first-out (FIFO) queue that performs a tail drop when the queue is full.
The DropTailQueue class defines one attribute:
• MaxSize: the maximum queue size
Usage
Helpers
A typical usage pattern is to create a device helper and to configure the queue type and attributes from
the helper, such as this example:
PointToPointHelper p2p;
p2p.SetQueue ("ns3::DropTailQueue");
p2p.SetDeviceAttribute ("DataRate", StringValue ("10Mbps"));
p2p.SetChannelAttribute ("Delay", StringValue ("2ms"));
NetDeviceContainer devn0n2 = p2p.Install (n0n2);
p2p.SetQueue ("ns3::DropTailQueue");
p2p.SetDeviceAttribute ("DataRate", StringValue ("10Mbps"));
p2p.SetChannelAttribute ("Delay", StringValue ("3ms"));
NetDeviceContainer devn1n2 = p2p.Install (n1n2);
p2p.SetQueue ("ns3::DropTailQueue",
"MaxSize", StringValue ("50p"));
p2p.SetDeviceAttribute ("DataRate", StringValue (linkDataRate));
p2p.SetChannelAttribute ("Delay", StringValue (linkDelay));
NetDeviceContainer devn2n3 = p2p.Install (n2n3);
Please note that the SetQueue method of the PointToPointHelper class allows to specify
“ns3::DropTailQueue” instead of “ns3::DropTailQueue<Packet>”. The same holds for CsmaHelper,
SimpleNetDeviceHelper and TrafficControlHelper.
Output
The ns-3 ascii trace helpers used by many of the NetDevices will hook the Enqueue, Dequeue, and Drop
traces of these queues and print out trace statements, such as the following from
examples/udp/udp-echo.cc:
+ 2 /NodeList/0/DeviceList/1/$ns3::CsmaNetDevice/TxQueue/Enqueue ns3::EthernetHeader
( length/type=0x806, source=00:00:00:00:00:01, destination=ff:ff:ff:ff:ff:ff)
ns3::ArpHeader (request source mac: 00-06-00:00:00:00:00:01 source ipv4: 10.1.1.1
dest ipv4: 10.1.1.2) Payload (size=18) ns3::EthernetTrailer (fcs=0)
- 2 /NodeList/0/DeviceList/1/$ns3::CsmaNetDevice/TxQueue/Dequeue ns3::EthernetHeader
( length/type=0x806, source=00:00:00:00:00:01, destination=ff:ff:ff:ff:ff:ff)
ns3::ArpHeader (request source mac: 00-06-00:00:00:00:00:01 source ipv4: 10.1.1.1
dest ipv4: 10.1.1.2) Payload (size=18) ns3::EthernetTrailer (fcs=0)
which shows an enqueue “+” and dequeue “-” event at time 2 seconds.
Users are, of course, free to define and hook their own trace sinks to these trace sources.
Examples
The drop-tail queue is used in several examples, such as examples/udp/udp-echo.cc.
Queue limits
This section documents the queue limits model, which is used by the traffic control to limit the
NetDevices queueing delay. It operates on the transmission path of the network node.
The reduction of the NetDevices queueing delay is essential to improve the effectiveness of Active Queue
Management (AQM) algorithms. Careful assessment of the queueing delay includes a byte-based measure of
the NetDevices queue length. In this design, traffic control can use different byte-based schemes to
limit the queueing delay. Currently the only available scheme is DynamicQueueLimits, which is modelled
after the dynamic queue limit library of Linux.
Model Description
The source code for the model lives in the directory src/network/utils.
The model allows a byte-based measure of the netdevice queue. The byte-based measure more accurately
approximates the time required to empty the queue than a packet-based measure.
To inform the upper layers about the transmission of packets, NetDevices can call a couple of functions:
• void NotifyQueuedBytes (uint32_t bytes): Report the number of bytes queued to the device queue
• void NotifyTransmittedBytes (uint32_t bytes): Report the number of bytes transmitted by device
Based on this information, the QueueLimits object can stop the transmission queue.
In case of multiqueue NetDevices this mechanism is available for each queue.
The QueueLimits model can be used on any NetDevice modelled in ns-3.
Design
An abstract base class, class QueueLimits, is subclassed for specific byte-based limiting strategies.
Common operations provided by the base class QueueLimits include:
• void Reset (): Reset queue limits state
• void Completed (uint32_t count): Record the number of completed bytes and recalculate the limit
• int32_t Available () const: Return how many bytes can be queued
• void Queued (uint32_t count): Record number of bytes queued
DynamicQueueLimits
Dynamic queue limits (DQL) is a basic library implemented in the Linux kernel to limit the Ethernet
queueing delay. DQL is a general purpose queue length controller. The goal of DQL is to calculate the
limit as the minimum number of bytes needed to prevent starvation.
Three attributes are defined in the DynamicQueueLimits class:
• HoldTime: The DQL algorithm hold time
• MaxLimit: Maximum limit
• MinLimit: Minimum limit
The DQL algorithm hold time is 1 s. Reducing the HoldTime increases the responsiveness of DQL with
consequent greater number of limit variation events. Conversely, increasing the HoldTime decreases the
responsiveness of DQL with a minor number of limit variation events. The limit calculated by DQL is in
the range from MinLimit to MaxLimit. The default values are respectively 0 and DQL_MAX_LIMIT.
Increasing the MinLimit is recommended in case of higher NetDevice transmission rate (e.g. 1 Gbps) while
reducing the MaxLimit is recommended in case of lower NetDevice transmission rate (e.g. 500 Kbps).
There is one trace source in DynamicQueueLimits class that may be hooked:
• Limit: Limit value calculated by DQL
Usage
Helpers
A typical usage pattern is to create a traffic control helper and configure the queue limits type and
attributes from the helper, such as this example:
TrafficControlHelper tch;
uint32_t handle = tch.SetRootQueueDisc ("ns3::PfifoFastQueueDisc", "Limit", UintegerValue (1000));
tch.SetQueueLimits ("ns3::DynamicQueueLimits", "HoldTime", StringValue ("4ms"));
then install the configuration on a NetDevices container
tch.Install (devices);
NIX-VECTOR ROUTING DOCUMENTATION
Nix-vector routing is a simulation specific routing protocol and is intended for large network
topologies. The on-demand nature of this protocol as well as the low-memory footprint of the nix-vector
provides improved performance in terms of memory usage and simulation run time when dealing with a large
number of nodes.
Model Description
The source code for the NixVectorRouting module lives in the directory src/nix-vector-routing.
ns-3 nix-vector-routing performs on-demand route computation using a breadth-first search and an
efficient route-storage data structure known as a nix-vector.
When a packet is generated at a node for transmission, the route is calculated, and the nix-vector is
built.
How is the Nix-Vector calculated? The nix-vector stores an index for each hop along the path, which
corresponds to the neighbor-index. This index is used to determine which net-device and gateway should
be used.
How does the routing take place? To route a packet, the nix-vector must be transmitted with the packet.
At each hop, the current node extracts the appropriate neighbor-index from the nix-vector and transmits
the packet through the corresponding net-device. This continues until the packet reaches the destination.
NOTE:
Nix-Vector routing does not use any routing metrics (interface metrics) during the calculation of
nix-vector. It is only based on the shortest path calculated according to BFS.
How does Nix decide between two equally short path from source to destination? It depends on how the
topology is constructed i.e., the order in which the net-devices are added on a node and net-devices
added on the channels associated with current node’s net-devices. Please check the nix-simple.cc example
below to understand how nix-vectors are calculated.
ns-3 supports IPv4 as well as IPv6 Nix-Vector routing.
Scope and Limitations
Currently, the ns-3 model of nix-vector routing supports IPv4 and IPv6 p2p links, CSMA links and multiple
WiFi networks with the same channel object. It does not (yet) provide support for efficient adaptation
to link failures. It simply flushes all nix-vector routing caches.
NixVectorRouting performs a subnet matching check, but it does not check entirely if the addresses have
been appropriately assigned. In other terms, using Nix-Vector routing, it is possible to have a working
network that violates some good practices in IP address assignments.
In case of IPv6, Nix assumes the link-local addresses assigned are unique. When using the IPv6 stack,
the link-local address allocation is unique by default over the entire topology. However, if the
link-local addresses are assigned manually, the user must ensure uniqueness of link-local addresses.
Usage
The usage pattern is the one of all the Internet routing protocols. Since NixVectorRouting is not
installed by default in the Internet stack, it is necessary to set it in the Internet Stack helper by
using InternetStackHelper::SetRoutingHelper.
Remember to include the header file ns3/nix-vector-helper.h to use IPv4 or IPv6 Nix-Vector routing.
NOTE:
The previous header file ns3/ipv4-nix-vector-helper.h is maintained for backward compatibility
reasons. Therefore, the existing IPv4 Nix-Vector routing simulations should work fine.
• Using IPv4 Nix-Vector Routing:
Ipv4NixVectorHelper nixRouting;
InternetStackHelper stack;
stack.SetRoutingHelper (nixRouting); // has effect on the next Install ()
stack.Install (allNodes); // allNodes is the NodeContainer
• Using IPv6 Nix-Vector Routing:
Ipv6NixVectorHelper nixRouting;
InternetStackHelper stack;
stack.SetRoutingHelper (nixRouting); // has effect on the next Install ()
stack.Install (allNodes); // allNodes is the NodeContainer
NOTE:
The NixVectorHelper helper class helps to use NixVectorRouting functionality. The NixVectorRouting
model can also be used directly to use Nix-Vector routing. To use it directly, the header file
ns3/nix-vector-routing.h should be included. The previous header file ns3/ipv4-nix-vector-routing.h is
maintained for backwards compatibility with any existing IPv4 Nix-Vector routing simulations.
Examples
The examples for the NixVectorRouting module lives in the directory src/nix-vector-routing/examples.
There are examples which use both IPv4 and IPv6 networking.
• nix-simple.cc
/*
* ________
* / \
* n0 -- n1 -- n2 -- n3
*
* n0 IP: 10.1.1.1, 10.1.4.1
* n1 IP: 10.1.1.2, 10.1.2.1
* n2 IP: 10.1.2.2, 10.1.3.1, 10.1.4.2
* n3 IP: 10.1.3.2
*/
In this topology, we install Nix-Vector routing between source n0 and destination n3. The shortest
possible route will be n0 -> n2 -> n3.
Let’s see how the nix-vector will be generated for this path:
n0 has 2 neighbors i.e. n1 and n3. n0 is connected to both using separate net-devices. But the net-device
for n0 – n1 p2p link was created before the netdevice for n0 – n2 p2p link. Thus, n2 has neighbor-index
of 1 (n1 has 0) with respect to n0.
n2 has 3 neighbors i.e. n1, n3 and n0. The n2 net-device for n1 – n2 p2p link was created before the n2
net-device for n2 – n3 p2p link which was before the n2 netdevice for n0 – n2 p2p link. This, n3 has
neighbor-index of 01 (n1 has 00 and n0 has 10) with repect to n2.
Thus, the nix-vector for the path from n0 to n3 is 101.
NOTE:
This neighbor-index or nix-index has total number of bits equal to minimum number of bits required to
represent all the neighbors in their binary form.
NOTE:
If there are multiple netdevices connected to the current netdevice on the channel then it depends on
which order netdevices were added to the channel.
#. Using IPv4: .. code-block:: bash
# By default IPv4 network is selected ./waf –run src/nix-vector-routing/examples/nix-simple
#. Using IPv6: .. code-block:: bash
# Use the –useIPv6 flag ./waf –run “src/nix-vector-routing/examples/nix-simple –useIPv6”
• nms-p2p-nix.cc
This example demonstrates the advantage of Nix-Vector routing as Nix performs source-based routing (BFS)
to have faster routing.
• nix-simple-multi-address.cc
This is an IPv4 example demonstrating multiple interface addresses. This example also shows how address
assignment in between the simulation causes the all the route caches and Nix caches to flush.
# By default IPv4 network is selected
./waf --run src/nix-vector-routing/examples/nix-simple-multi-address
• nix-double-wifi.cc
This example demonstrates the working of Nix with two Wifi networks operating on the same Wifi channel
object. The example uses ns3::YansWifiChannel for both the wifi networks.
#. Using IPv4: .. code-block:: bash
# By default IPv4 network is selected ./waf –run src/nix-vector-routing/examples/nix-double-wifi #
Use the –enableNixLog to enable NixVectorRouting logging. ./waf –run
“src/nix-vector-routing/examples/nix-double-wifi –enableNixLog”
#. Using IPv6: .. code-block:: bash
# Use the –useIPv6 flag ./waf –run “src/nix-vector-routing/examples/nix-double-wifi –useIPv6” #
Use the –enableNixLog to enable NixVectorRouting logging. ./waf –run
“src/nix-vector-routing/examples/nix-double-wifi –useIPv6 –enableNixLog”
OPTIMIZED LINK STATE ROUTING (OLSR)
This model implements the base specification of the Optimized Link State Routing (OLSR) protocol, which
is a dynamic mobile ad hoc unicast routing protocol. It has been developed at the University of Murcia
(Spain) by Francisco J. Ros for NS-2, and was ported to NS-3 by Gustavo Carneiro at INESC Porto
(Portugal).
The implementation is based on OLSR Version 1 (RFC 3626 [rfc3626]) and it is not compliant with OLSR
Version 2 (RFC 7181 [rfc7181]) or any of the Version 2 extensions.
Model Description
The source code for the OLSR model lives in the directory src/olsr. As stated before, the model is based
on RFC 3626 ([rfc3626]). Moreover, many design choices are based on the previous ns2 model.
Scope and Limitations
The model is for IPv4 only.
• Mostly compliant with OLSR as documented in RFC 3626 ([rfc3626]),
• The use of multiple interfaces was not supported by the NS-2 version, but is supported in NS-3;
• OLSR does not respond to the routing event notifications corresponding to dynamic interface up and down
(ns3::RoutingProtocol::NotifyInterfaceUp and ns3::RoutingProtocol::NotifyInterfaceDown) or address
insertion/removal ns3::RoutingProtocol::NotifyAddAddress and
ns3::RoutingProtocol::NotifyRemoveAddress).
• Unlike the NS-2 version, does not yet support MAC layer feedback as described in RFC 3626 ([rfc3626]);
Host Network Association (HNA) is supported in this implementation of OLSR. Refer to examples/olsr-hna.cc
to see how the API is used.
References
[rfc3626]
RFC 3626 Optimized Link State Routing
[rfc7181]
RFC 7181 The Optimized Link State Routing Protocol Version 2
Usage
The usage pattern is the one of all the Internet routing protocols. Since OLSR is not installed by
default in the Internet stack, it is necessary to set it in the Internet Stack helper by using
InternetStackHelper::SetRoutingHelper
Typically, OLSR is enabled in a main program by use of an OlsrHelper class that installs OLSR into an
Ipv4ListRoutingProtocol object. The following sample commands will enable OLSR in a simulation using this
helper class along with some other routing helper objects. The setting of priority value 10, ahead of the
staticRouting priority of 0, means that OLSR will be consulted for a route before the node’s static
routing table.:
NodeContainer c:
...
// Enable OLSR
NS_LOG_INFO ("Enabling OLSR Routing.");
OlsrHelper olsr;
Ipv4StaticRoutingHelper staticRouting;
Ipv4ListRoutingHelper list;
list.Add (staticRouting, 0);
list.Add (olsr, 10);
InternetStackHelper internet;
internet.SetRoutingHelper (list);
internet.Install (c);
Once installed,the OLSR “main interface” can be set with the SetMainInterface() command. If the user does
not specify a main address, the protocol will select the first primary IP address that it finds, starting
first the loopback interface and then the next non-loopback interface found, in order of Ipv4 interface
index. The loopback address of 127.0.0.1 is not selected. In addition, a number of protocol constants are
defined in olsr-routing-protocol.cc.
Olsr is started at time zero of the simulation, based on a call to Object::Start() that eventually calls
OlsrRoutingProtocol::DoStart(). Note: a patch to allow the user to start and stop the protocol at other
times would be welcome.
Examples
The examples are in the src/olsr/examples/ directory. However, many other examples esists in the general
examples directory, e.g., examples/routing/manet-routing-compare.cc.
For specific examples of the HNA feature, see the examples in src/olsr/examples/.
Helpers
A helper class for OLSR has been written. After an IPv4 topology has been created and unique IP
addresses assigned to each node, the simulation script writer can call one of three overloaded functions
with different scope to enable OLSR: ns3::OlsrHelper::Install (NodeContainer container);
ns3::OlsrHelper::Install (Ptr<Node> node); or ns3::OlsrHelper::InstallAll (void)
Attributes
In addition, the behavior of OLSR can be modified by changing certain attributes. The method
ns3::OlsrHelper::Set () can be used to set OLSR attributes. These include HelloInterval, TcInterval,
MidInterval, Willingness. Other parameters are defined as macros in olsr-routing-protocol.cc.
The list of configurabel attributes is:
• HelloInterval (time, default 2s), HELLO messages emission interval.
• TcInterval (time, default 5s), TC messages emission interval.
• MidInterval (time, default 5s), MID messages emission interval.
• HnaInterval (time, default 5s), HNA messages emission interval.
• Willingness (enum, default OLSR_WILL_DEFAULT), Willingness of a node to carry and forward traffic for
other nodes.
Tracing
The available traces are:
• Rx: Receive OLSR packet.
• Tx: Send OLSR packet.
• RoutingTableChanged: The OLSR routing table has changed.
Caveats
Presently, OLSR is limited to use with an Ipv4ListRouting object, and does not respond to dynamic changes
to a device’s IP address or link up/down notifications; i.e. the topology changes are due to loss/gain of
connectivity over a wireless channel.
The code does not present any known issue.
Validation
The code validationhas been done through Wireshark message compliance and unit testings.
OPENFLOW SWITCH SUPPORT
ns-3 simulations can use OpenFlow switches (McKeown et al. [1]), widely used in research. OpenFlow
switches are configurable via the OpenFlow API, and also have an MPLS extension for quality-of-service
and service-level-agreement support. By extending these capabilities to ns-3 for a simulated OpenFlow
switch that is both configurable and can use the MPLS extension, ns-3 simulations can accurately simulate
many different switches.
The OpenFlow software implementation distribution is hereby referred to as the OFSID. This is a
demonstration of running OpenFlow in software that the OpenFlow research group has made available. There
is also an OFSID that Ericsson researchers created to add MPLS capabilities; this is the OFSID currently
used with ns-3. The design will allow the users to, with minimal effort, switch in a different OFSID that
may include more efficient code than a previous OFSID.
Model Description
The model relies on building an external OpenFlow switch library (OFSID), and then building some ns-3
wrappers that call out to the library. The source code for the ns-3 wrappers lives in the directory
src/openflow/model.
Design
The OpenFlow module presents a OpenFlowSwitchNetDevice and a OpenFlowSwitchHelper for installing it on
nodes. Like the Bridge module, it takes a collection of NetDevices to set up as ports, and it acts as the
intermediary between them, receiving a packet on one port and forwarding it on another, or all but the
received port when flooding. Like an OpenFlow switch, it maintains a configurable flow table that can
match packets by their headers and do different actions with the packet based on how it matches. The
module’s understanding of OpenFlow configuration messages are kept the same format as a real
OpenFlow-compatible switch, so users testing Controllers via ns-3 won’t have to rewrite their Controller
to work on real OpenFlow-compatible switches.
The ns-3 OpenFlow switch device models an OpenFlow-enabled switch. It is designed to express basic use of
the OpenFlow protocol, with the maintaining of a virtual Flow Table and TCAM to provide OpenFlow-like
results.
The functionality comes down to the Controllers, which send messages to the switch that configure its
flows, producing different effects. Controllers can be added by the user, under the ofi namespace
extending ofi::Controller. To demonstrate this, a DropController, which creates flows for ignoring every
single packet, and LearningController, which effectively makes the switch a more complicated
BridgeNetDevice. A user versed in a standard OFSID, and/or OF protocol, can write virtual controllers to
create switches of all kinds of types.
OpenFlow switch Model
The OpenFlow switch device behaves somewhat according to the diagram setup as a classical OFSID switch,
with a few modifications made for a proper simulation environment.
Normal OF-enabled Switch:
| Secure Channel | <--OF Protocol--> | Controller is external |
| Hardware or Software Flow Table |
ns-3 OF-enabled Switch (module):
| m_controller->ReceiveFromSwitch() | <--OF Protocol--> | Controller is internal |
| Software Flow Table, virtual TCAM |
In essence, there are two differences:
1) No SSL, Embedded Controller: Instead of a secure channel and connecting to an outside location for the
Controller program/machine, we currently only allow a Controller extended from ofi::Controller, an
extension of an ns3::Object. This means ns-3 programmers cannot model the SSL part of the interface or
possibility of network failure. The connection to the OpenFlowSwitch is local and there aren’t any
reasons for the channel/connection to break down. <<This difference may be an option in the future. Using
EmuNetDevices, it should be possible to engage an external Controller program/machine, and thus work with
controllers designed outside of the ns-3 environment, that simply use the proper OF protocol when
communicating messages to the switch through a tap device.>>
2) Virtual Flow Table, TCAM: Typical OF-enabled switches are implemented on a hardware TCAM. The OFSID we
turn into a library includes a modelled software TCAM, that produces the same results as a hardware TCAM.
We include an attribute FlowTableLookupDelay, which allows a simple delay of using the TCAM to be
modelled. We don’t endeavor to make this delay more complicated, based on the tasks we are running on the
TCAM, that is a possible future improvement.
The OpenFlowSwitch network device is aimed to model an OpenFlow switch, with a TCAM and a connection to a
controller program. With some tweaking, it can model every switch type, per OpenFlow’s extensibility. It
outsources the complexity of the switch ports to NetDevices of the user’s choosing. It should be noted
that these NetDevices must behave like practical switch ports, i.e. a Mac Address is assigned, and
nothing more. It also must support a SendFrom function so that the OpenFlowSwitch can forward across that
port.
Scope and Limitations
All MPLS capabilities are implemented on the OFSID side in the OpenFlowSwitchNetDevice, but ns-3-mpls
hasn’t been integrated, so ns-3 has no way to pass in proper MPLS packets to the OpenFlowSwitch. If it
did, one would only need to make BufferFromPacket pick up the MplsLabelStack or whatever the MPLS header
is called on the Packet, and build the MPLS header into the ofpbuf.
Future Work
References
[1] McKeown, N.; Anderson, T.; Balakrishan, H.; Parulkar, G.; Peterson, L.; Rexford, J.; Shenker, S.;
Turner, J.; OpenFlow: enabling innovation in campus networks, ACM SIGCOMM Computer Communication
Review, Vol. 38, Issue 2, April 2008.
Usage
The OFSID requires libxml2 (for MPLS FIB xml file parsing), and libdl (for address fault checking).
Building OFSID
In order to use the OpenFlowSwitch module, you must create and link the OFSID (OpenFlow Software
Implementation Distribution) to ns-3. To do this:
1. Obtain the OFSID code. An ns-3 specific OFSID branch is provided to ensure operation with ns-3. Use
mercurial to download this branch and waf to build the library:
$ hg clone http://code.nsnam.org/openflow
$ cd openflow
From the “openflow” directory, run:
$ ./waf configure
$ ./waf build
2. Your OFSID is now built into a libopenflow.a library! To link to an ns-3 build with this OpenFlow
switch module, run from the ns-3-dev (or whatever you have named your distribution):
$ ./waf configure --enable-examples --enable-tests --with-openflow=path/to/openflow
3. Under ---- Summary of optional NS-3 features: you should see:
"NS-3 OpenFlow Integration : enabled"
indicating the library has been linked to ns-3. Run:
$ ./waf build
to build ns-3 and activate the OpenFlowSwitch module in ns-3.
Examples
For an example demonstrating its use in a simple learning controller/switch, run:
$ ./waf --run openflow-switch
To see it in detailed logging, run:
$ ./waf --run "openflow-switch -v"
Helpers
Attributes
The SwitchNetDevice provides following Attributes:
• FlowTableLookUpDelay: This time gets run off the clock when making a lookup in our Flow Table.
•
Flags: OpenFlow specific configuration flags. They are defined in the ofp_config_flags enum. Choices
include:
OFPC_SEND_FLOW_EXP (Switch notifies controller when a flow has expired), OFPC_FRAG_NORMAL (Match
fragment against Flow table), OFPC_FRAG_DROP (Drop fragments), OFPC_FRAG_REASM (Reassemble only
if OFPC_IP_REASM set, which is currently impossible, because switch implementation does not
support IP reassembly) OFPC_FRAG_MASK (Mask Fragments)
•
FlowTableMissSendLength: When the packet doesn’t match in our Flow Table, and we forward to the
controller,
this sets # of bytes forwarded (packet is not forwarded in its entirety, unless specified).
NOTE:
TODO
Tracing
NOTE:
TODO
Logging
NOTE:
TODO
Caveats
NOTE:
TODO
Validation
This model has one test suite which can be run as follows:
$ ./test.py --suite=openflow
POINTTOPOINT NETDEVICE
This is the introduction to PointToPoint NetDevice chapter, to complement the PointToPoint model doxygen.
Overview of the PointToPoint model
The ns-3 point-to-point model is of a very simple point to point data link connecting exactly two
PointToPointNetDevice devices over an PointToPointChannel. This can be viewed as equivalent to a full
duplex RS-232 or RS-422 link with null modem and no handshaking.
Data is encapsulated in the Point-to-Point Protocol (PPP – RFC 1661), however the Link Control Protocol
(LCP) and associated state machine is not implemented. The PPP link is assumed to be established and
authenticated at all times.
Data is not framed, therefore Address and Control fields will not be found. Since the data is not
framed, there is no need to provide Flag Sequence and Control Escape octets, nor is a Frame Check
Sequence appended. All that is required to implement non-framed PPP is to prepend the PPP protocol number
for IP Version 4 which is the sixteen-bit number 0x21 (see http://www.iana.org/assignments/ppp-numbers).
The PointToPointNetDevice provides following Attributes:
• Address: The ns3::Mac48Address of the device (if desired);
• DataRate: The data rate (ns3::DataRate) of the device;
• TxQueue: The transmit queue (ns3::Queue) used by the device;
• InterframeGap: The optional ns3::Time to wait between “frames”;
• Rx: A trace source for received packets;
• Drop: A trace source for dropped packets.
The PointToPointNetDevice models a transmitter section that puts bits on a corresponding channel “wire.”
The DataRate attribute specifies the number of bits per second that the device will simulate sending over
the channel. In reality no bits are sent, but an event is scheduled for an elapsed time consistent with
the number of bits in each packet and the specified DataRate. The implication here is that the receiving
device models a receiver section that can receive any any data rate. Therefore there is no need, nor way
to set a receive data rate in this model. By setting the DataRate on the transmitter of both devices
connected to a given PointToPointChannel one can model a symmetric channel; or by setting different
DataRates one can model an asymmetric channel (e.g., ADSL).
The PointToPointNetDevice supports the assignment of a “receive error model.” This is an ErrorModel
object that is used to simulate data corruption on the link.
Point-to-Point Channel Model
The point to point net devices are connected via an PointToPointChannel. This channel models two wires
transmitting bits at the data rate specified by the source net device. There is no overhead beyond the
eight bits per byte of the packet sent. That is, we do not model Flag Sequences, Frame Check Sequences
nor do we “escape” any data.
The PointToPointChannel provides following Attributes:
• Delay: An ns3::Time specifying the propagation delay for the channel.
Using the PointToPointNetDevice
The PointToPoint net devices and channels are typically created and configured using the associated
PointToPointHelper object. The various ns3 device helpers generally work in a similar way, and their use
is seen in many of our example programs and is also covered in the ns-3 tutorial.
The conceptual model of interest is that of a bare computer “husk” into which you plug net devices. The
bare computers are created using a NodeContainer helper. You just ask this helper to create as many
computers (we call them Nodes) as you need on your network:
NodeContainer nodes;
nodes.Create (2);
Once you have your nodes, you need to instantiate a PointToPointHelper and set any attributes you may
want to change. Note that since this is a point-to-point (as compared to a point-to-multipoint) there may
only be two nodes with associated net devices connected by a PointToPointChannel.:
PointToPointHelper pointToPoint;
pointToPoint.SetDeviceAttribute ("DataRate", StringValue ("5Mbps"));
pointToPoint.SetChannelAttribute ("Delay", StringValue ("2ms"));
Once the attributes are set, all that remains is to create the devices and install them on the required
nodes, and to connect the devices together using a PointToPoint channel. When we create the net devices,
we add them to a container to allow you to use them in the future. This all takes just one line of code.:
NetDeviceContainer devices = pointToPoint.Install (nodes);
PointToPoint Tracing
Like all ns-3 devices, the Point-to-Point Model provides a number of trace sources. These trace sources
can be hooked using your own custom trace code, or you can use our helper functions to arrange for
tracing to be enabled on devices you specify.
Upper-Level (MAC) Hooks
From the point of view of tracing in the net device, there are several interesting points to insert trace
hooks. A convention inherited from other simulators is that packets destined for transmission onto
attached networks pass through a single “transmit queue” in the net device. We provide trace hooks at
this point in packet flow, which corresponds (abstractly) only to a transition from the network to data
link layer, and call them collectively the device MAC hooks.
When a packet is sent to the Point-to-Point net device for transmission it always passes through the
transmit queue. The transmit queue in the PointToPointNetDevice inherits from Queue, and therefore
inherits three trace sources:
• An Enqueue operation source (see ns3::Queue::m_traceEnqueue);
• A Dequeue operation source (see ns3::Queue::m_traceDequeue);
• A Drop operation source (see ns3::Queue::m_traceDrop).
The upper-level (MAC) trace hooks for the PointToPointNetDevice are, in fact, exactly these three trace
sources on the single transmit queue of the device.
The m_traceEnqueue event is triggered when a packet is placed on the transmit queue. This happens at the
time that ns3::PointtoPointNetDevice::Send or ns3::PointToPointNetDevice::SendFrom is called by a higher
layer to queue a packet for transmission. An Enqueue trace event firing should be interpreted as only
indicating that a higher level protocol has sent a packet to the device.
The m_traceDequeue event is triggered when a packet is removed from the transmit queue. Dequeues from the
transmit queue can happen in two situations: 1) If the underlying channel is idle when
PointToPointNetDevice::Send is called, a packet is dequeued from the transmit queue and immediately
transmitted; 2) a packet may be dequeued and immediately transmitted in an internal
TransmitCompleteEvent that functions much like a transmit complete interrupt service routine. An Dequeue
trace event firing may be viewed as indicating that the PointToPointNetDevice has begun transmitting a
packet.
Lower-Level (PHY) Hooks
Similar to the upper level trace hooks, there are trace hooks available at the lower levels of the net
device. We call these the PHY hooks. These events fire from the device methods that talk directly to the
PointToPointChannel.
The trace source m_dropTrace is called to indicate a packet that is dropped by the device. This happens
when a packet is discarded as corrupt due to a receive error model indication (see ns3::ErrorModel and
the associated attribute “ReceiveErrorModel”).
The other low-level trace source fires on reception of a packet (see
ns3::PointToPointNetDevice::m_rxTrace) from the PointToPointChannel.
PROPAGATION
The ns-3 propagation module defines two generic interfaces, namely PropagationLossModel and
PropagationDelayModel, to model respectively the propagation loss and the propagation delay.
PropagationLossModel
Propagation loss models calculate the Rx signal power considering the Tx signal power and the mutual Rx
and Tx antennas positions.
A propagation loss model can be “chained” to another one, making a list. The final Rx power takes into
account all the chained models. In this way one can use a slow fading and a fast fading model (for
example), or model separately different fading effects.
The following propagation loss models are implemented:
• Cost231PropagationLossModel
• FixedRssLossModel
• FriisPropagationLossModel
• ItuR1411LosPropagationLossModel
• ItuR1411NlosOverRooftopPropagationLossModel
• JakesPropagationLossModel
• Kun2600MhzPropagationLossModel
• LogDistancePropagationLossModel
• MatrixPropagationLossModel
• NakagamiPropagationLossModel
• OkumuraHataPropagationLossModel
• RandomPropagationLossModel
• RangePropagationLossModel
• ThreeLogDistancePropagationLossModel
• TwoRayGroundPropagationLossModel
• ThreeGppPropagationLossModel
• ThreeGppRMaPropagationLossModel
• ThreeGppUMaPropagationLossModel
• ThreeGppUmiStreetCanyonPropagationLossModel
• ThreeGppIndoorOfficePropagationLossModel
Other models could be available thanks to other modules, e.g., the building module.
Each of the available propagation loss models of ns-3 is explained in one of the following subsections.
FriisPropagationLossModel
This model implements the Friis propagation loss model. This model was first described in [friis]. The
original equation was described as:
\frac{P_r}{P_t} = \frac{A_r A_t}{d^2\lambda^2}
with the following equation for the case of an isotropic antenna with no heat loss:
A_{isotr.} = \frac{\lambda^2}{4\pi}
The final equation becomes:
\frac{P_r}{P_t} = \frac{\lambda^2}{(4 \pi d)^2}
Modern extensions to this original equation are:
P_r = \frac{P_t G_t G_r \lambda^2}{(4 \pi d)^2 L}
With:
P_t : transmission power (W)
P_r : reception power (W)
G_t : transmission gain (unit-less)
G_r : reception gain (unit-less)
\lambda : wavelength (m)
d : distance (m)
L : system loss (unit-less)
In the implementation, \lambda is calculated as \frac{C}{f}, where C = 299792458 m/s is the speed of
light in vacuum, and f is the frequency in Hz which can be configured by the user via the Frequency
attribute.
The Friis model is valid only for propagation in free space within the so-called far field region, which
can be considered approximately as the region for d > 3 \lambda. The model will still return a value for
d < 3 \lambda, as doing so (rather than triggering a fatal error) is practical for many simulation
scenarios. However, we stress that the values obtained in such conditions shall not be considered
realistic.
Related with this issue, we note that the Friis formula is undefined for d = 0, and results in P_r > P_t
for d < \lambda / 2 \sqrt{\pi}.
Both these conditions occur outside of the far field region, so in principle the Friis model shall not be
used in these conditions. In practice, however, Friis is often used in scenarios where accurate
propagation modeling is not deemed important, and values of d = 0 can occur.
To allow practical use of the model in such scenarios, we have to 1) return some value for d = 0, and 2)
avoid large discontinuities in propagation loss values (which could lead to artifacts such as bogus
capture effects which are much worse than inaccurate propagation loss values). The two issues are
conflicting, as, according to the Friis formula, \lim_{d \to 0} P_r = +\infty; so if, for d = 0, we use
a fixed loss value, we end up with an infinitely large discontinuity, which as we discussed can cause
undesirable simulation artifacts.
To avoid these artifact, this implementation of the Friis model provides an attribute called MinLoss
which allows to specify the minimum total loss (in dB) returned by the model. This is used in such a way
that P_r continuously increases for d \to 0, until MinLoss is reached, and then stay constant; this allow
to return a value for d = 0 and at the same time avoid discontinuities. The model won’t be much
realistic, but at least the simulation artifacts discussed before are avoided. The default value of
MinLoss is 0 dB, which means that by default the model will return P_r = P_t for d <= \lambda / 2
\sqrt{\pi}. We note that this value of d is outside of the far field region, hence the validity of the
model in the far field region is not affected.
TwoRayGroundPropagationLossModel
This model implements a Two-Ray Ground propagation loss model ported from NS2
The Two-ray ground reflection model uses the formula
P_r = \frac{P_t * G_t * G_r * (H_t^2 * H_r^2)}{d^4 * L}
The original equation in Rappaport’s book assumes L = 1. To be consistent with the free space equation,
L is added here.
H_t and H_r are set at the respective nodes z coordinate plus a model parameter set via SetHeightAboveZ.
The two-ray model does not give a good result for short distances, due to the oscillation caused by
constructive and destructive combination of the two rays. Instead the Friis free-space model is used for
small distances.
The crossover distance, below which Friis is used, is calculated as follows:
dCross = \frac{(4 * \pi * H_t * H_r)}{\lambda}
In the implementation, \lambda is calculated as \frac{C}{f}, where C = 299792458 m/s is the speed of
light in vacuum, and f is the frequency in Hz which can be configured by the user via the Frequency
attribute.
LogDistancePropagationLossModel
This model implements a log distance propagation model.
The reception power is calculated with a so-called log-distance propagation model:
L = L_0 + 10 n \log(\frac{d}{d_0})
where:
n : the path loss distance exponent
d_0 : reference distance (m)
L_0 : path loss at reference distance (dB)
d : - distance (m)
L : path loss (dB)
When the path loss is requested at a distance smaller than the reference distance, the tx power is
returned.
ThreeLogDistancePropagationLossModel
This model implements a log distance path loss propagation model with three distance fields. This model
is the same as ns3::LogDistancePropagationLossModel except that it has three distance fields: near,
middle and far with different exponents.
Within each field the reception power is calculated using the log-distance propagation equation:
L = L_0 + 10 \cdot n_0 \log_{10}(\frac{d}{d_0})
Each field begins where the previous ends and all together form a continuous function.
There are three valid distance fields: near, middle, far. Actually four: the first from 0 to the
reference distance is invalid and returns txPowerDbm.
\underbrace{0 \cdots\cdots}_{=0} \underbrace{d_0 \cdots\cdots}_{n_0} \underbrace{d_1 \cdots\cdots}_{n_1}
\underbrace{d_2 \cdots\cdots}_{n_2} \infty
Complete formula for the path loss in dB:
\displaystyle L =
\begin{cases} 0 & d < d_0 \\ L_0 + 10 \cdot n_0 \log_{10}(\frac{d}{d_0}) & d_0 \leq d < d_1 \\ L_0 + 10
\cdot n_0 \log_{10}(\frac{d_1}{d_0}) + 10 \cdot n_1 \log_{10}(\frac{d}{d_1}) & d_1 \leq d < d_2 \\ L_0 +
10 \cdot n_0 \log_{10}(\frac{d_1}{d_0}) + 10 \cdot n_1 \log_{10}(\frac{d_2}{d_1}) + 10 \cdot n_2
\log_{10}(\frac{d}{d_2})& d_2 \leq d \end{cases}
where:
d_0, d_1, d_2 : three distance fields (m)
n_0, n_1, n_2 : path loss distance exponent for each field (unitless)
L_0 : path loss at reference distance (dB)
d : - distance (m)
L : path loss (dB)
When the path loss is requested at a distance smaller than the reference distance d_0, the tx power (with
no path loss) is returned. The reference distance defaults to 1m and reference loss defaults to
FriisPropagationLossModel with 5.15 GHz and is thus L_0 = 46.67 dB.
JakesPropagationLossModel
ToDo
RandomPropagationLossModel
The propagation loss is totally random, and it changes each time the model is called. As a consequence,
all the packets (even those between two fixed nodes) experience a random propagation loss.
NakagamiPropagationLossModel
This propagation loss model implements the Nakagami-m fast fading model, which accounts for the
variations in signal strength due to multipath fading. The model does not account for the path loss due
to the distance traveled by the signal, hence for typical simulation usage it is recommended to consider
using it in combination with other models that take into account this aspect.
The Nakagami-m distribution is applied to the power level. The probability density function is defined as
p(x; m, \omega) = \frac{2 m^m}{\Gamma(m) \omega^m} x^{2m - 1} e^{-\frac{m}{\omega} x^2} )
with m the fading depth parameter and \omega the average received power.
It is implemented by either a GammaRandomVariable or a ErlangRandomVariable random variable.
The implementation of the model allows to specify different values of the m parameter (and hence
different fast fading profiles) for three different distance ranges:
\underbrace{0 \cdots\cdots}_{m_0} \underbrace{d_1 \cdots\cdots}_{m_1} \underbrace{d_2 \cdots\cdots}_{m_2} \infty
For m = 1 the Nakagami-m distribution equals the Rayleigh distribution. Thus this model also implements
Rayleigh distribution based fast fading.
FixedRssLossModel
This model sets a constant received power level independent of the transmit power.
The received power is constant independent of the transmit power; the user must set received power level.
Note that if this loss model is chained to other loss models, it should be the first loss model in the
chain. Else it will disregard the losses computed by loss models that precede it in the chain.
MatrixPropagationLossModel
The propagation loss is fixed for each pair of nodes and doesn’t depend on their actual positions. This
model should be useful for synthetic tests. Note that by default the propagation loss is assumed to be
symmetric.
RangePropagationLossModel
This propagation loss depends only on the distance (range) between transmitter and receiver.
The single MaxRange attribute (units of meters) determines path loss. Receivers at or within MaxRange
meters receive the transmission at the transmit power level. Receivers beyond MaxRange receive at power
-1000 dBm (effectively zero).
OkumuraHataPropagationLossModel
This model is used to model open area pathloss for long distance (i.e., > 1 Km). In order to include all
the possible frequencies usable by LTE we need to consider several variants of the well known Okumura
Hata model. In fact, the original Okumura Hata model [hata] is designed for frequencies ranging from 150
MHz to 1500 MHz, the COST231 [cost231] extends it for the frequency range from 1500 MHz to 2000 MHz.
Another important aspect is the scenarios considered by the models, in fact the all models are originally
designed for urban scenario and then only the standard one and the COST231 are extended to suburban,
while only the standard one has been extended to open areas. Therefore, the model cannot cover all
scenarios at all frequencies. In the following we detail the models adopted.
The pathloss expression of the COST231 OH is:
L = 46.3 + 33.9\log{f} - 13.82 \log{h_\mathrm{b}} + (44.9 - 6.55\log{h_\mathrm{b}})\log{d} -
F(h_\mathrm{M}) + C
where
F(h_\mathrm{M}) = \left\{\begin{array}{ll} (1.1\log(f))-0.7 \times h_\mathrm{M} - (1.56\times
\log(f)-0.8) & \mbox{for medium and small size cities} \\ 3.2\times (\log{(11.75\times h_\mathrm{M}}))^2
& \mbox{for large cities}\end{array} \right.
C = \left\{\begin{array}{ll} 0dB & \mbox{for medium-size cities and suburban areas} \\ 3dB & \mbox{for
large cities}\end{array} \right.
and
f : frequency [MHz]
h_\mathrm{b} : eNB height above the ground [m]
h_\mathrm{M} : UE height above the ground [m]
d : distance [km]
log : is a logarithm in base 10 (this for the whole document)
This model is only for urban scenarios.
The pathloss expression of the standard OH in urban area is:
L = 69.55 + 26.16\log{f} - 13.82 \log{h_\mathrm{b}} + (44.9 - 6.55\log{h_\mathrm{b}})\log{d} - C_\mathrm{H}
where for small or medium sized city
C_\mathrm{H} = 0.8 + (1.1\log{f} - 0.7)h_\mathrm{M} -1.56\log{f}
and for large cities
C_\mathrm{H} = \left\{\begin{array}{ll} 8.29 (\log{(1.54h_M)})^2 -1.1 & \mbox{if } 150\leq f\leq 200 \\
3.2(\log{(11.75h_M)})^2 -4.97 & \mbox{if } 200<f\leq 1500\end{array} \right.
There extension for the standard OH in suburban is
L_\mathrm{SU} = L_\mathrm{U} - 2 \left(\log{\frac{f}{28}}\right)^2 - 5.4
where
L_\mathrm{U} : pathloss in urban areas
The extension for the standard OH in open area is
L_\mathrm{O} = L_\mathrm{U} - 4.70 (\log{f})^2 + 18.33\log{f} - 40.94
The literature lacks of extensions of the COST231 to open area (for suburban it seems that we can just
impose C = 0); therefore we consider it a special case fo the suburban one.
Cost231PropagationLossModel
ToDo
ItuR1411LosPropagationLossModel
This model is designed for Line-of-Sight (LoS) short range outdoor communication in the frequency range
300 MHz to 100 GHz. This model provides an upper and lower bound respectively according to the following
formulas
L_\mathrm{LoS,l} = L_\mathrm{bp} + \left\{\begin{array}{ll} 20\log{\frac{d}{R_\mathrm{bp}}} & \mbox{for
$d \le R_\mathrm{bp}$} \\ 40\log{\frac{d}{R_\mathrm{bp}}} & \mbox{for $d > R_\mathrm{bp}$}\end{array} \right.
L_\mathrm{LoS,u} = L_\mathrm{bp} + 20 + \left\{\begin{array}{ll} 25\log{\frac{d}{R_\mathrm{bp}}} &
\mbox{for $d \le R_\mathrm{bp}$} \\ 40\log{\frac{d}{R_\mathrm{bp}}} & \mbox{for $d >
R_\mathrm{bp}$}\end{array} \right.
where the breakpoint distance is given by
R_\mathrm{bp} \approx \frac{4h_\mathrm{b}h_\mathrm{m}}{\lambda}
and the above parameters are
\lambda : wavelength [m]
h_\mathrm{b} : eNB height above the ground [m]
h_\mathrm{m} : UE height above the ground [m]
d : distance [m]
and L_{bp} is the value for the basic transmission loss at the break point, defined as:
L_{bp} = \left|20\log \left(\frac{\lambda^2}{8\pi h_\mathrm{b}h\mathrm{m}}\right)\right|
The value used by the simulator is the average one for modeling the median pathloss.
ItuR1411NlosOverRooftopPropagationLossModel
This model is designed for Non-Line-of-Sight (LoS) short range outdoor communication over rooftops in the
frequency range 300 MHz to 100 GHz. This model includes several scenario-dependent parameters, such as
average street width, orientation, etc. It is advised to set the values of these parameters manually
(using the ns-3 attribute system) according to the desired scenario.
In detail, the model is based on [walfisch] and [ikegami], where the loss is expressed as the sum of
free-space loss (L_{bf}), the diffraction loss from rooftop to street (L_{rts}) and the reduction due to
multiple screen diffraction past rows of building (L_{msd}). The formula is:
L_{NLOS1} = \left\{ \begin{array}{ll} L_{bf} + L_{rts} + L_{msd} & \mbox{for } L_{rts} + L_{msd} > 0 \\
L_{bf} & \mbox{for } L_{rts} + L_{msd} \le 0\end{array}\right.
The free-space loss is given by:
L_{bf} = 32.4 + 20 \log {(d/1000)} + 20\log{(f)}
where:
f : frequency [MHz]
d : distance (where d > 1) [m]
The term L_{rts} takes into account the width of the street and its orientation, according to the
formulas
L_{rts} = -8.2 - 10\log {(w)} + 10\log{(f)} + 20\log{(\Delta h_m)} + L_{ori}
L_{ori} = \left\{ \begin{array}{lll} -10 + 0.354\varphi & \mbox{for } 0^{\circ} \le \varphi < 35^{\circ}
\\ 2.5 + 0.075(\varphi-35) & \mbox{for } 35^{\circ} \le \varphi < 55^{\circ} \\ 4.0 -0.114(\varphi-55) &
\mbox{for } 55^{\circ} \varphi \le 90^{\circ}\end{array}\right.
\Delta h_m = h_r - h_m
where:
h_r : is the height of the rooftop [m]
h_m : is the height of the mobile [m]
\varphi : is the street orientation with respect to the direct path (degrees)
The multiple screen diffraction loss depends on the BS antenna height relative to the building height and
on the incidence angle. The former is selected as the higher antenna in the communication link. Regarding
the latter, the “settled field distance” is used for select the proper model; its value is given by
d_{s} = \frac{\lambda d^2}{\Delta h_{b}^2}
with
\Delta h_b = h_b - h_m
Therefore, in case of l > d_s (where l is the distance over which the building extend), it can be
evaluated according to
L_{msd} = L_{bsh} + k_{a} + k_{d}\log{(d/1000)} + k_{f}\log{(f)} - 9\log{(b)}
L_{bsh} = \left\{ \begin{array}{ll} -18\log{(1+\Delta h_{b})} & \mbox{for } h_{b} > h_{r} \\ 0 &
\mbox{for } h_{b} \le h_{hr} \end{array}\right.
k_a = \left\{ \begin{array}{lll}
71.4 & \mbox{for } h_{b} > h_{r} \mbox{ and } f>2000 \mbox{ MHz} \\
54 & \mbox{for } h_{b} > h_{r} \mbox{ and } f\le2000 \mbox{ MHz} \\
54-0.8\Delta h_b & \mbox{for } h_{b} \le h_{r} \mbox{ and } d \ge 500 \mbox{ m} \\
54-1.6\Delta h_b & \mbox{for } h_{b} \le h_{r} \mbox{ and } d < 500 \mbox{ m} \\
\end{array} \right.
k_d = \left\{ \begin{array}{ll}
18 & \mbox{for } h_{b} > h_{r} \\
18 -15\frac{\Delta h_b}{h_r} & \mbox{for } h_{b} \le h_{r}
\end{array} \right.
k_f = \left\{ \begin{array}{ll}
-8 & \mbox{for } f>2000 \mbox{ MHz} \\
-4 + 0.7(f/925 -1) & \mbox{for medium city and suburban centres and} f\le2000 \mbox{ MHz} \\
-4 + 1.5(f/925 -1) & \mbox{for metropolitan centres and } f\le2000 \mbox{ MHz}
\end{array}\right.
Alternatively, in case of l < d_s, the formula is:
L_{msd} = -10\log{\left(Q_M^2\right)}
where
Q_M = \left\{ \begin{array}{lll}
2.35\left(\frac{\Delta h_b}{d}\sqrt{\frac{b}{\lambda}}\right)^{0.9} & \mbox{for } h_{b} > h_{r} \\
\frac{b}{d} & \mbox{for } h_{b} \approx h_{r} \\
\frac{b}{2\pi d}\sqrt{\frac{\lambda}{\rho}}\left(\frac{1}{\theta}-\frac{1}{2\pi + \theta}\right) &
\mbox{for } h_{b} < h_{r}
\end{array}\right.
where:
\theta = arc tan \left(\frac{\Delta h_b}{b}\right)
\rho = \sqrt{\Delta h_b^2 + b^2}
Kun2600MhzPropagationLossModel
This is the empirical model for the pathloss at 2600 MHz for urban areas which is described in
[kun2600mhz]. The model is as follows. Let d be the distance between the transmitter and the receiver in
meters; the pathloss L in dB is calculated as:
L = 36 + 26\log{d}
ThreeGppPropagationLossModel
The base class ThreeGppPropagationLossModel and its derived classes implement the path loss and shadow
fading models described in 3GPP TR 38.901 [38901]. 3GPP TR 38.901 includes multiple scenarios modeling
different propagation environments, i.e., indoor, outdoor urban and rural, for frequencies between 0.5
and 100 GHz.
Implemented features:
• Path loss and shadowing models (3GPP TR 38.901, Sec. 7.4.1)
• Autocorrelation of shadow fading (3GPP TR 38.901, Sec. 7.4.4)
• Channel condition models (3GPP TR 38.901, Sec. 7.4.2)
To be implemented:
• O2I penetration loss (3GPP TR 38.901, Sec. 7.4.3)
• Spatial consistent update of the channel states (3GPP TR 38.901 Sec. 7.6.3.3)
Configuration
The ThreeGppPropagationLossModel instance is paired with a ChannelConditionModel instance used to
retrieve the LOS/NLOS channel condition. By default, a 3GPP channel condition model related to the same
scenario is set (e.g., by default, ThreeGppRmaPropagationLossModel is paired with
ThreeGppRmaChannelConditionModel), but it can be configured using the method SetChannelConditionModel.
The channel condition models are stored inside the propagation module, for a limitation of the current
spectrum API and to avoid a circular dependency between the spectrum and the propagation modules. Please
note that it is necessary to install at least one ChannelConditionModel when using any
ThreeGppPropagationLossModel subclass. Please look below for more information about the Channel Condition
models.
The operating frequency has to be set using the attribute “Frequency”, otherwise an assert is raised. The
addition of the shadow fading component can be enabled/disabled through the attribute “ShadowingEnabled”.
Other scenario-related parameters can be configured through attributes of the derived classes.
Implementation details
The method DoCalcRxPower computes the propagation loss considering the path loss and the shadow fading
(if enabled). The path loss is computed by the method GetLossLos or GetLossNlos depending on the LOS/NLOS
channel condition, and their implementation is left to the derived classes. The shadow fading is computed
by the method GetShadowing, which generates an additional random loss component characterized by Gaussian
distribution with zero mean and scenario-specific standard deviation. Subsequent shadowing components of
each BS-UT link are correlated as described in 3GPP TR 38.901, Sec. 7.4.4 [38901].
Note 1: The TR defines height ranges for UTs and BSs, depending on the chosen propagation model (for the
exact values, please see below in the specific model documentation). If the user does not set correct
values, the model will emit a warning but perform the calculation anyway.
Note 2: The 3GPP model is originally intended to be used to represent BS-UT links. However, in ns-3, we
may need to compute the pathloss between two BSs or UTs to evaluate the interference. We have decided to
support this case by considering the tallest node as a BS and the smallest as a UT. As a consequence, the
height values may be outside the validity range of the chosen class: therefore, an inaccuracy warning may
be printed, but it can be ignored.
There are four derived class, each one implementing the propagation model for a different scenario:
ThreeGppRMaPropagationLossModel
This class implements the LOS/NLOS path loss and shadow fading models described in 3GPP TR 38.901
[38901], Table 7.4.1-1 for the RMa scenario. It supports frequencies between 0.5 and 30 GHz. It is
possible to configure some scenario-related parameters through the attributes AvgBuildingHeight and
AvgStreetWidth.
As specified in the TR, the 2D distance between the transmitter and the receiver should be between 10 m
and 10 km for the LOS case, or between 10 m and 5 km for the NLOS case, otherwise the model may not be
accurate (a warning message is printed if the user has enabled logging on the model). Also, the height of
the base station (hBS) should be between 10 m and 150 m, while the height of the user terminal (hUT)
should be between 1 m and 10 m.
ThreeGppUMaPropagationLossModel
This implements the LOS/NLOS path loss and shadow fading models described in 3GPP TR 38.901 [38901],
Table 7.4.1-1 for the UMa scenario. It supports frequencies between 0.5 and 100 GHz.
As specified in the TR, the 2D distance between the transmitter and the receiver should be between 10 m
and 5 km both for the LOS and NLOS cases, otherwise the model may not be accurate (a warning message is
printed if the user has enabled logging on the model). Also, the height of the base station (hBS) should
be 25 m and the height of the user terminal (hUT) should be between 1.5 m and 22.5 m.
ThreeGppUmiStreetCanyonPropagationLossModel
This implements the LOS/NLOS path loss and shadow fading models described in 3GPP TR 38.901 [38901],
Table 7.4.1-1 for the UMi-Street Canyon scenario. It supports frequencies between 0.5 and 100 GHz.
As specified in the TR, the 2D distance between the transmitter and the receiver should be between 10 m
and 5 km both for the LOS and NLOS cases, otherwise the model may not be accurate (a warning message is
printed if the user has enabled logging on the model). Also, the height of the base station (hBS) should
be 10 m and the height of the user terminal (hUT) should be between 1.5 m and 10 m (the validity range is
reduced because we assume that the height of the UT nodes is always lower that the height of the BS
nodes).
ThreeGppIndoorOfficePropagationLossModel
This implements the LOS/NLOS path loss and shadow fading models described in 3GPP TR 38.901 [38901],
Table 7.4.1-1 for the Indoor-Office scenario. It supports frequencies between 0.5 and 100 GHz.
As specified in the TR, the 3D distance between the transmitter and the receiver should be between 1 m
and 150 m both for the LOS and NLOS cases, otherwise the model may not be accurate (a warning log message
is printed if the user has enabled logging on the model).
Testing
The test suite ThreeGppPropagationLossModelsTestSuite provides test cases for the classes implementing
the 3GPP propagation loss models. The test cases ThreeGppRmaPropagationLossModelTestCase,
ThreeGppUmaPropagationLossModelTestCase, ThreeGppUmiPropagationLossModelTestCase and
ThreeGppIndoorOfficePropagationLossModelTestCase compute the path loss between two nodes and compares it
with the value obtained using the formulas in 3GPP TR 38.901 [38901], Table 7.4.1-1. The test case
ThreeGppShadowingTestCase checks if the shadowing is correctly computed by testing the deviation of the
overall propagation loss from the path loss. The test is carried out for all the scenarios, both in LOS
and NLOS condition.
ChannelConditionModel
The loss models require to know if two nodes are in Line-of-Sight (LoS) or if they are not. The interface
for that is represented by this class. The main method is GetChannelCondition (a, b), which returns a
ChannelCondition object containing the information about the channel state.
We modeled the LoS condition in two ways: (i) by using a probabilistic model specified by the 3GPP (),
and (ii) by using an ns-3 specific building-aware model, which checks the space position of the BSs and
the UTs. For what regards the first option, the probability is independent of the node location: in
other words, following the 3GPP model, two UT spatially separated by an epsilon may have different LoS
conditions. To take into account mobility, we have inserted a parameter called “UpdatePeriod,” which
indicates how often a 3GPP-based channel condition has to be updated. By default, this attribute is set
to 0, meaning that after the channel condition is generated, it is never updated. With this default
value, we encourage the users to run multiple simulations with different seeds to get statistical
significance from the data. For the users interested in using mobile nodes, we suggest changing this
parameter to a value that takes into account the node speed and the desired accuracy. For example,
lower-speed node conditions may be updated in terms of seconds, while high-speed UT or BS may be updated
more often.
The two approach are coded, respectively, in the classes:
• ThreeGppChannelConditionModel
• BuildingsChannelConditionModel (see the building module documentation for further details)
ThreeGppChannelConditionModel
This is the base class for the 3GPP channel condition models. It provides the possibility to updated the
condition of each channel periodically, after a given time period which can be configured through the
attribute “UpdatePeriod”. If “UpdatePeriod” is set to 0, the channel condition is never updated. It has
five derived classes implementing the channel condition models described in 3GPP TR 38.901 [38901] for
different propagation scenarios.
ThreeGppRmaChannelConditionModel
This implements the statistical channel condition model described in 3GPP TR 38.901 [38901], Table
7.4.2-1, for the RMa scenario.
ThreeGppUmaChannelConditionModel
This implements the statistical channel condition model described in 3GPP TR 38.901 [38901], Table
7.4.2-1, for the UMa scenario.
ThreeGppUmiStreetCanyonChannelConditionModel
This implements the statistical channel condition model described in 3GPP TR 38.901 [38901], Table
7.4.2-1, for the UMi-Street Canyon scenario.
ThreeGppIndoorMixedOfficeChannelConditionModel
This implements the statistical channel condition model described in 3GPP TR 38.901 [38901], Table
7.4.2-1, for the Indoor-Mixed office scenario.
ThreeGppIndoorOpenOfficeChannelConditionModel
This implements the statistical channel condition model described in 3GPP TR 38.901 [38901], Table
7.4.2-1, for the Indoor-Open office scenario.
Testing
The test suite ChannelConditionModelsTestSuite contains a single test case:
• ThreeGppChannelConditionModelTestCase, which tests all the 3GPP channel condition models. It determines
the channel condition between two nodes multiple times, estimates the LOS probability, and compares it
with the value given by the formulas in 3GPP TR 38.901 [38901], Table 7.4.2-1
PropagationDelayModel
The following propagation delay models are implemented:
• ConstantSpeedPropagationDelayModel
• RandomPropagationDelayModel
ConstantSpeedPropagationDelayModel
In this model, the signal travels with constant speed. The delay is calculated according with the
transmitter and receiver positions. The Euclidean distance between the Tx and Rx antennas is used.
Beware that, according to this model, the Earth is flat.
RandomPropagationDelayModel
The propagation delay is totally random, and it changes each time the model is called. All the packets
(even those between two fixed nodes) experience a random delay. As a consequence, the packets order is
not preserved.
Models for vehicular environments
The 3GPP TR 37.885 [37885] specifications extends the channel modeling framework described in TR 38.901
[38901] to simulate wireless channels in vehicular environments. The extended framework supports
frequencies between 0.5 to 100 GHz and provides the possibility to simulate urban and highway propagation
environments. To do so, new propagation loss and channel condition models, as well as new parameters for
the fast fading model, are provided.
Vehicular channel condition models
To properly capture channel dynamics in vehicular environments, three different channel conditions have
been identified:
• LOS (Line Of Sight): represents the case in which the direct path between the transmitter and the
receiver is not blocked
• NLOSv (Non Line Of Sight vehicle): when the direct path between the transmitter and the receiver is
blocked by a vehicle
• NLOS (Non Line Of Sight): when the direct path is blocked by a building
TR 37.885 includes two models that can be used to determine the condition of the wireless channel between
a pair of nodes, the first for urban and the second for highway environments. Each model includes both a
deterministic and a stochastic part, and works as follows:
1. The model determines the presence of buildings obstructing the direct path between the
communicating nodes. This is done in a deterministic way, looking at the possible interceptions
between the direct path and the buildings. If the path is obstructed, the channel condition is set
to NLOS.
2. If not, the model determines the presence of vehicles obstructing the direct path. This is done
using a probabilistic model, which is specific for the scenario of interest. If the path is
obstructed, the channel condition is set to NLOSv, otherwise is set to LOS.
These models have been implemented by extending the interface ChannelConditionModel with the following
classes. They have been included in the building module, because they make use of Buildings objects to
determine the presence of obstructions caused by buildings.
• ThreeGppV2vUrbanChannelConditionModel: implements the model described in Table 6.2-1 of TR 37.885
for the urban scenario.
• ThreeGppV2vHighwayChannelConditionModel: implements the model described in Table 6.2-1 of TR 37.885
for the highway scenario.
These models rely on Buildings objects to determine the presence of obstructing buildings. When
considering large scenarios with a large number of buildings, this process may become computationally
demanding and dramatically increase the simulation time. To solve this problem, we implemented two
fully-probabilistic models that can be used as an alternative to the ones included in TR 37.885. These
models are based on the work carried out by M. Boban et al. [Boban2016Modeling], which derived a
statistical representation of the three channel conditions, With the fully-probabilistic models there is
no need to determine the presence of blocking buildings in a deterministic way, and therefore the
computational effort is reduced. To determine the channel condition, these models account for the
propagation environment, i.e., urban or highway, as well as for the density of vehicles in the scenario,
which can be high, medium, or low.
The classes implementing the fully-probabilistic models are:
• ProbabilisticV2vUrbanChannelConditionModel: implements the model described in [Boban2016Modeling]
for the urban scenario.
• ProbabilisticV2vHighwayChannelConditionModel: implements the model described in [Boban2016Modeling]
for the highway scenario.
Both the classes own the attribute “Density”, which can be used to select the proper value depending on
the scenario that have to be simulated. Differently from the hybrid models described above, these
classes have been included in the propagation module, since they do not have any dependency on the
building module.
NOTE: Both the hybrid and the fully-probabilistic models supports the modeling of outdoor scenarios, no
support is provided for the modeling of indoor scenarios.
Vehicular propagation loss models
The propagation models described in TR 37.885 determines the attenuation caused by path loss and
shadowing by considering the propagation environment and the channel condition.
These models have been implemented by extending the interface ThreeGppPropagationLossModel with the
following classes, which are part of the propagation module:
• ThreeGppV2vUrbanPropagationLossModel: implements the models defined in Table 6.2.1-1 of TR 37.885
for the urban scenario.
• ThreeGppV2vHighwayPropagationLossModel: implements the models defined in Table 6.2.1-1 of TR 37.885
for the highway scenario.
As for all the classes extending the interface ThreeGppPropagationLossModel, they have to be paired with
an instance of the class ChannelConditionModel which is used to determine the channel condition. This is
done by setting the attribute ChannelConditionModel. To build the channel modeling framework described
in TR 37.885, ThreeGppV2vUrbanChannelConditionModel or ThreeGppV2vHighwayChannelConditionModel should be
used, but users are allowed to test any other combination.
Vehicular fast fading model
The fast fading model described in Sec. 6.2.3 of TR 37.885 is based on the one specified in TR 38.901,
whose implementation is provided in the spectrum module (see the spectrum module documentation). This
model is general and includes different parameters which can be tuned to simulate multiple propagation
environments. To better model the channel dynamics in vehicular environments, TR 37.885 provides new
sets of values for these parameters, specific for vehicle-to-vehicle transmissions in urban and highway
scenarios. To select the parameters for vehicular scenarios, it is necessary to set the attribute
“Scenario” of the class ThreeGppChannelModel using the value “V2V-Urban” or “V2V-Highway”.
Additionally, TR 37.885 specifies a new equation to compute the Doppler component, which accounts for the
mobility of both nodes, as well as scattering from the environment. In particular, the scattering effect
is considered by deviating the Doppler frequency by a random value, whose distribution depends on the
parameter v_{scatt}. TR 37.885 specifies that v_{scatt} should be set to the maximum speed of the
vehicles in the layout and, if v_{scatt} = 0, the scattering effect is not considered. The Doppler
equation is implemented in the class ThreeGppSpectrumPropagationLossModel. By means of the attribute
“vScatt”, it is possible to adjust the value of v_{scatt} = 0 (by default, the value is set to 0).
Example
We implemented the example three-gpp-v2v-channel-example.cc which shows how to configure the different
classes to simulate wireless propagation in vehicular scenarios, it can be found in the folder
examples/channel-models.
We considered two communicating vehicles moving within the scenario, and computed the SNR experienced
during the entire simulation, with a time resolution of 10 ms. The vehicles are equipped with 2x2
antenna arrays modeled using the 3GPP antenna model. The bearing and the downtilt angles are properly
configured and the optimal beamforming vectors are computed at the beginning of the simulation.
The simulation script accepts the following command line parameters:
• frequency: the operating frequency in Hz
• txPow: the transmission power in dBm
• noiseFigure: the noise figure in dB
• scenario: the simulation scenario, “V2V-Urban” or “V2V-Highway”
The “V2V-Urban” scenario simulates urban environment with a rectangular grid of buildings. The vehicles
moves with a waypoint mobility model. They start from the same position and travel in the same direction,
along the main street. The first vehicle moves at 60 km/h and the second at 30 km/h. At a certain
point, the first vehicle turns left while the second continues on the main street.
The “V2V-Highway” scenario simulates an highway environment in which the two vehicles travel on the same
lane, in the same direction, and keep a safety distance of 20 m. They maintain a constant speed of 140
km/h.
The example generates the output file example-output.txt. Each row of the file is organized as follows:
Time[s] TxPosX[m] TxPosY[m] RxPosX[m] RxPosY[m] ChannelState SNR[dB] Pathloss[dB]
We also provide the bash script three-gpp-v2v-channel-example.sh which reads the output file and
generates two figures:
1. map.gif, a GIF representing the simulation scenario and vehicle mobility;
2. snr.png, which represents the behavior of the SNR.
References
[friis]
Friis, H.T., “A Note on a Simple Transmission Formula,” Proceedings of the IRE , vol.34, no.5,
pp.254,256, May 1946
[hata]
M.Hata, “Empirical formula for propagation loss in land mobile radio services”, IEEE Trans. on
Vehicular Technology, vol. 29, pp. 317-325, 1980
[cost231]
“Digital Mobile Radio: COST 231 View on the Evolution Towards 3rd Generation Systems”, Commission of
the European Communities, L-2920, Luxembourg, 1989
[walfisch]
J.Walfisch and H.L. Bertoni, “A Theoretical model of UHF propagation in urban environments,” in IEEE
Trans. Antennas Propagat., vol.36, 1988, pp.1788- 1796
[ikegami]
F.Ikegami, T.Takeuchi, and S.Yoshida, “Theoretical prediction of mean field strength for Urban
Mobile Radio”, in IEEE Trans. Antennas Propagat., Vol.39, No.3, 1991
[kun2600mhz]
Sun Kun, Wang Ping, Li Yingze, “Path loss models for suburban scenario at 2.3GHz, 2.6GHz and
3.5GHz”, in Proc. of the 8th International Symposium on Antennas, Propagation and EM Theory (ISAPE),
Kunming, China, Nov 2008.
[38901]
3GPP. 2018. TR 38.901, Study on channel model for frequencies from 0.5 to 100 GHz, V15.0.0.
(2018-06).
[37885]
3GPP. 2019. TR 37.885, Study on evaluation methodology of new Vehicle-to-Everything (V2X) use cases
for LTE and NR, V15.3.0. (2019-06).
[Boban2016Modeling]
M. Boban, X. Gong, and W. Xu, “Modeling the evolution of line-of-sight blockage for V2V channels,”
in IEEE 84th Vehicular Technology Conference (VTC-Fall), 2016.
SPECTRUM MODULE
The Spectrum module aims at providing support for modeling the frequency-dependent aspects of
communications in ns-3. The model was first introduced in [Baldo2009Spectrum], and has been enhanced and
refined over the years.
[image] Spectrogram produced by a spectrum analyzer in a scenario involving wifi signals interfered by
a microwave oven, as simulated by the example adhoc-aloha-ideal-phy-with-microwave-oven..UNINDENT
Model Description
The module provides:
• a set of classes for modeling signals and
• a Channel/PHY interface based on a power spectral density signal representation that is
technology-independent
• two technology-independent Channel implementations based on the Channel/PHY interface
• a set of basic PHY model implementations based on the Channel/PHY interface
The source code for the spectrum module is located at src/spectrum.
Design
Signal model
The signal model is implemented by the SpectrumSignalParameters class. This class provides the following
information for a signal being transmitted/received by PHY devices:
• a reference to the transmitting PHY device
• a reference to the antenna model used by the transmitting PHY device to transmit this signal
• the duration of the signal
• its Power Spectral Density (PSD) of the signal, which is assumed to be constant for the duration of the
signal.
The PSD is represented as a set of discrete scalar values each corresponding to a certain subband in
frequency. The set of frequency subbands to which the PSD refers to is defined by an instance of the
SpectrumModel class. The PSD itself is implemented as an instance of the SpectrumValue class which
contains a reference to the associated SpectrumModel class instance. The SpectrumValue class provides
several arithmetic operators to allow to perform calculations with PSD instances. Additionally, the
SpectrumConverter class provides means for the conversion of SpectrumValue instances from one
SpectrumModel to another.
For a more formal mathematical description of the signal model just described, the reader is referred to
[Baldo2009Spectrum].
The SpectrumSignalParameters class is meant to include only information that is valid for all signals; as
such, it is not meant to be modified to add technology-specific information (such as type of modulation
and coding schemes used, info on preambles and reference signals, etc). Instead, such information shall
be put in a new class that inherits from SpectrumSignalParameters and extends it with any
technology-specific information that is needed. This design is intended to model the fact that in the
real world we have signals of different technologies being simultaneously transmitted and received over
the air.
Channel/PHY interface
The spectrum Channel/PHY interface is defined by the base classes SpectrumChannel and SpectrumPhy. Their
interaction simulates the transmission and reception of signals over the medium. The way this interaction
works is depicted in Sequence diagram showing the interaction between SpectrumPhy and SpectrumChannel:
[image] Sequence diagram showing the interaction between SpectrumPhy and SpectrumChannel.UNINDENT
Spectrum Channel implementations
The module provides two SpectrumChannel implementations: SingleModelSpectrumChannel and
MultiModelSpectrumChannel. They both provide this functionality:
• Propagation loss modeling, in two forms:
• you can plug models based on PropagationLossModel on these channels. Only linear models (where the
loss value does not depend on the transmission power) can be used. These models are
single-frequency in the sense that the loss value is applied equally to all components of the
power spectral density.
• you can plug models based on SpectrumPropagationLossModel on these channels. These models can have
frequency-dependent loss, i.e., a separate loss value is calculated and applied to each component
of the power spectral density.
• Propagation delay modeling, by plugging a model based on PropagationDelayModel. The delay is
independent of frequency and applied to the signal as a whole. Delay modeling is implemented by
scheduling the StartRx event with a delay respect to the StartTx event.
SingleModelSpectrumChannel and MultiModelSpectrumChannel are quite similar, the main difference is that
MultiModelSpectrumChannel allows to use different SpectrumModel instances with the same channel instance,
by automatically taking care of the conversion of PSDs among the different models.
Example model implementations
The spectrum module provides some basic implementation of several components that are mainly intended as
a proof-of-concept and as an example for building custom models with the spectrum module. Here is a brief
list of the available implementations:
• SpectrumModel300Khz300GhzLog and SpectrumModelIsm2400MhzRes1Mhz are two example SpectrumModel
implementations
• HalfDuplexIdealPhy: a basic PHY model using a gaussian interference model (implemented in
SpectrumInterference) together with an error model based on Shannon capacity (described in
[Baldo2009Spectrum] and implemented in SpectrumErrorModel. This PHY uses the GenericPhy interface.
Its addditional custom signal parameters are defined in HalfDuplexIdealPhySignalParameters.
• WifiSpectrumValueHelper is an helper object that makes it easy to create SpectrumValues representing
PSDs and RF filters for the wifi technology.
• AlohaNoackNetDevice: a minimal NetDevice that allows to send packets over HalfDuplexIdealPhy (or
other PHY model based on the GenericPhy interface).
• SpectrumAnalyzer, WaveformGenerator and MicrowaveOven are examples of PHY models other than
communication devices - the names should be self-explaining.
References
[Baldo2009Spectrum]
N. Baldo and M. Miozzo, “Spectrum-aware Channel and PHY layer modeling for ns3”, Proceedings of ICST
NSTools 2009, Pisa, Italy
Usage
The main use case of the spectrum model is for developers who want to develop a new model for the PHY
layer of some wireless technology to be used within ns-3. Here are some notes on how the spectrum module
is expected to be used.
• SpectrumPhy and SpectrumChannel are abstract base classes. Real code will use classes that inherit
from these classes.
• If you are implementing a new model for some wireless technology of your interest, and want to use
the spectrum module, you’ll typically create your own module and make it depend on the spectrum
module. Then you typically have to implement:
• a child class of SpectrumModel which defines the (sets of) frequency subbands used by the
considered wireless technology. Note: instances of SpectrumModel are typically statically
allocated, in order to allow several SpectrumValue instances to reference the same
SpectrumModel instance.
• a child class of SpectrumPhy which will handle transmission and reception of signals
(including, if appropriate, interference and error modeling).
• a child class of SpectrumSignalParameters which will contain all the information needed to
model the signals for the wireless technology being considered that is not already provided by
the base SpectrumSignalParameters class. Examples of such information are the type of
modulation and coding schemes used, the PHY preamble format, info on the pilot/reference
signals, etc.
• The available SpectrumChannel implementations (SingleModelSpectrumChannel and
MultiModelSpectrumChannel, are quite generic. Chances are you can use them as-is. Whether you prefer
one or the other it is just a matter of whether you will have a single SpectrumModel or multiple
ones in your simulations.
• Typically, there will be a single SpectrumChannel instance to which several SpectrumPhy instances
are plugged. The rule of thumb is that all PHYs that are interfering with each other shall be
plugged on the same channel. Multiple SpectrumChannel instances are expected to be used mainly when
simulating completely orthogonal channels; for example, when simulating the uplink and downlink of a
Frequency Division Duplex system, it is a good choice to use two SpectrumChannel instances in order
to reduce computational complexity.
• Different types of SpectrumPhy (i.e., instances of different child classes) can be plugged on the
same SpectrumChannel instance. This is one of the main features of the spectrum module, to support
inter-technology interference. For example, if you implement a WifiSpectrumPhy and a
BluetoohSpectrumPhy, and plug both on a SpectrumChannel, then you’ll be able to simulate
interference between wifi and bluetooth and vice versa.
• Different child classes of SpectrumSignalParameters can coexist in the same simulation, and be
transmitted over the same channel object. Again, this is part of the support for inter-technology
interference. A PHY device model is expected to use the DynamicCast<> operator to determine if a
signal is of a certain type it can attempt to receive. If not, the signal is normally expected to be
considered as interference.
Helpers
The helpers provided in src/spectrum/helpers are mainly intended for the example implementations
described in Example model implementations. If you are developing your custom model based on the
spectrum framework, you will probably prefer to define your own helpers.
Attributes
• Both SingleModelSpectrumChannel and MultiModelSpectrumChannel have an attribute MaxLossDb which can
use to avoid propagating signals affected by very high propagation loss. You can use this to reduce
the complexity of interference calculations. Just be careful to choose a value that does not make
the interference calculations inaccurate.
• The example implementations described in Example model implementations also have several attributes.
Output
• Both SingleModelSpectrumChannel and MultiModelSpectrumChannel provide a trace source called PathLoss
which is fired whenever a new path loss value is calclulated. Note: only single-frequency path loss
is accounted for, see the attribute description.
• The example implementations described in Example model implementations also provide some trace
sources.
• The helper class SpectrumAnalyzerHelper can be conveniently used to generate an output text file
containing the spectrogram produced by a SpectrumAnalyzer instance. The format is designed to be
easily plotted with gnuplot. For example, if your run the example
adhoc-aloha-ideal-phy-with-microwave-oven you will get an output file called
spectrum-analyzer-output-3-0.tr. From this output file, you can generate a figure similar to
Spectrogram produced by a spectrum analyzer in a scenario involving wifi signals interfered by a
microwave oven, as simulated by the example adhoc-aloha-ideal-phy-with-microwave-oven. by executing
the following gnuplot commands:
unset surface
set pm3d at s
set palette
set key off
set view 50,50
set xlabel "time (ms)"
set ylabel "freq (MHz)"
set zlabel "PSD (dBW/Hz)" offset 15,0,0
splot "./spectrum-analyzer-output-3-0.tr" using ($1*1000.0):($2/1e6):(10*log10($3))
Examples
The example programs in src/spectrum/examples/ allow to see the example implementations described in
Example model implementations in action.
Troubleshooting
• Disclaimer on inter-technology interference: the spectrum model makes it very easy to implement an
inter-technology interference model, but this does not guarantee that the resulting model is
accurate. For example, the gaussian interference model implemented in the SpectrumInterference class
can be used to calculate inter-technology interference, however the results might not be valid in
some scenarios, depending on the actual waveforms involved, the number of interferers, etc.
Moreover, it is very important to use error models that are consistent with the interference model.
The responsibility of ensuring that the models being used are correct is left to the user.
Testing
In this section we describe the test suites that are provided within the spectrum module.
SpectrumValue test
The test suite spectrum-value verifies the correct functionality of the arithmetic operators implemented
by the SpectrumValue class. Each test case corresponds to a different operator. The test passes if the
result provided by the operator implementation is equal to the reference values which were calculated
offline by hand. Equality is verified within a tolerance of 10^{-6} which is to account for numerical
errors.
SpectrumConverter test
The test suite spectrum-converter verifies the correct functionality of the SpectrumConverter class.
Different test cases correspond to the conversion of different SpectrumValue instances to different
SpectrumModel instances. Each test passes if the SpectrumValue instance resulting from the conversion is
equal to the reference values which were calculated offline by hand. Equality is verified within a
tolerance of 10^{-6} which is to account for numerical errors.
Describe how the model has been tested/validated. What tests run in the test suite? How much API and
code is covered by the tests? Again, references to outside published work may help here.
Interference test
The test suite spectrum-interference verifies the correct functionality of the SpectrumInterference and
ShannonSpectrumErrorModel in a scenario involving four signals (an intended signal plus three
interferers). Different test cases are created corresponding to different PSDs of the intended signal and
different amount of transmitted bytes. The test passes if the output of the error model (successful or
failed) coincides with the expected one which was determine offline by manually calculating the
achievable rate using Shannon’s formula.
IdealPhy test
The test verifies that AlohaNoackNetDevice and HalfDuplexIdealPhy work properly when installed in a node.
The test recreates a scenario with two nodes (a TX and a RX) affected by a path loss such that a certain
SNR is obtained. The TX node transmits with a pre-determined PHY rate and with an application layer rate
which is larger than the PHY rate, so as to saturate the channel. PacketSocket is used in order to avoid
protocol overhead. Different test cases correspond to different PHY rate and SNR values. For each test
case, we calculated offline (using Shannon’s formula) whether the PHY rate is achievable or not. Each
test case passes if the following conditions are satisfied:
• if the PHY rate is achievable, the application throughput shall be within 1\% of the PHY rate;
• if the PHY rate is not achievable, the application throughput shall be zero.
Additional Models
TV Transmitter Model
A TV Transmitter model is implemented by the TvSpectrumTransmitter class. This model enables
transmission of realistic TV signals to be simulated and can be used for interference modeling. It
provides a customizable power spectral density (PSD) model, with configurable attributes including the
type of modulation (with models for analog, 8-VSB, and COFDM), signal bandwidth, power spectral density
level, frequency, and transmission duration. A helper class, TvSpectrumTransmitterHelper, is also
provided to assist users in setting up simulations.
Main Model Class
The main TV Transmitter model class, TvSpectrumTransmitter, provides a user-configurable PSD model that
can be transmitted on the SpectrumChannel. It inherits from SpectrumPhy and is comprised of attributes
and methods to create and transmit the signal on the channel.
[image] 8K COFDM signal spectrum generated from TvSpectrumTransmitter (Left) and theoretical COFDM
signal spectrum [KoppCOFDM] (Right).UNINDENT
One of the user-configurable attributes is the type of modulation for the TV transmitter to use. The
options are 8-VSB (Eight-Level Vestigial Sideband Modulation) which is notably used in the North
America ATSC digital television standard, COFDM (Coded Orthogonal Frequency Division Multiplexing)
which is notably used in the DVB-T and ISDB-T digital television standards adopted by various countries
around the world, and analog modulation which is a legacy technology but is still being used by some
countries today. To accomplish realistic PSD models for these modulation types, the signals’ PSDs were
approximated from real standards and developed into models that are scalable by frequency and power.
The COFDM PSD is approximated from Figure 12 (8k mode) of [KoppCOFDM], the 8-VSB PSD is approximated
from Figure 3 of [Baron8VSB], and the analog PSD is approximated from Figure 4 of [QualcommAnalog].
Note that the analog model is approximated from the NTSC standard, but other analog modulation
standards such as PAL have similar signals. The approximated COFDM PSD model is in 8K mode. The other
configurable attributes are the start frequency, signal/channel bandwidth, base PSD, antenna type,
starting time, and transmit duration.
TvSpectrumTransmitter uses IsotropicAntennaModel as its antenna model by default, but any model that
inherits from AntennaModel is selectable, so directional antenna models can also be used. The
propagation loss models used in simulation are configured in the SpectrumChannel that the user chooses
to use. Terrain and spherical Earth/horizon effects may be supported in future ns-3 propagation loss
models.
After the attributes are set, along with the SpectrumChannel, MobilityModel, and node locations, the
PSD of the TV transmitter signal can be created and transmitted on the channel.
Helper Class
The helper class, TvSpectrumTransmitterHelper, consists of features to assist users in setting up TV
transmitters for their simulations. Functionality is also provided to easily simulate real-world
scenarios.
[image] North America ATSC channel 19 & 20 signals generated using TvSpectrumTransmitterHelper (Left)
and theoretical 8-VSB signal [Baron8VSB] (Right). Note that the theoretical signal is not shown in dB
while the ns-3 generated signals are..UNINDENT
Using this helper class, users can easily set up TV transmitters right after configuring attributes.
Multiple transmitters can be created at a time. Also included are real characteristics of specific
geographic regions that can be used to run realistic simulations. The regions currently included are
North America, Europe, and Japan. The frequencies and bandwidth of each TV channel for each these
regions are provided.
[image] Plot from MATLAB implementation of CreateRegionalTvTransmitters method in
TvSpectrumTransmitterHelper. Shows 100 random points on Earth’s surface (with altitude 0) corresponding
to TV transmitter locations within a 2000 km radius of 35° latitude and -100° longitude..UNINDENT
A method (CreateRegionalTvTransmitters) is provided that enables users to randomly generate multiple TV
transmitters from a specified region with a given density within a chosen radius around a point on
Earth’s surface. The region, which determines the channel frequencies of the generated TV transmitters,
can be specified to be one of the three provided, while the density determines the amount of
transmitters generated. The TV transmitters’ antenna heights (altitude) above Earth’s surface can also
be randomly generated to be within a given maximum altitude. This method models Earth as a perfect
sphere, and generated location points are referenced accordingly in Earth-Centered Earth-Fixed
Cartesian coordinates. Note that bodies of water on Earth are not considered in location point
generation–TV transmitters can be generated anywhere on Earth around the origin point within the chosen
maximum radius.
Examples
Two example simulations are provided that demonstrate the functionality of the TV transmitter model.
tv-trans-example simulates two 8-VSB TV transmitters with adjacent channel frequencies.
tv-trans-regional-example simulates randomly generated COFDM TV transmitters (modeling the DVB-T
standard) located around the Paris, France area with channel frequencies and bandwidths corresponding to
the European television channel allocations.
Testing
The tv-spectrum-transmitter test suite verifies the accuracy of the spectrum/PSD model in
TvSpectrumTransmitter by testing if the maximum power spectral density, start frequency, and end
frequency comply with expected values for various test cases.
The tv-helper-distribution test suite verifies the functionality of the method in
TvSpectrumTransmitterHelper that generates a random number of TV transmitters based on the given density
(low, medium, or high) and maximum number of TV channels. It verifies that the number of TV transmitters
generated does not exceed the expected bounds.
The CreateRegionalTvTransmitters method in TvSpectrumTransmitterHelper described in Helper Class uses two
methods from the GeographicPositions class in the Mobility module to generate the random Cartesian points
on or above earth’s surface around an origin point which correspond to TV transmitter positions. The
first method converts Earth geographic coordinates to Earth-Centered Earth-Fixed (ECEF) Cartesian
coordinates, and is tested in the geo-to-cartesian test suite by comparing (with 10 meter tolerance) its
output with the output of the geographic to ECEF conversion function [MatlabGeo] of the MATLAB Mapping
Toolbox for numerous test cases. The other used method generates random ECEF Cartesian points around the
given geographic origin point, and is tested in the rand-cart-around-geo test suite by verifying that the
generated points do not exceed the given maximum distance radius from the origin point.
3GPP TR 38.901 fast fading model
The framework described by TR 38.901 [TR38901] is a 3D statistical Spatial Channel Model supporting
different propagation environments (e.g., urban, rural, indoor), multi-antenna operations and the
modeling of wireless channels between 0.5 and 100 GHz. The overall channel is represented by the matrix
H(t,\tau), in which each entry H u,s (t,\tau) corresponds to the impulse response of the channel between
the s-th element of the transmitting antenna and the u-th element of the receiving antenna. H u,s
(t,\tau) is generated by the superposition of N different multi-path components, called clusters, each of
which composed of M different rays. The channel matrix generation procedure accounts for large and small
scale propagation phenomena. The classes ThreeGppSpectrumPropagationLossModel and ThreeGppChannelModel
included in the spectrum module takes care of the generation of the channel coefficients and the
computation of the frequency-dependent propagation loss.
Implementation
Our implementation is described in [Zugno]. It is based on the model described in [Zhang], but the code
has been refactored, extended, and aligned to TR 38.901 [TR38901]. The fundamental assumption behind
this model is the channel reciprocity, i.e., the impulse response of the channel between node a and node
b is the same as between node b and node a. To deal with the equivalence of the channel between a and b,
no matter who is the transmitter and who is the receiver, the model considers the pair of nodes to be
composed by one “s” and one “u” node. The channel matrix, as well as other parameters, are saved and used
under the assumption that, within a pair, the definition of the “s” and “u” node will always be the same.
For more details, please have a look at the documentation of the classes ThreeGppChannelModel and
ThreeGppSpectrumPropagationLossModel.
Note:
• Currently, no error model is provided; a link-to-system campaign may be needed to incorporate it in
existing modules.
• The model does not include any spatial consistency update procedure (see [TR38901], Sec. 7.6.1). The
implementation of this feature is left as future work.
• Issue regarding the blockage model: according to 3GPP TR 38.901 v15.0.0 (2018-06) section 7.6.4.1,
the blocking region for self-blocking is provided in LCS.
However, here, clusterAOA and clusterZOA are in GCS and blocking check is performed for
self-blocking similar to non-self blocking, that is in GCS. One would expect the angles to be
transposed to LCS before checking self-blockage.
ThreeGppSpectrumPropagationLossModel
The class ThreeGppSpectrumPropagationLossModel extends the SpectrumPropagationLossModel interface and
enables the modeling of frequency dependent propagation phenomena. The main method is
DoCalcRxPowerSpectralDensity, which takes as input the power spectral density (PSD) of the transmitted
signal, the mobility models of the transmitting node and receiving node, and returns the PSD of the
received signal.
Procedure used to compute the PSD of to compute the PSD of the received signal:
1. Retrieve the beamforming vectors To account for the beamforming, ThreeGppSpectrumPropagationLossModel
has to retrieve the beamforming vectors of the transmitting and receiving antennas. The method
DoCalcRxPowerSpectralDensity uses m_deviceAntennaMap to obtain the antenna objects associated to the
transmitting and receiving devices, and calls the method GetCurrentBeamformingVector to retrieve the
beamforming vectors. For each device using the channel, the m_deviceAntennaMap contains the associated
antenna object of type PhasedArrayModel. Since the mapping is one-to-one, the model supports a single
antenna object for each device. The m_deviceAntennaMap has to be initialized by inserting the
device-antenna pairs using the method AddDevice.
2. Retrieve the channel matrix The ThreeGppSpectrumPropagationLossModel relies on the
ThreeGppChannelModel class to obtain the channel matrix. In particular, it makes use of the method
GetChannel, which returns a ChannelMatrix object containing the channel matrix and other channel
parameters. The ThreeGppChannelModel instance is automatically created in the the
ThreeGppSpectrumPropagationLossModel constructor and it can be configured using the method
SetChannelModelAttribute ().
4. Compute the long term component The method GetLongTerm returns the long term component obtained by
multiplying the channel matrix and the beamforming vectors. To reduce the computational load, the long
term components associated to the different channels are stored in the m_longTermMap and recomputed only
if the associated channel matrix is updated or if the transmitting and/or receiving beamforming vectors
have changed. Given the channel reciprocity assumption, for each node pair a single long term component
is saved in the map.
5. Apply the small scale fading and compute the channel gain The method CalcBeamformingGain computes the
channel gain in each sub-band and applies it to the PSD of the transmitted signal to obtain the received
PSD. To compute the sub-band gain, it accounts for the Doppler phenomenon and the time dispersion effect
on each cluster. In order to reduce the computational load, the Doppler component of each cluster is
computed considering only the central ray. Also, as specified here, it is possible to account for the
effect of environmental scattering following the model described in Sec. 6.2.3 of 3GPP TR 37.885. This
is done by deviating the Doppler frequency by a random value, whose distribution depends on the parameter
v_{scatt}. The value of v_{scatt} can be configured using the attribute “vScatt” (by default it is set
to 0, so that the scattering effect is not considered).
ThreeGppChannelModel
The class ThreeGppChannelModel implements the channel matrix generation procedure described in Sec. of
[TR38901]. The main method is GetChannel, which takes as input the mobility models of the transmitter
and receiver nodes, the associated antenna objects, and returns a ChannelMatrix object containing:
• the channel matrix of size UxSxN, where U is the number of receiving antenna elements, S is the number
of transmitting antenna elements and N is the number of clusters
• the clusters delays, as an array of size N
• the clusters arrival and departure angles, as a 2D array in which each row corresponds to a direction
(AOA, ZOA, AOD, ZOD) and each column corresponds to a different cluster
• a time stamp indicating the time at which the channel matrix was generated
• the node IDs
• other channel parameters
The ChannelMatrix objects are saved in the map m_channelMap and updated when the coherence time expires,
or in case the LOS/NLOS channel condition changes. The coherence time can be configured through the
attribute “UpdatePeriod”, and should be chosen by taking into account all the factors that affects the
channel variability, such as mobility, frequency, propagation scenario, etc. By default, it is set to 0,
which means that the channel is recomputed only when the LOS/NLOS condition changes. It is possible to
configure the propagation scenario and the operating frequency of interest through the attributes
“Scenario” and “Frequency”, respectively.
Blockage model: 3GPP TR 38.901 also provides an optional feature that can be used to model the blockage
effect due to the presence of obstacles, such as trees, cars or humans, at the level of a single cluster.
This differs from a complete blockage, which would result in an LOS to NLOS transition. Therefore, when
this feature is enabled, an additional attenuation is added to certain clusters, depending on their angle
of arrival. There are two possi- ble methods for the computation of the additional attenuation, i.e.,
stochastic (Model A) and geometric (Model B). In this work, we used the implementation provided by
[Zhang], which uses the stochastic method. In particular, the model is implemented by the method
CalcAttenuationOfBlockage, which computes the additional attenuation. The blockage feature can be
disable through the attribute “Blockage”. Also, the attributes “NumNonselfBlocking”, “PortraitMode” and
“BlockerSpeed” can be used to configure the model.
Testing
The test suite ThreeGppChannelTestSuite includes three test cases:
• ThreeGppChannelMatrixComputationTest checks if the channel matrix has the correct dimensions and if it
correctly normalized
• ThreeGppChannelMatrixUpdateTest, which checks if the channel matrix is correctly updated when the
coherence time exceeds
• ThreeGppSpectrumPropagationLossModelTest, which tests the functionalities of the class
ThreeGppSpectrumPropagationLossModel. It builds a simple network composed of two nodes, computes the
power spectral density received by the receiving node, and
1. Checks if the long term components for the direct and the reverse link are the same,
2. Checks if the long term component is updated when changing the beamforming vectors,
3. Checks if the long term is updated when changing the channel matrix
Note: TR 38.901 includes a calibration procedure that can be used to validate the model, but it requires
some additional features which are not currently implemented, thus is left as future work.
References
[Baron8VSB]
Baron, Stanley. “First-Hand:Digital Television: The Digital Terrestrial Television Broadcasting
(DTTB) Standard.” IEEE Global History Network.
<http://www.ieeeghn.org/wiki/index.php/First-Hand:Digital_Television:_The_Digital_Terrestrial_Television_Broadcasting_(DTTB)_Standard>.
[KoppCOFDM]
Kopp, Carlo. “High Definition Television.” High Definition Television. Air Power Australia. <‐
http://www.ausairpower.net/AC-1100.html>.
[MatlabGeo]
“Geodetic2ecef.” Convert Geodetic to Geocentric (ECEF) Coordinates. The MathWorks, Inc. <‐
http://www.mathworks.com/help/map/ref/geodetic2ecef.html>.
[QualcommAnalog]
Stephen Shellhammer, Ahmed Sadek, and Wenyi Zhang. “Technical Challenges for Cognitive Radio in the
TV White Space Spectrum.” Qualcomm Incorporated.
[TR38901]
3GPP. 2018. TR 38.901. Study on channel for frequencies from 0.5 to 100 GHz. V.15.0.0. (2018-06).
[Zhang]
Menglei Zhang, Michele Polese, Marco Mezzavilla, Sundeep Rangan, Michele Zorzi. “ns-3 Implementation
of the 3GPP MIMO Channel Model for Frequency Spectrum above 6 GHz”. In Proceedings of the Workshop
on ns-3 (WNS3 ‘17). 2017.
[Zugno]
Tommaso Zugno, Michele Polese, Natale Patriciello, Biljana Bojovic, Sandra Lagen, Michele Zorzi.
“Implementation of a Spatial Channel Model for ns-3”. Submitted to the Workshop on ns-3 (WNS3 ‘20).
2020. Available: https://arxiv.org/abs/2002.09341
6LOWPAN: TRANSMISSION OF IPV6 PACKETS OVER IEEE 802.15.4 NETWORKS
This chapter describes the implementation of ns-3 model for the compression of IPv6 packets over IEEE
802.15.4-Based Networks as specified by RFC 4944 (“Transmission of IPv6 Packets over IEEE 802.15.4
Networks”) and RFC 6282 (“Compression Format for IPv6 Datagrams over IEEE 802.15.4-Based Networks”).
Model Description
The source code for the sixlowpan module lives in the directory src/sixlowpan.
Design
The model design does not follow strictly the standard from an architectural standpoint, as it does
extend it beyond the original scope by supporting also other kinds of networks.
Other than that, the module strictly follows RFC 4944 and RFC 6282, with the exception that HC2 encoding
is not supported, as it has been superseded by IPHC and NHC compression type (RFC 6282).
IPHC sateful (context-based) compression is supported but, since RFC 6775 (“Neighbor Discovery
Optimization for IPv6 over Low-Power Wireless Personal Area Networks (6LoWPANs)”) is not yet implemented,
it is necessary to add the context to the nodes manually.
This is possible though the SixLowPanHelper::AddContext function. Mind that installing different
contexts in different nodes will lead to decompression failures.
NetDevice
The whole module is developed as a transparent NetDevice, which can act as a proxy between IPv6 and any
NetDevice (the module has been successfully tested with PointToPointNedevice, CsmaNetDevice and
LrWpanNetDevice).
For this reason, the module implements a virtual NetDevice, and all the calls are passed without
modifications to the underlying NetDevice. The only important difference is in GetMtu behaviour. It will
always return at least 1280 bytes, as is the minimum IPv6 MTU.
The module does provide some attributes and some tracesources. The attributes are:
• Rfc6282 (boolean, default true), used to activate HC1 (RFC 4944) or IPHC (RFC 6282) compression.
• OmitUdpChecksum (boolean, default true), used to activate UDP checksum compression in IPHC.
• FragmentReassemblyListSize (integer, default 0), indicating the number of packets that can be
reassembled at the same time. If the limit is reached, the oldest packet is discarded. Zero means
infinite.
• FragmentExpirationTimeout (Time, default 60 seconds), being the timeout to wait for further fragments
before discarding a partial packet.
• CompressionThreshold (unsigned 32 bits integer, default 0), minimum compressed payload size.
• ForceEtherType (boolean, default false).
• EtherType (unsigned 16 bits integer, default 0xFFFF), to force a particular L2 EtherType.
• UseMeshUnder (boolean, default false), it enables mesh-under flood routing.
• MeshUnderRadius (unsigned 8 bits integer, default 10), the maximum number of hops that a packet will be
forwarded.
• MeshCacheLength (unsigned 16 bits integer, default 10), the length of the cache for each source.
• MeshUnderJitter (ns3::UniformRandomVariable[Min=0.0|Max=10.0]), the jitter in ms a node uses to forward
mesh-under packets - used to prevent collisions.
The CompressionThreshold attribute is similar to Contiki’s SICSLOWPAN_CONF_MIN_MAC_PAYLOAD option. If a
compressed packet size is less than the threshold, the uncompressed version is used (plus one byte for
the correct dispatch header). This option is useful when a MAC requires a minimum frame size (e.g.,
ContikiMAC) and the compression would violate the requirement.
The last two attributes are needed to use the module with a NetDevice other than 802.15.4, as neither
IANA or IEEE did reserve an EtherType for 6LoWPAN. As a consequence there might be a conflict with the L2
multiplexer/demultiplexer which is based on EtherType. The default value is 0xFFFF, which is reserved by
IEEE (see [IANA802] and [Ethertype]). The default module behaviour is to not change the EtherType,
however this would not work with any NetDevice actually understanding and using the EtherType.
Note that the ForceEtherType parameter have also a direct effect on the MAC address kind the module is
expecting to handle: * ForceEtherType true: Mac48Address (Ethernet, WiFi, etc.). * ForceEtherType false:
Mac16Address or Mac64Address (IEEE 802.15.4).
Note that using 6LoWPAN over any NetDevice other than 802.15.4 will produce valid .pcap files, but they
will not be correctly dissected by Wireshark. The reason lies on the fact that 6LoWPAN was really meant
to be used only over 802.15.4, so Wireshark dissectors will not even try to decode 6LoWPAN headers on top
of protocols other than 802.15.4.
The Trace sources are:
• Tx - exposing packet (including 6LoWPAN header), SixLoWPanNetDevice Ptr, interface index.
• Rx - exposing packet (including 6LoWPAN header), SixLoWPanNetDevice Ptr, interface index.
• Drop - exposing DropReason, packet (including 6LoWPAN header), SixLoWPanNetDevice Ptr, interface index.
The Tx and Rx traces are called as soon as a packet is received or sent. The Drop trace is invoked when a
packet (or a fragment) is discarded.
Mesh-Under routing
The module provides a very simple mesh-under routing [Shelby], implemented as a flooding (a mesh-under
routing protocol is a routing system implemented below IP).
This functionality can be activated through the UseMeshUnder attribute and fine-tuned using the
MeshUnderRadius and MeshUnderJitter attributes.
Note that flooding in a PAN generates a lot of overhead, which is often not wanted. Moreover, when using
the mesh-under facility, ALL the packets are sent without acknowledgment because, at lower level, they
are sent to a broadcast address.
At node level, each packet is re-broadcasted if its BC0 Sequence Number is not in the cache of the
recently seen packets. The cache length (by default 10) can be changed through the MeshCacheLength
attribute.
Scope and Limitations
Contex-based compression
IPHC sateful (context-based) compression is supported but, since RFC 6775 (“Neighbor Discovery
Optimization for IPv6 over Low-Power Wireless Personal Area Networks (6LoWPANs)”) is not yet implemented,
it is necessary to add the context to the nodes manually.
6LoWPAM-ND
Future versions of this module will support RFC 6775, however no timeframe is guaranteed.
Mesh-under routing
It would be a good idea to improve the mesh-under flooding by providing the following:
• Adaptive hop-limit calculation,
• Adaptive forwarding jitter,
• Use of direct (non mesh) transmission for packets directed to 1-hop neighbors.
Mixing compression types in a PAN
The IPv6/MAC addressing scheme defined in RFC 6282 and RFC 4944 is different. One adds the PanId in the
pseudo-MAC address (4944) and the other doesn’t (6282).
The expected use cases (confirmed by the RFC editor) is to never have a mixed environment where part of
the nodes are using HC1 and part IPHC because this would lead to confusion on what the IPv6 address of a
node is.
Due to this, the nodes configured to use IPHC will drop the packets compressed with HC1 and viceversa.
The drop is logged in the drop trace as DROP_DISALLOWED_COMPRESSION.
Using 6LoWPAN with IPv4 (or other L3 protocols)
As the name implies, 6LoWPAN can handle only IPv6 packets. Any other protocol will be discarded.
Moreover, 6LoWPAN assumes that the network is uniform, as is all the devices connected by the same same
channel are using 6LoWPAN. Mixed environments are not supported by the standard. The reason is simple:
802.15.4 frame doesn’t have a “protocol” field. As a consequence, there is no demultiplexing at MAC layer
and the protocol carried by L2 frames must be known in advance.
In the ns-3 implementation it is possible, but not advisable, to violate this requirement if the
underlying NetDevice is capable of discriminating different protocols. As an example, CsmaNetDevice can
carry IPv4 and 6LoWPAN at the same time. However, this configuration has not been tested.
References
[IANA802]
IANA, assigned IEEE 802 numbers:
http://www.iana.org/assignments/ieee-802-numbers/ieee-802-numbers.xml
[Ethertype]
IEEE Ethertype numbers: http://standards.ieee.org/develop/regauth/ethertype/eth.txt
[Shelby]
Z. Shelby and C. Bormann, 6LoWPAN: The Wireless Embedded Internet. Wiley, 2011. [Online]. Available:
https://books.google.it/books?id=3Nm7ZCxscMQC
Usage
Enabling sixlowpan
Add sixlowpan to the list of modules built with ns-3.
Helper
The helper is patterned after other device helpers.
Examples
The following example can be found in src/sixlowpan/examples/:
• example-sixlowpan.cc: A simple example showing end-to-end data transfer.
In particular, the example enables a very simplified end-to-end data transfer scenario, with a CSMA
network forced to carry 6LoWPAN compressed packets.
Tests
The test provided checks the connection between two UDP clients and the correctness of the received
packets.
Validation
The model has been validated against WireShark, checking whatever the packets are correctly interpreted
and validated.
TOPOLOGY INPUT READERS
The topology modules aim at reading a topology file generated by an automatic topology generator.
The process is divided in two steps:
• running a topology generator to build a topology file
• reading the topology file and build a ns-3 simulation
Hence, model is focused on being able to read correctly the various topology formats.
Currently there are three models:
• ns3::OrbisTopologyReader for Orbis 0.7 traces
• ns3::InetTopologyReader for Inet 3.0 traces
• ns3::RocketfuelTopologyReader for Rocketfuel traces
An helper ns3::TopologyReaderHelper is provided to assist on trivial tasks.
A good source for topology data is also Archipelago.
The current Archipelago Measurements, monthly updated, are stored in the CAIDA website using a complete
notation and triple data source, one for each working group.
A different and more compact notation reporting only the AS-relationships (a sort of more Orbis-like
format) is here: as-relationships.
The compact notation can be easily stripped down to a pure Orbis format, just removing the double
relationships (the compact format use one-way links, while Orbis use two-way links) and pruning the 3rd
parameter. Note that with the compact data Orbis can then be used create a rescaled version of the
topology, thus being the most effective way (to my best knowledge) to make an internet-like topology.
Examples can be found in the directory src/topology-read/examples/
TRAFFIC CONTROL LAYER
Traffic Control Layer
The Traffic Control layer aims at introducing an equivalent of the Linux Traffic Control infrastructure
into ns-3. The Traffic Control layer sits in between the NetDevices (L2) and any network protocol (e.g.
IP). It is in charge of processing packets and performing actions on them: scheduling, dropping, marking,
policing, etc.
Introducing the Traffic Control Layer
The Traffic Control layer intercepts both outgoing packets flowing downwards from the network layer to
the network device and incoming packets flowing in the opposite direction. Currently, only outgoing
packets are processed by the Traffic Control layer. In particular, outgoing packets are enqueued in a
queuing discipline, which can perform multiple actions on them.
In the following, more details are given about how the Traffic Control layer intercepts outgoing and
incoming packets and, more in general, about how the packets traverse the network stack.
Transmitting packets
The IPv{4,6} interfaces uses the aggregated object TrafficControlLayer to send down packets, instead of
calling NetDevice::Send() directly. After the analysis and the process of the packet, when the
backpressure mechanism allows it, TrafficControlLayer will call the Send() method on the right NetDevice.
Receiving packets
The callback chain that (in the past) involved IPv{4,6}L3Protocol and NetDevices, through
ReceiveCallback, is extended to involve TrafficControlLayer. When an IPv{4,6}Interface is added in the
IPv{4,6}L3Protocol, the callback chain is configured to have the following packet exchange:
NetDevice –> Node –> TrafficControlLayer –> IPv{4,6}L3Protocol
Brief description of old node/device/protocol interactions
The main question that we would like to answer in the following paragraphs is: how a ns-3 node can
send/receive packets?
If we analyze any example out there, the ability of the node to receive/transmit packets derives from the
interaction of two helper:
• L2 Helper (something derived from NetDevice)
• L3 Helper (usually from Internet module)
L2 Helper main operations
Any good L2 Helper will do the following operations:
• Create n netdevices (n>1)
• Attach a channel between these devices
• Call Node::AddDevice ()
Obviously the last point is the most important.
Node::AddDevice (network/model/node.cc:128) assigns an interface index to the device, calls
NetDevice::SetNode, sets the receive callback of the device to Node::NonPromiscReceiveFromDevice. Then,
it schedules NetDevice::Initialize() method at Seconds(0.0), then notify the registered
DeviceAdditionListener handlers (not used BY ANYONE).
Node::NonPromiscReceiveFromDevice calls Node::ReceiveFromDevice.
Node::ReceiveFromDevice iterates through ProtocolHandlers, which are callbacks which accept as signature:
ProtocolHandler (Ptr<NetDevice>, Ptr<const Packet>, protocol, from_addr, to_addr, packetType).
If device, protocol number and promiscuous flag corresponds, the handler is invoked.
Who is responsible to set ProtocolHandler ? We will analyze that in the next section.
L3 Helper
We have only internet which provides network protocol (IP). That module splits the operations between two
helpers: InternetStackHelper and Ipv{4,6}AddressHelper.
InternetStackHelper::Install (internet/helper/internet-stack-helper.cc:423) creates and aggregates
protocols {ArpL3,Ipv4L3,Icmpv4}Protocol. It creates the routing protocol, and if Ipv6 is enabled it adds
{Ipv6L3,Icmpv6L4}Protocol. In any case, it instantiates and aggregates an UdpL4Protocol object, along
with a PacketSocketFactory. Ultimately, it creates the required objects and aggregates them to the node.
Let’s assume an Ipv4 environment (things are the same for Ipv6).
Ipv4AddressHelper::Assign (src/internet/helper/ipv4-address-helper.cc:131) registers the handlers. The
process is a bit long. The method is called with a list of NetDevice. For each of them, the node and
Ipv4L3Protocol pointers are retrieved; if an Ipv4Interface is already registered for the device, on that
the address is set. Otherwise, the method Ipv4L3Protocol::AddInterface is called, before adding the
address.
IP interfaces
In Ipv4L3Protocol::AddInterface (src/internet/model/ipv4-l3-protocol.cc:300) two protocol handlers are
installed: one that react to ipv4 protocol number, and one that react to arp protocol number
(Ipv4L3Protocol::Receive and ArpL3Protocol::Receive, respectively). The interface is then created,
initialized, and returned.
Ipv4L3Protocol::Receive (src/internet/model/ipv4-l3-protocol.cc:472) iterates through the interface. Once
it finds the Ipv4Interface which has the same device as the one passed as argument, invokes the rxTrace
callback. If the interface is down, the packet is dropped. Then, it removes the header and trim any
residual frame padding. If checksum is not OK, it drops the packet. Otherwise, forward the packet to the
raw sockets (not used). Then, it ask the routing protocol what is the destiny of that packet. The choices
are: Ipv4L3Protocol::{IpForward, IpMulticastForward,LocalDeliver,RouteInputError}.
Queue disciplines
Model Description
Packets received by the Traffic Control layer for transmission to a netdevice can be passed to a queueing
discipline (queue disc) to perform scheduling and policing. The ns-3 term “queue disc” corresponds to
what Linux calls a “qdisc”. A netdevice can have a single (root) queue disc installed on it. Installing
a queue disc on a netdevice is not mandatory. If a netdevice does not have a queue disc installed on it,
the traffic control layer sends the packets directly to the netdevice. This is the case, for instance, of
the loopback netdevice.
As in Linux, queue discs may be simple queues or may be complicated hierarchical structures. A queue
disc may contain distinct elements:
• queues, which actually store the packets waiting for transmission
• classes, which permit the definition of different treatments for different subdivisions of traffic
• filters, which determine the queue or class which a packet is destined to
Linux uses the terminology “classful qdiscs” or “classless qdiscs” to describe how packets are handled.
This use of the term “class” should be distinguished from the C++ language “class”. In general, the
below discussion uses “class” in the Linux, not C++, sense, but there are some uses of the C++ term, so
please keep in mind the dual use of this term in the below text.
Notice that a child queue disc must be attached to every class and a packet filter is only able to
classify packets of a single protocol. Also, while in Linux some queue discs (e.g., fq-codel) use an
internal classifier and do not make use of packet filters, in ns-3 every queue disc including multiple
queues or multiple classes needs an external filter to classify packets (this is to avoid having the
traffic-control module depend on other modules such as internet).
Queue disc configuration vary from queue disc to queue disc. A typical taxonomy divides queue discs in
classful (i.e., support classes) and classless (i.e., do not support classes). More recently, after the
appearance of multi-queue devices (such as Wi-Fi), some multi-queue aware queue discs have been
introduced. Multi-queue aware queue discs handle as many queues (or queue discs – without using classes)
as the number of transmission queues used by the device on which the queue disc is installed. An attempt
is made, also, to classify each packet similarly in the queue disc and within the device (i.e., to keep
the packet classification consistent across layers).
The traffic control layer interacts with a queue disc in a simple manner: after requesting to enqueue a
packet, the traffic control layer requests the qdisc to “run”, i.e., to dequeue a set of packets, until a
predefined number (“quota”) of packets is dequeued or the netdevice stops the queue disc. A netdevice
shall stop the queue disc when its transmission queue does not have room for another packet. Also, a
netdevice shall wake the queue disc when it detects that there is room for another packet in its
transmission queue, but the transmission queue is stopped. Waking a queue disc is equivalent to make it
run.
Every queue disc collects statistics about the total number of packets/bytes received from the upper
layers (in case of root queue disc) or from the parent queue disc (in case of child queue disc),
enqueued, dequeued, requeued, dropped, dropped before enqueue, dropped after dequeue, marked, and stored
in the queue disc and sent to the netdevice or to the parent queue disc. Note that packets that are
dequeued may be requeued, i.e., retained by the traffic control infrastructure, if the netdevice is not
ready to receive them. Requeued packets are not part of the queue disc. The following identities hold:
• dropped = dropped before enqueue + dropped after dequeue
• received = dropped before enqueue + enqueued
• queued = enqueued - dequeued
• sent = dequeued - dropped after dequeue (- 1 if there is a requeued packet)
Separate counters are also kept for each possible reason to drop a packet. When a packet is dropped by
an internal queue, e.g., because the queue is full, the reason is “Dropped by internal queue”. When a
packet is dropped by a child queue disc, the reason is “(Dropped by child queue disc) ” followed by the
reason why the child queue disc dropped the packet.
The QueueDisc base class provides the SojournTime trace source, which provides the sojourn time of every
packet dequeued from a queue disc, including packets that are dropped or requeued after being dequeued.
The sojourn time is taken when the packet is dequeued from the queue disc, hence it does not account for
the additional time the packet is retained within the queue disc in case it is requeued.
Design
A C++ abstract base class, class QueueDisc, is subclassed to implement a specific queue disc. A subclass
is required to implement the following methods:
• bool DoEnqueue (Ptr<QueueDiscItem> item): Enqueue a packet
• Ptr<QueueDiscItem> DoDequeue (void): Dequeue a packet
• bool CheckConfig (void) const: Check if the configuration is correct
• void InitializeParams (void): Initialize queue disc parameters
and may optionally override the default implementation of the following method:
• Ptr<const QueueDiscItem> DoPeek (void) const: Peek the next packet to extract
The default implementation of the DoPeek method is based on the qdisc_peek_dequeued function of the Linux
kernel, which dequeues a packet and retains it in the queue disc as a requeued packet. This approach is
recommended especially for queue discs for which it is not obvious what is the next packet that will be
dequeued (e.g., queue discs having multiple internal queues or child queue discs or queue discs that drop
packets after dequeue). Therefore, unless the subclass redefines the DoPeek method, calling Peek causes
the next packet to be dequeued from the queue disc, though the packet is still considered to be part of
the queue disc and the dequeue trace is fired when Dequeue is called and the packet is actually extracted
from the queue disc.
The C++ base class QueueDisc implements:
• methods to add/get a single queue, class or filter and methods to get the number of installed queues,
classes or filters
• a Classify method which classifies a packet by processing the list of filters until a filter able to
classify the packet is found
• methods to extract multiple packets from the queue disc, while handling transmission (to the device)
failures by requeuing packets
The base class QueueDisc provides many trace sources:
• Enqueue
• Dequeue
• Requeue
• Drop
• Mark
• PacketsInQueue
• BytesInQueue
The C++ base class QueueDisc holds the list of attached queues, classes and filter by means of three
vectors accessible through attributes (InternalQueueList, QueueDiscClassList and PacketFilterList).
Internal queues are implemented as (subclasses of) Queue objects. A Queue stores QueueItem objects, which
consist of just a Ptr<Packet>. Since a queue disc has to store at least the destination address and the
protocol number for each enqueued packet, a new C++ class, QueueDiscItem, is derived from QueueItem to
store such additional information for each packet. Thus, internal queues are implemented as Queue objects
storing QueueDiscItem objects. Also, there could be the need to store further information depending on
the network layer protocol of the packet. For instance, for IPv4 and IPv6 packets it is needed to
separately store the header and the payload, so that header fields can be manipulated, e.g., to support
Explicit Congestion Notification as defined in RFC 3168. To this end, subclasses Ipv4QueueDiscItem and
Ipv6QueueDiscItem are derived from QueueDiscItem to additionally store the IP header and provide protocol
specific operations such as ECN marking.
Classes (in the Linux sense of the term) are implemented via the QueueDiscClass class, which consists of
a pointer to the attached queue disc. Such a pointer is accessible through the QueueDisc attribute.
Classful queue discs needing to set parameters for their classes can subclass QueueDiscClass and add the
required parameters as attributes.
An abstract base class, PacketFilter, is subclassed to implement specific filters. Subclasses are
required to implement two virtual private pure methods:
• bool CheckProtocol (Ptr<QueueDiscItem> item) const: check whether the filter is able to classify
packets of the same protocol as the given packet
• int32_t DoClassify (Ptr<QueueDiscItem> item) const: actually classify the packet
PacketFilter provides a public method, Classify, which first calls CheckProtocol to check that the
protocol of the packet matches the protocol of the filter and then calls DoClassify. Specific filters
subclassed from PacketFilter should not be placed in the traffic-control module but in the module
corresponding to the protocol of the classified packets.
Usage
The traffic control layer is automatically created and inserted on an ns3::Node object when typical
device and internet module helpers are used. By default, the InternetStackHelper::Install() method
aggregates a TrafficControlLayer object to every node. When invoked to assign an IPv{4,6} address to a
device, the Ipv{4,6}AddressHelper, besides creating an Ipv{4,6}Interface, also installs the default qdisc
on the device, unless a queue disc has been already installed. For single-queue NetDevices (such as
PointToPoint, Csma and Simple), the default root qdisc is FqCoDel. For multi-queue NetDevices (such as
Wifi), the default root qdisc is Mq with as many FqCoDel child qdiscs as the number of device queues.
To install a queue disc other than the default one, it is necessary to install such queue disc before an
IP address is assigned to the device. Alternatively, the default queue disc can be removed from the
device after assigning an IP address, by using the Uninstall method of the TrafficControlHelper C++
class, and then installing a different queue disc on the device. By uninstalling without adding a new
queue disc, it is also possible to have no queue disc installed on a device.
Helpers
A typical usage pattern is to create a traffic control helper and to configure type and attributes of
queue discs, queues, classes and filters from the helper, For example, the pfifo_fast can be configured
as follows:
TrafficControlHelper tch;
uint16_t handle = tch.SetRootQueueDisc ("ns3::PfifoFastQueueDisc");
tch.AddInternalQueues (handle, 3, "ns3::DropTailQueue", "MaxSize", StringValue ("1000p"));
QueueDiscContainer qdiscs = tch.Install (devices);
The above code adds three internal queues to the root queue disc of type PfifoFast. With the above
configuration, the config path of the root queue disc installed on the j-th device of the i-th node (the
index of a device is the same as in DeviceList) is:
/NodeList/[i]/$ns3::TrafficControlLayer/RootQueueDiscList/[j]
and the config path of the second internal queue is:
/NodeList/[i]/$ns3::TrafficControlLayer/RootQueueDiscList/[j]/InternalQueueList/1
For this helper’s configuration to take effect, it should be added to the ns-3 program after
InternetStackHelper::Install() is called, but before IP addresses are configured using
Ipv{4,6}AddressHelper.
Implementation details
In Linux, the struct netdev_queue is used to store information about a single transmission queue of a
device: status (i.e., whether it has been stopped or not), data used by techniques such as Byte Queue
Limits and a qdisc pointer field that is mainly used to solve the following problems:
• if a device transmission queue is (almost) empty, identify the queue disc to wake
• if a packet will be enqueued in a given device transmission queue, identify the queue disc which the
packet must be enqueued into
The latter problem arises because Linux attempts to determine the device transmission queue which a
packet will be enqueued into before passing the packet to a queue disc. This is done by calling a
specific function of the device driver, if implemented, or by employing fallback mechanisms (such as
hashing of the addresses) otherwise. The identifier of the selected device transmission queue is stored
in the queue_mapping field of the struct sk_buff, so that both the queue disc and the device driver can
get the same information. In ns-3, such identifier is stored in a member of the QueueDiscItem class.
The NetDeviceQueue class in ns-3 is the equivalent of the Linux struct netdev_queue. The qdisc field of
the Linux struct netdev_queue, however, cannot be similarly stored in a NetDeviceQueue object, because it
would make the network module depend on the traffic-control module. Instead, this information is stored
in the TrafficControlLayer object aggregated to each node. In particular, a TrafficControlLayer object
holds a struct NetDeviceInfo which stores, for each NetDevice, a pointer to the root queue disc installed
on the device, a pointer to the netdevice queue interface (see below) aggregated to the device, and a
vector of pointers (one for each device transmission queue) to the queue discs to activate when the above
problems occur. The traffic control layer takes care of configuring such a vector at initialization time,
based on the “wake mode” of the root queue disc. If the wake mode of the root queue disc is WAKE_ROOT,
then all the elements of the vector are pointers to the root queue disc. If the wake mode of the root
queue disc is WAKE_CHILD, then each element of the vector is a pointer to a distinct child queue disc.
This requires that the number of child queue discs matches the number of netdevice queues. It follows
that the wake mode of a classless queue disc must necessarily be WAKE_ROOT. These two configurations are
illustrated by the figures below.
Setup of a queue disc (wake mode: WAKE_ROOT) below shows how the TrafficControlLayer map looks like in
case of a classful root queue disc whose wake mode is WAKE_ROOT.
[image] Setup of a queue disc (wake mode: WAKE_ROOT).UNINDENT
Setup of a multi-queue aware queue disc below shows instead how the TrafficControlLayer map looks like
in case of a classful root queue disc whose wake mode is WAKE_CHILD.
[image] Setup of a multi-queue aware queue disc.UNINDENT
A NetDeviceQueueInterface object is used by the traffic control layer to access the information stored
in the NetDeviceQueue objects, retrieve the number of transmission queues of the device and get the
transmission queue selected for the transmission of a given packet. A NetDeviceQueueInterface object
must be therefore aggregated to all the devices having an interface supporting the traffic control
layer (i.e., an IPv4 or IPv6 interface). In particular:
• a NetDeviceQueueInterface object is aggregated to all the devices by the NetDevice helper classes, at
Install time. See, for example, the implementation in the method CsmaHelper::InstallPriv().
• when notified that a netdevice queue interface has been aggregated, traffic control aware devices can
cache the pointer to the netdevice queue interface created by the traffic control layer into a member
variable. Also, multi-queue devices can set the number of device transmission queues and set the select
queue callback through the netdevice queue interface
• at initialization time, the traffic control (after calling device->Initialize () to ensure that the
netdevice has set the number of device transmission queues, if it has to do so) completes the
installation of the queue discs by setting the wake callbacks on the device transmission queues
(through the netdevice queue interface). Also, the traffic control calls the Initialize method of the
root queue discs. This initialization of queue discs triggers calls to the CheckConfig and
InitializeParams methods of the queue disc.
Requeue
In Linux, a packet dequeued from a queue disc can be requeued (i.e., stored somewhere and sent to the
device at a later time) in some circumstances. Firstly, the function used to dequeue a packet
(dequeue_skb) actually dequeues a packet only if the device is multi-queue or the (unique) device queue
is not stopped. If a packet has been dequeued from the queue disc, it is passed to the sch_direct_xmit
function for transmission to the device. This function checks whether the device queue the packet is
destined to is stopped, in which case the packet is requeued. Otherwise, the packet is sent to the
device. If the device returns NETDEV_TX_BUSY, the packet is requeued. However, it is advised that the
function called to send a packet to the device (ndo_start_xmit) should always return NETDEV_TX_OK, which
means that the packet is consumed by the device driver and thus needs not to be requeued. However, the
ndo_start_xmit function of the device driver is allowed to return NETDEV_TX_BUSY (and hence the packet is
requeued) when there is no room for the received packet in the device queue, despite the queue is not
stopped. This case is considered as a corner case or an hard error, and should be avoided.
ns-3 implements the requeue mechanism in a similar manner, the only difference being that packets are not
requeued when such corner cases occur. Basically, the method used to dequeue a packet
(QueueDisc::DequeuePacket) actually dequeues a packet only if the device is multi-queue or the (unique)
device queue is not stopped. If a packet has been dequeued from the queue disc, it is passed to the
QueueDisc::Transmit method for transmission to the device. This method checks whether the device queue
the packet is destined to is stopped, in which case the packet is requeued. Otherwise, the packet is sent
to the device. We request netdevices to stop a device queue when it is not able to store another packet,
so as to avoid the situation in which a packet is received that cannot be enqueued while the device queue
is not stopped. Should such a corner case occur, the netdevice drops the packet but, unlike Linux, the
value returned by NetDevice::Send is ignored and the packet is not requeued.
The way the requeue mechanism is implemented in ns-3 has the following implications:
• if the underlying device has a single queue, no packet will ever be requeued. Indeed, if the device
queue is not stopped when QueueDisc::DequeuePacket is called, it will not be stopped also when
QueueDisc::Transmit is called, hence the packet is not requeued (recall that a packet is not requeued
after being sent to the device, as the value returned by NetDevice::Send is ignored).
• if the underlying device does not implement flow control, i.e., it does not stop its queue(s), no
packet will ever be requeued (recall that a packet is only requeued by QueueDisc::Transmit when the
device queue the packet is destined to is stopped)
It turns out that packets may only be requeued when the underlying device is multi-queue and supports
flow control.
Fifo queue disc
Model Description
FifoQueueDisc implements the FIFO (First-In First-Out) policy. Packets are enqueued in the unique
internal queue, which is implemented as a DropTail queue. The queue disc capacity can be specified in
terms of either packets or bytes, depending on the value of the Mode attribute.
User is allowed to provide an internal queue before the queue disc is initialized. If no internal queue
is provided, one DropTail queue having the same capacity of the queue disc is created by default. No
packet filter can be added to a FifoQueueDisc.
Attributes
The FifoQueueDisc class holds the following attribute:
• MaxSize: The maximum number of packets/bytes the queue disc can hold. The default value is 1000
packets.
Validation
The fifo model is tested using FifoQueueDiscTestSuite class defined in
src/traffic-control/test/fifo-queue-disc-test-suite.cc. The test aims to check that the capacity of the
queue disc is not exceeded and packets are dequeued in the correct order.
pfifo_fast queue disc
Model Description
PfifoFastQueueDisc behaves like pfifo_fast, which is the default queue disc enabled on Linux systems
(init systems such as systemd may override such default setting). Packets are enqueued in three priority
bands (implemented as FIFO droptail queues) based on their priority (users can read Socket-options for
details on how to set packet priority). The four least significant bits of the priority are used to
determine the selected band according to the following table:
┌───────────────┬──────┐
│Priority & 0xf │ Band │
├───────────────┼──────┤
│0 │ 1 │
├───────────────┼──────┤
│1 │ 2 │
├───────────────┼──────┤
│2 │ 2 │
├───────────────┼──────┤
│3 │ 2 │
├───────────────┼──────┤
│4 │ 1 │
├───────────────┼──────┤
│5 │ 2 │
├───────────────┼──────┤
│6 │ 0 │
├───────────────┼──────┤
│7 │ 0 │
├───────────────┼──────┤
│8 │ 1 │
├───────────────┼──────┤
│9 │ 1 │
├───────────────┼──────┤
│10 │ 1 │
├───────────────┼──────┤
│11 │ 1 │
├───────────────┼──────┤
│12 │ 1 │
├───────────────┼──────┤
│13 │ 1 │
├───────────────┼──────┤
│14 │ 1 │
├───────────────┼──────┤
│15 │ 1 │
└───────────────┴──────┘
The system behaves similar to three ns3::DropTail queues operating together, in which packets from higher
priority bands are always dequeued before a packet from a lower priority band is dequeued.
The queue disc capacity, i.e., the maximum number of packets that can be enqueued in the queue disc, is
set through the MaxSize attribute, which plays the same role as txqueuelen in Linux. If no internal queue
is provided, three DropTail queues having each a capacity equal to MaxSize are created by default. User
is allowed to provide queues, but they must be three, operate in packet mode and each have a capacity not
less than MaxSize. No packet filter can be added to a PfifoFastQueueDisc.
Attributes
The PfifoFastQueueDisc class holds a single attribute:
• MaxSize: The maximum number of packets accepted by the queue disc. The default value is 1000.
Examples
Various examples located in src/traffic-control/examples (e.g., codel-vs-pfifo-asymmetric.cc) shows how
to configure and install a PfifoFastQueueDisc on internet nodes.
Validation
The pfifo_fast model is tested using PfifoFastQueueDiscTestSuite class defined in
src/test/ns3tc/pfifo-fast-queue-disc-test-suite.cc. The suite includes 4 test cases:
• Test 1: The first test checks whether IPv4 packets are enqueued in the correct band based on the TOS
byte
• Test 2: The second test checks whether IPv4 packets are enqueued in the correct band based on the TOS
byte
• Test 3: The third test checks that the queue disc cannot enqueue more packets than its limit
• Test 4: The fourth test checks that packets that the filters have not been able to classify are
enqueued into the default band of 1
Prio queue disc
Model Description
PrioQueueDisc implements a strict priority policy, where packets are dequeued from a band only if higher
priority bands are all empty. PrioQueueDisc is a classful queue disc and can have an arbitrary number of
bands, each of which is handled by a queue disc of any kind. The capacity of PrioQueueDisc is not
limited; packets can only be dropped by child queue discs (which may have a limited capacity). If no
packet filter is installed or able to classify a packet, then the packet is enqueued into a priority band
based on its priority (modulo 16), which is used as an index into an array called priomap. Users can read
Socket-options for details on how to set the packet priority. If a packet is classified by an installed
packet filter and the returned value i is non-negative and less than the number of priority bands, then
the packet is enqueued into the i-th priority band. Otherwise, the packet is enqueued into the priority
band specified by the first element of the priomap array.
If no queue disc class is added by the user before the queue disc is initialized, three child queue discs
of type FifoQueueDisc are automatically added. It has to be noted that PrioQueueDisc needs at least two
child queue discs.
Attributes
The PrioQueueDisc class holds the following attribute:
• Priomap: The priority to band mapping. The default value is the same mapping as the (fixed) one used by
PfifoFastQueueDisc.
Examples
An example of how to configure PrioQueueDisc with custom child queue discs and priomap is provided by
queue-discs-benchmark.cc located in examples/traffic-control:
TrafficControlHelper tch;
uint16_t handle = tch.SetRootQueueDisc ("ns3::PrioQueueDisc", "Priomap", StringValue ("0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1"));
TrafficControlHelper::ClassIdList cid = tch.AddQueueDiscClasses (handle, 2, "ns3::QueueDiscClass");
tch.AddChildQueueDisc (handle, cid[0], "ns3::FifoQueueDisc");
tch.AddChildQueueDisc (handle, cid[1], "ns3::RedQueueDisc");
The code above adds two classes (bands) to a PrioQueueDisc. The highest priority one is a FifoQueueDisc,
the other one is a RedQueueDisc. The attribute Priomap is set to an array containing only 0 and 1 (since
PrioQueueDisc only has two bands).
Validation
PrioQueueDisc is tested using PrioQueueDiscTestSuite class defined in
src/traffic-control/test/prio-queue-disc-test-suite.cc. The test aims to check that: i) packets are
enqueued in the correct band based on their priority and the priomap or according to the value returned
by the installed packet filter; ii) packets are dequeued in the correct order.
The test suite can be run using the following commands:
$ ./waf configure --enable-examples --enable-tests
$ ./waf build
$ ./test.py -s prio-queue-disc
or
$ NS_LOG="PrioQueueDisc" ./waf --run "test-runner --suite=prio-queue-disc"
TBF queue disc
This chapter describes the TBF ([Ref1]) queue disc implementation in ns-3. The TBF model in ns-3 is
ported based on Linux kernel code implemented by A. Kuznetsov and D. Torokhov.
TBF is a qdisc that allows controlling the bandwidth of the output according to a set rate with the
possibility of managing burst conditions also. The TBF implementation consists of a bucket (buffer)
having a limited capacity into which tokens (normally representing a unit of bytes or a single packet of
predetermined size) are added at a fixed rate ‘r’ called the token rate. Whenever a packet arrives into
the tx queue (fifo by default), the bucket is checked to see if there are appropriate number of tokens
that is equivalent to the length of the packet in bytes. If yes, then the tokens are removed and the
packet is passed for transmission. If no, then packets will have to wait until there are sufficient
tokens in the bucket. This data conformance can be thus put into three possible scenarios [Ref3]:
1. Data rate = Token rate : Packets pass without delay.
2. Data rate < Token rate : The tokens might accumulate and the bucket might become full. Then, the next
packets to enter TBF will be transmitted right away without having any limit applied to them, until
the bucket is empty. This is called a burst condition and in TBF the burst parameter defines the size
of the bucket. In order to overcome this problem and provide better control over the bursts, TBF
implements a second bucket which is smaller and generally the same size as the MTU. This second bucket
cannot store large amount of tokens, but its replenishing rate will be a lot faster than the one of
the big bucket. This second rate is called ‘peakRate’ and it will determine the maximum rate of a
burst.
3. Data rate > Token rate : This causes the TBF algorithm to throttle itself for a while as soon as the
bucket gets empty. This is called an ‘overlimit situation’ [Ref2]. In this situation, some of the
packets will be blocked until enough tokens are available at which time a schedule for the waking of
the queue will be done. If packets keep coming in, at a larger rate, then the packets will start to
get dropped when the total number of bytes exceeds the QueueLimit.
Model Description
The TBF queue disc does not require packet filters, does not admit internal queues and uses a single
child queue disc. If the user does not provide a child queue disc, a Fifo queue disc operating in the
same mode (packet or byte) as the TBF queue disc and having a size equal to the TBF QueueLimit attribute
is created. Otherwise, the capacity of the TBF queue disc is determined by the capacity of the child
queue disc.
There are two token buckets: first bucket and second bucket. The size of the first bucket called ‘Burst’
should always be greater than the size of the second bucket called the Mtu (which is usually the size of
a single packet). But the ‘PeakRate’ which is the second bucket’s token rate should be always greater
than the ‘Rate’ which is the first bucket’s token rate.
If the PeakRate is zero, then the second bucket does not exist. In order to activate the second bucket,
both the Mtu and PeakRate values have to be greater than zero. If the Mtu value is zero at initialization
time, then if a NetDevice exits, the Mtu’s value will be equal to the Mtu of the NetDevice. But if no
NetDevice exists, then the QueueDisc will complain thus prompting the user to set the Mtu value.
The source code for the TBF model is located in the directory src/traffic-control/model and consists of 2
files tbf-queue-disc.h and tbf-queue-disc.cc defining a TbfQueueDisc class.
• class TbfQueueDisc: This class implements the main TBF algorithm:
• TbfQueueDisc::DoEnqueue (): This routine enqueue’s the incoming packet if the queue is not full and
drops the packet otherwise.
• TbfQueueDisc::DoPeek (): This routine peeks for the top item in the queue and if the queue is not
empty, it returns the topmost item.
• TbfQueueDisc::DoDequeue (): This routine performs the dequeuing of packets according to the following
logic:
• It calls TbfQueueDisc::Peek () and calculates the size of the packet to be dequeued in bytes.
• Then it calculates the time difference ‘delta’, which is the time elapsed since the last update of
tokens in the buckets.
• If the second bucket exists, the number of tokens are updated according to the ‘PeakRate’ and
‘delta’.
• From this second bucket a number of tokens equal to the size of the packet to be dequeued is
subtracted.
• Now the number of tokens in the first bucket are updated according to ‘Rate’ and ‘delta’.
• From this first bucket a number of tokens equal to the size of the packet to be dequeued is
subtracted.
• If after this, both the first and second buckets have tokens greater than zero, then the packet is
dequeued.
• Else, an event to QueueDisc::Run () is scheduled after a time period when enough tokens will be
present for the dequeue operation.
References
[Ref1]
A. Kuznetsov and D. Torokhov; Linux Cross Reference Source Code; Available online at
http://lxr.free-electrons.com/source/net/sched/sch_tbf.c.
[Ref2]
J. Vehent; Journey to the Center of the Linux Kernel: Traffic Control, Shaping and QoS; Available online
at
http://wiki.linuxwall.info/doku.php/en:resources:dossiers:networking:traffic_control#tbf_-_token_bucket_filter.
[Ref3]
Practical IP Network QoS: TBF queuing discipline; Available online at
http://web.opalsoft.net/qos/default.php?p=ds-24.
Attributes
The key attributes that the TbfQueueDisc class holds include the following:
• MaxSize: The maximum number of packets/bytes the queue disc can hold. The default value is 1000
packets.
• Burst: Size of the first bucket, in bytes. The default value is 125000 bytes.
• Mtu: Size of second bucket defaults to the MTU of the attached NetDevice, if any, or 0 otherwise.
• Rate: Rate at which tokens enter the first bucket. The default value is 125KB/s.
• PeakRate: Rate at which tokens enter the second bucket. The default value is 0KB/s, which means that
there is no second bucket.
TraceSources
The TbfQueueDisc class provides the following trace sources:
• TokensInFirstBucket: Number of First Bucket Tokens in bytes
• TokensInSecondBucket: Number of Second Bucket Tokens in bytes
Examples
The example for TBF is tbf-example.cc located in examples/traffic-control/. The command to run the file
(the invocation below shows the available command-line options) is:
.. sourcecode:: bash
$ ./waf –run “tbf-example –PrintHelp” $ ./waf –run “tbf-example –burst=125000 –rate=1Mbps
–peakRate=1.5Mbps”
The expected output from the previous commands are traced value changes in the number of tokens in the
first and second buckets.
Validation
The TBF model is tested using TbfQueueDiscTestSuite class defined in
src/traffic-control/test/tbf-queue-disc-test-suite.cc. The suite includes 4 test cases:
• Test 1: Simple Enqueue/Dequeue with verification of attribute setting and subtraction of tokens from
the buckets.
• Test 2: When DataRate == FirstBucketTokenRate; packets should pass smoothly.
• Test 3: When DataRate >>> FirstBucketTokenRate; some packets should get blocked and waking of queue
should get scheduled.
• Test 4: When DataRate < FirstBucketTokenRate; burst condition, peakRate is set so that bursts are
controlled.
The test suite can be run using the following commands:
.. sourcecode:: bash
$ ./waf configure –enable-examples –enable-tests $ ./waf build $ ./test.py -s tbf-queue-disc
or
.. sourcecode:: bash
$ NS_LOG=”TbfQueueDisc” ./waf –run “test-runner –suite=tbf-queue-disc”
RED queue disc
Model Description
Random Early Detection (RED) is a queue discipline that aims to provide early signals to transport
protocol congestion control (e.g. TCP) that congestion is imminent, so that they back off their rate
gracefully rather than with a bunch of tail-drop losses (possibly incurring TCP timeout). The model in
ns-3 is a port of Sally Floyd’s ns-2 RED model.
Note that, starting from ns-3.25, RED is no longer a queue variant and cannot be installed as a NetDevice
queue. Instead, RED is a queue disc and must be installed in the context of the traffic control (see the
examples mentioned below).
The RED queue disc does not require packet filters, does not admit child queue discs and uses a single
internal queue. If not provided by the user, a DropTail queue operating in the same mode (packet or byte)
as the queue disc and having a size equal to the RED MaxSize attribute is created. Otherwise, the
capacity of the queue disc is determined by the capacity of the internal queue provided by the user.
Adaptive Random Early Detection (ARED)
ARED is a variant of RED with two main features: (i) automatically sets Queue weight, MinTh and MaxTh and
(ii) adapts maximum drop probability. The model in ns-3 contains implementation of both the features, and
is a port of Sally Floyd’s ns-2 ARED model. Note that the user is allowed to choose and explicitly
configure the simulation by selecting feature (i) or feature (ii), or both.
Feng’s Adaptive RED
Feng’s Adaptive RED is a variant of RED that adapts the maximum drop probability. The model in ns-3
contains implementation of this feature, and is a port of ns-2 Feng’s Adaptive RED model.
Nonlinear Random Early Detection (NLRED)
NLRED is a variant of RED in which the linear packet dropping function of RED is replaced by a nonlinear
quadratic function. This approach makes packet dropping gentler for light traffic load and aggressive for
heavy traffic load.
Explicit Congestion Notification (ECN)
This RED model supports an ECN mode of operation to notify endpoints of congestion that may be developing
in a bottleneck queue, without resorting to packet drops. Such a mode is enabled by setting the UseEcn
attribute to true (it is false by default) and only affects incoming packets with the ECT bit set in
their header. When the average queue length is between the minimum and maximum thresholds, an incoming
packet is marked instead of being dropped. When the average queue length is above the maximum threshold,
an incoming packet is marked (instead of being dropped) only if the UseHardDrop attribute is set to false
(it is true by default).
The implementation of support for ECN marking is done in such a way as to not impose an internet module
dependency on the traffic control module. The RED model does not directly set ECN bits on the header,
but delegates that job to the QueueDiscItem class. As a result, it is possible to use RED queues for
other non-IP QueueDiscItems that may or may not support the Mark () method.
References
The RED queue disc aims to be close to the results cited in: S.Floyd, K.Fall
http://icir.org/floyd/papers/redsims.ps
ARED queue implementation is based on the algorithm provided in: S. Floyd et al,
http://www.icir.org/floyd/papers/adaptiveRed.pdf
Feng’s Adaptive RED queue implementation is based on the algorithm provided in: W. C. Feng et al,
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=752150
NLRED queue implementation is based on the algorithm provided in: Kaiyu Zhou et al,
http://www.sciencedirect.com/science/article/pii/S1389128606000879
The addition of explicit congestion notification (ECN) to IP: K. K. Ramakrishnan et al,
https://tools.ietf.org/html/rfc3168
Attributes
The RED queue contains a number of attributes that control the RED policies:
• MaxSize
• MeanPktSize
• IdlePktSize
• Wait (time)
• Gentle mode
• MinTh, MaxTh
• Queue weight
• LInterm
• LinkBandwidth
• LinkDelay
• UseEcn
• UseHardDrop
In addition to RED attributes, ARED queue requires following attributes:
• ARED (Boolean attribute. Default: false)
• AdaptMaxP (Boolean attribute to adapt m_curMaxP. Default: false)
• Target Delay (time)
• Interval (time)
• LastSet (time)
• Top (upper limit of m_curMaxP)
• Bottom (lower limit of m_curMaxP)
• Alpha (increment parameter for m_curMaxP)
• Beta (decrement parameter for m_curMaxP)
• RTT
In addition to RED attributes, Feng’s Adaptive RED queue requires following attributes:
• FengAdaptive (Boolean attribute, Default: false)
• Status (status of current queue length, Default: Above)
• FengAlpha (increment parameter for m_curMaxP, Default: 3)
• FengBeta (decrement parameter for m_curMaxP, Default: 2)
The following attribute should be turned on to simulate NLRED queue disc:
• NLRED (Boolean attribute. Default: false)
Consult the ns-3 documentation for explanation of these attributes.
Simulating ARED
To switch on ARED algorithm, the attribute ARED must be set to true, as done in
src/traffic-control/examples/adaptive-red-tests.cc:
Config::SetDefault ("ns3::RedQueueDisc::ARED", BooleanValue (true));
Setting ARED to true implicitly configures both: (i) automatic setting of Queue weight, MinTh and MaxTh
and (ii) adapting m_curMaxP.
NOTE: To explicitly configure (i) or (ii), set ARED attribute to false and follow the procedure described
next:
To configure (i); Queue weight, MinTh and MaxTh, all must be set to 0, as done in
src/traffic-control/examples/adaptive-red-tests.cc:
Config::SetDefault ("ns3::RedQueueDisc::QW", DoubleValue (0.0));
Config::SetDefault ("ns3::RedQueueDisc::MinTh", DoubleValue (0));
Config::SetDefault ("ns3::RedQueueDisc::MaxTh", DoubleValue (0));
To configure (ii); AdaptMaxP must be set to true, as done in
src/traffic-control/examples/adaptive-red-tests.cc:
Config::SetDefault ("ns3::RedQueueDisc::AdaptMaxP", BooleanValue (true));
Simulating Feng’s Adaptive RED
To switch on Feng’s Adaptive RED algorithm, the attribute FengAdaptive must be set to true, as done in
examples/traffic-control/red-vs-fengadaptive.cc:
Config::SetDefault ("ns3::RedQueueDisc::FengAdaptive", BooleanValue (true));
Simulating NLRED
To switch on NLRED algorithm, the attribute NLRED must be set to true, as shown below:
Config::SetDefault ("ns3::RedQueueDisc::NLRED", BooleanValue (true));
Examples
The RED queue example is found at src/traffic-control/examples/red-tests.cc.
ARED queue examples can be found at: src/traffic-control/examples/adaptive-red-tests.cc and
src/traffic-control/examples/red-vs-ared.cc
Feng’s Adaptive RED example can be found at: examples/traffic-control/red-vs-fengadaptive.cc
NLRED queue example can be found at: examples/traffic-control/red-vs-nlred.cc
Validation
The RED model has been validated and the report is currently stored at:
https://github.com/downloads/talau/ns-3-tcp-red/report-red-ns3.pdf
CoDel queue disc
This chapter describes the CoDel ([Nic12], [Nic14]) queue disc implementation in ns-3.
Developed by Kathleen Nichols and Van Jacobson as a solution to the bufferbloat [Buf14] problem, CoDel
(Controlled Delay Management) is a queuing discipline that uses a packet’s sojourn time (time in queue)
to make decisions on packet drops.
Note that, starting from ns-3.25, CoDel is no longer a queue variant and cannot be installed as a
NetDevice queue. Instead, CoDel is a queue disc and must be installed in the context of the traffic
control (see the examples mentioned below).
Model Description
The source code for the CoDel model is located in the directory src/traffic-control/model and consists of
2 files codel-queue-disc.h and codel-queue-disc.cc defining a CoDelQueueDisc class and a helper
CoDelTimestampTag class. The code was ported to ns-3 by Andrew McGregor based on Linux kernel code
implemented by Dave Täht and Eric Dumazet.
• class CoDelQueueDisc: This class implements the main CoDel algorithm:
• CoDelQueueDisc::DoEnqueue (): This routine tags a packet with the current time before pushing it into
the queue. The timestamp tag is used by CoDelQueue::DoDequeue() to compute the packet’s sojourn
time. If the queue is full upon the packet arrival, this routine will drop the packet and record the
number of drops due to queue overflow, which is stored in m_dropOverLimit.
• CoDelQueueDisc::ShouldDrop (): This routine is CoDelQueueDisc::DoDequeue()’s helper routine that
determines whether a packet should be dropped or not based on its sojourn time. If the sojourn time
goes above m_target and remains above continuously for at least m_interval, the routine returns true
indicating that it is OK to drop the packet. Otherwise, it returns false.
• CoDelQueueDisc::DoDequeue (): This routine performs the actual packet drop based on
CoDelQueueDisc::ShouldDrop ()’s return value and schedules the next drop/mark.
• class CoDelTimestampTag: This class implements the timestamp tagging for a packet. This tag is used to
compute the packet’s sojourn time (the difference between the time the packet is dequeued and the time
it is pushed into the queue).
There are 2 branches to CoDelQueueDisc::DoDequeue ():
1. If the queue is currently in the dropping state, which means the sojourn time has remained above
m_target for more than m_interval, the routine determines if it’s OK to leave the dropping state or
it’s time for the next drop/mark. When CoDelQueueDisc::ShouldDrop () returns false, the queue can move
out of the dropping state (set m_dropping to false). Otherwise, the queue continuously drops/marks
packets and updates the time for next drop (m_dropNext) until one of the following conditions is met:
1. The queue is empty, upon which the queue leaves the dropping state and exits
CoDelQueueDisc::ShouldDrop () routine;
2. CoDelQueueDisc::ShouldDrop () returns false (meaning the sojourn time goes below m_target) upon
which the queue leaves the dropping state;
3. It is not yet time for next drop/mark (m_dropNext is less than current time) upon which the
queue waits for the next packet dequeue to check the condition again.
2. If the queue is not in the dropping state, the routine enters the dropping state and drop/mark the
first packet if CoDelQueueDisc::ShouldDrop () returns true (meaning the sojourn time has gone above
m_target for at least m_interval for the first time or it has gone above again after the queue leaves
the dropping state).
The CoDel queue disc does not require packet filters, does not admit child queue discs and uses a single
internal queue. If not provided by the user, a DropTail queue operating in the same mode (packet or byte)
as the queue disc and having a size equal to the CoDel MaxSize attribute is created. Otherwise, the
capacity of the queue disc is determined by the capacity of the internal queue provided by the user.
References
[Nic12]
K. Nichols and V. Jacobson, Controlling Queue Delay, ACM Queue, Vol. 10 No. 5, May 2012. Available
online at http://queue.acm.org/detail.cfm?id=2209336.
[Nic14]
K. Nichols and V. Jacobson, Internet-Draft: Controlled Delay Active Queue Management, March 2014.
Available online at http://tools.ietf.org/html/draft-nichols-tsvwg-codel-02.
[Buf14]
Bufferbloat.net. Available online at http://www.bufferbloat.net/.
Attributes
The key attributes that the CoDelQueue class holds include the following:
• MaxSize: The maximum number of packets/bytes the queue can hold. The default value is 1500 *
DEFAULT_CODEL_LIMIT, which is 1500 * 1000 bytes.
• MinBytes: The CoDel algorithm minbytes parameter. The default value is 1500 bytes.
• Interval: The sliding-minimum window. The default value is 100 ms.
• Target: The CoDel algorithm target queue delay. The default value is 5 ms.
• UseEcn: True to use ECN (packets are marked instead of being dropped). The default value is false.
• CeThreshold: The CoDel CE threshold for marking packets. Disabled by default.
Examples
The first example is codel-vs-pfifo-basic-test.cc located in src/traffic-control/examples. To run the
file (the first invocation below shows the available command-line options):
$ ./waf --run "codel-vs-pfifo-basic-test --PrintHelp"
$ ./waf --run "codel-vs-pfifo-basic-test --queueType=CoDel --pcapFileName=codel.pcap --cwndTrFileName=cwndCodel.tr"
The expected output from the previous commands are two files: codel.pcap file and cwndCoDel.tr (ASCII
trace) file The .pcap file can be analyzed using wireshark or tcptrace:
$ tcptrace -l -r -n -W codel.pcap
The second example is codel-vs-pfifo-asymmetric.cc located in src/traffic-control/examples. This example
is intended to model a typical cable modem deployment scenario. To run the file:
$ ./waf --run "codel-vs-pfifo-asymmetric --PrintHelp"
$ ./waf --run codel-vs-pfifo-asymmetric
The expected output from the previous commands is six pcap files:
• codel-vs-pfifo-asymmetric-CoDel-server-lan.pcap
• codel-vs-pfifo-asymmetric-CoDel-router-wan.pcap
• codel-vs-pfifo-asymmetric-CoDel-router-lan.pcap
• codel-vs-pfifo-asymmetric-CoDel-cmts-wan.pcap
• codel-vs-pfifo-asymmetric-CoDel-cmts-lan.pcap
• codel-vs-pfifo-asymmetric-CoDel-host-lan.pcap
One attribute file:
• codel-vs-pfifo-asymmetric-CoDel.attr
Five ASCII trace files:
• codel-vs-pfifo-asymmetric-CoDel-drop.tr
• codel-vs-pfifo-asymmetric-CoDel-drop-state.tr
• codel-vs-pfifo-asymmetric-CoDel-sojourn.tr
• codel-vs-pfifo-asymmetric-CoDel-length.tr
• codel-vs-pfifo-asymmetric-CoDel-cwnd.tr
Validation
The CoDel model is tested using CoDelQueueDiscTestSuite class defined in
src/traffic-control/test/codel-queue-test-suite.cc. The suite includes 5 test cases:
• Test 1: The first test checks the enqueue/dequeue with no drops and makes sure that CoDel attributes
can be set correctly.
• Test 2: The second test checks the enqueue with drops due to queue overflow.
• Test 3: The third test checks the NewtonStep() arithmetic against explicit port of Linux implementation
• Test 4: The fourth test checks the ControlLaw() against explicit port of Linux implementation
• Test 5: The fifth test checks the enqueue/dequeue with drops according to CoDel algorithm
• Test 6: The sixth test checks the enqueue/dequeue with marks according to CoDel algorithm
The test suite can be run using the following commands:
$ ./waf configure --enable-examples --enable-tests
$ ./waf build
$ ./test.py -s codel-queue-disc
or
$ NS_LOG="CoDelQueueDisc" ./waf --run "test-runner --suite=codel-queue-disc"
FqCoDel queue disc
This chapter describes the FqCoDel ([Hoe16]) queue disc implementation in ns-3.
The FlowQueue-CoDel (FQ-CoDel) algorithm is a combined packet scheduler and Active Queue Management (AQM)
algorithm developed as part of the bufferbloat-fighting community effort ([Buf16]). FqCoDel classifies
incoming packets into different queues (by default, 1024 queues are created), which are served according
to a modified Deficit Round Robin (DRR) queue scheduler. Each queue is managed by the CoDel AQM
algorithm. FqCoDel distinguishes between “new” queues (which don’t build up a standing queue) and “old”
queues, that have queued enough data to be around for more than one iteration of the round-robin
scheduler.
FqCoDel is installed by default on single-queue NetDevices (such as PointToPoint, Csma and Simple). Also,
on multi-queue devices (such as Wifi), the default root qdisc is Mq with as many FqCoDel child queue
discs as the number of device queues.
Model Description
The source code for the FqCoDel queue disc is located in the directory src/traffic-control/model and
consists of 2 files fq-codel-queue-disc.h and fq-codel-queue-disc.cc defining a FqCoDelQueueDisc class
and a helper FqCoDelFlow class. The code was ported to ns-3 based on Linux kernel code implemented by
Eric Dumazet. Set associative hashing is also based on the Linux kernel CAKE queue management code. Set
associative hashing is used to reduce the number of hash collisions in comparison to choosing queues
normally with a simple hash. For a given number of queues, set associative hashing has fewer collisions
than a traditional hash, as long as the number of flows is lesser than the number of queues.
Essentially, it makes the queue management system more efficient. Set associative hashing is a vital
component of CAKE, which is another popular flow management algorithm that is implemented in Linux and is
being tested for FqCoDel. Furthermore, this module can be directly used with CAKE when its other
components are implemented in ns-3. The only changes needed to incorporate this new hashing scheme are in
the SetAssociativeHash and DoEnqueue methods, as described below.
• class FqCoDelQueueDisc: This class implements the main FqCoDel algorithm:
• FqCoDelQueueDisc::DoEnqueue (): If no packet filter has been configured, this routine calls the
QueueDiscItem::Hash() method to classify the given packet into an appropriate queue. Otherwise, the
configured filters are used to classify the packet. If the filters are unable to classify the packet,
the packet is dropped. Otherwise, an option is provided if set associative hashing is to be used.The
packet is now handed over to the CoDel algorithm for timestamping. Then, if the queue is not
currently active (i.e., if it is not in either the list of new or the list of old queues), it is
added to the end of the list of new queues, and its deficit is initiated to the configured quantum.
Otherwise, the queue is left in its current queue list. Finally, the total number of enqueued
packets is compared with the configured limit, and if it is above this value (which can happen since
a packet was just enqueued), packets are dropped from the head of the queue with the largest current
byte count until the number of dropped packets reaches the configured drop batch size or the backlog
of the queue has been halved. Note that this in most cases means that the packet that was just
enqueued is not among the packets that get dropped, which may even be from a different queue.
• FqCoDelQueueDisc::SetAssociativeHash(): An outer hash is identified for the given packet. This
corresponds to the set into which the packet is to be enqueued. A set consists of a group of queues.
The set determined by outer hash is enumerated; if a queue corresponding to this packet’s flow is
found (we use per-queue tags to achieve this), or in case of an inactive queue, or if a new queue can
be created for this set without exceeding the maximum limit, the index of this queue is returned.
Otherwise, all queues of this full set are active and correspond to flows different from the current
packet’s flow. In such cases, the index of first queue of this set is returned. We don’t consider
creating new queues for the packet in these cases, since this approach may waste resources in the
long run. The situation highlighted is a guaranteed collision and cannot be avoided without
increasing the overall number of queues.
• FqCoDelQueueDisc::DoDequeue (): The first task performed by this routine is selecting a queue from
which to dequeue a packet. To this end, the scheduler first looks at the list of new queues; for the
queue at the head of that list, if that queue has a negative deficit (i.e., it has already dequeued
at least a quantum of bytes), it is given an additional amount of deficit, the queue is put onto the
end of the list of old queues, and the routine selects the next queue and starts again. Otherwise,
that queue is selected for dequeue. If the list of new queues is empty, the scheduler proceeds down
the list of old queues in the same fashion (checking the deficit, and either selecting the queue for
dequeuing, or increasing deficit and putting the queue back at the end of the list). After having
selected a queue from which to dequeue a packet, the CoDel algorithm is invoked on that queue. As a
result of this, one or more packets may be discarded from the head of the selected queue, before the
packet that should be dequeued is returned (or nothing is returned if the queue is or becomes empty
while being handled by the CoDel algorithm). Finally, if the CoDel algorithm does not return a
packet, then the queue must be empty, and the scheduler does one of two things: if the queue selected
for dequeue came from the list of new queues, it is moved to the end of the list of old queues. If
instead it came from the list of old queues, that queue is removed from the list, to be added back
(as a new queue) the next time a packet for that queue arrives. Then (since no packet was available
for dequeue), the whole dequeue process is restarted from the beginning. If, instead, the scheduler
did get a packet back from the CoDel algorithm, it subtracts the size of the packet from the byte
deficit for the selected queue and returns the packet as the result of the dequeue operation.
• FqCoDelQueueDisc::FqCoDelDrop (): This routine is invoked by FqCoDelQueueDisc::DoEnqueue() to drop
packets from the head of the queue with the largest current byte count. This routine keeps dropping
packets until the number of dropped packets reaches the configured drop batch size or the backlog of
the queue has been halved.
• class FqCoDelFlow: This class implements a flow queue, by keeping its current status (whether it is in
the list of new queues, in the list of old queues or inactive) and its current deficit.
In Linux, by default, packet classification is done by hashing (using a Jenkins hash function) the
5-tuple of IP protocol, source and destination IP addresses and port numbers (if they exist). This value
modulo the number of queues is salted by a random value selected at initialization time, to prevent
possible DoS attacks if the hash is predictable ahead of time. Alternatively, any other packet filter can
be configured. In ns-3, packet classification is performed in the same way as in Linux. Neither
internal queues nor classes can be configured for an FqCoDel queue disc.
Possible next steps
• what to do if ECT(1) and either/both ECT(0) and NotECT are in the same flow queue (hash collisions or
tunnels)– our L4S traffic flows will avoid this situation by supporting AccECN and ECN++ (and if it
happens in practice, the CoDel logic will just apply two separate thresholds)
• adding a ramp marking response instead of step threshold
• adding a floor value (to suppress marks if the queue length is below a certain number of bytes or
packets)
• adding a heuristic such as in PIE to avoid marking a packet if it arrived to an empty flow queue (check
on ingress, remember at egress time)
References
[Hoe16]
T. Hoeiland-Joergensen, P. McKenney, D. Taht, J. Gettys and E. Dumazet, The FlowQueue-CoDel Packet
Scheduler and Active Queue Management Algorithm, IETF draft. Available online at
https://tools.ietf.org/html/draft-ietf-aqm-fq-codel
[Buf16]
Bufferbloat.net. Available online at http://www.bufferbloat.net/.
Attributes
The key attributes that the FqCoDelQueue class holds include the following:
• UseEcn: True to use ECN (packets are marked instead of being dropped)
• Interval: The interval parameter to be used on the CoDel queues. The default value is 100 ms.
• Target: The target parameter to be used on the CoDel queues. The default value is 5 ms.
• MaxSize: The limit on the maximum number of packets stored by FqCoDel.
• Flows: The number of flow queues managed by FqCoDel.
• DropBatchSize: The maximum number of packets dropped from the fat flow.
• Perturbation: The salt used as an additional input to the hash function used to classify packets.
• CeThreshold The FqCoDel CE threshold for marking packets
• UseL4s True to use L4S (only ECT1 packets are marked at CE threshold)
• EnableSetAssociativeHash: The parameter used to enable set associative hash.
Perturbation is an optional configuration attribute and can be used to generate different hash outcomes
for different inputs. For instance, the tuples used as input to the hash may cause hash collisions
(mapping to the same bucket) for a given set of inputs, but by changing the perturbation value, the same
hash inputs now map to distinct buckets.
Note that the quantum, i.e., the number of bytes each queue gets to dequeue on each round of the
scheduling algorithm, is set by default to the MTU size of the device (at initialisation time). The
FqCoDelQueueDisc::SetQuantum () method can be used (at any time) to configure a different value.
Examples
A typical usage pattern is to create a traffic control helper and to configure type and attributes of
queue disc and filters from the helper. For example, FqCodel can be configured as follows:
TrafficControlHelper tch;
tch.SetRootQueueDisc ("ns3::FqCoDelQueueDisc", "DropBatchSize", UintegerValue (1)
"Perturbation", UintegerValue (256));
QueueDiscContainer qdiscs = tch.Install (devices);
The example for FqCoDel’s L4S mode is FqCoDel-L4S-example.cc located in src/traffic-control/examples. To
run the file (the first invocation below shows the available command-line options):
$ ./waf --run "FqCoDel-L4S-example --PrintHelp"
$ ./waf --run "FqCoDel-L4S-example --scenarioNum=5"
The expected output from the previous command are .dat files.
Validation
The FqCoDel model is tested using FqCoDelQueueDiscTestSuite class defined in
src/test/ns3tc/codel-queue-test-suite.cc. The suite includes 5 test cases:
• Test 1: The first test checks that packets that cannot be classified by any available filter are
dropped.
• Test 2: The second test checks that IPv4 packets having distinct destination addresses are enqueued
into different flow queues. Also, it checks that packets are dropped from the fat flow in case the
queue disc capacity is exceeded.
• Test 3: The third test checks the dequeue operation and the deficit round robin-based scheduler.
• Test 4: The fourth test checks that TCP packets with distinct port numbers are enqueued into different
flow queues.
• Test 5: The fifth test checks that UDP packets with distinct port numbers are enqueued into different
flow queues.
• Test 6: The sixth test checks that the packets are marked correctly.
• Test 7: The seventh test checks the working of set associative hashing and its linear probing
capabilities by using TCP packets with different hashes enqueued into different sets and queues.
• Test 8: The eighth test checks the L4S mode of FqCoDel where ECT1 packets are marked at CE threshold
(target delay does not matter) while ECT0 packets continue to be marked at target delay (CE threshold
does not matter).
The test suite can be run using the following commands:
$ ./waf configure --enable-examples --enable-tests
$ ./waf build
$ ./test.py -s fq-codel-queue-disc
or:
$ NS_LOG="FqCoDelQueueDisc" ./waf --run "test-runner --suite=fq-codel-queue-disc"
Set associative hashing is tested by generating a probability collision graph. This graph is then
overlapped with the theoretical graph provided in the original CAKE paper (refer to Figure 1 from CAKE).
The generated graph is linked below: [image: Generated Collision Probability Graph] [image]
The overlapped graph is also linked below: [image: Overlapped Image with the graph from CAKE paper]
[image]
The steps to replicate this graph are available on this link.
Cobalt queue disc
This chapter describes the COBALT (CoDel BLUE Alternate) ([Cake16]) queue disc implementation in ns-3.
COBALT queue disc is an integral component of CAKE smart queue management system. It is a combination of
the CoDel ([Kath17]) and BLUE ([BLUE02]) Active Queue Management algorithms.
Model Description
The source code for the COBALT model is located in the directory src/traffic-control/model and consists
of 2 files: cobalt-queue-disc.h and cobalt-queue-disc.cc defining a CobaltQueueDisc class and a helper
CobaltTimestampTag class. The code was ported to ns-3 by Vignesh Kanan, Harsh Lara, Shefali Gupta,
Jendaipou Palmei and Mohit P. Tahiliani based on the Linux kernel code.
Stefano Avallone and Pasquale Imputato helped in verifying the correctness of COBALT model in ns-3 by
comparing the results obtained from it to those obtained from the Linux model of COBALT. A detailed
comparison of ns-3 model of COBALT with Linux model of COBALT is provided in ([Cobalt19]).
• class CobaltQueueDisc: This class implements the main Cobalt algorithm:
• CobaltQueueDisc::DoEnqueue (): This routine tags a packet with the current time before pushing it into
the queue. The timestamp tag is used by CobaltQueue::DoDequeue() to compute the packet’s sojourn time.
If the queue is full upon the packet arrival, this routine will drop the packet and record the number
of drops due to queue overflow, which is stored in m_stats.qLimDrop.
• CobaltQueueDisc::ShouldDrop (): This routine is CobaltQueueDisc::DoDequeue()’s helper routine that
determines whether a packet should be dropped or not based on its sojourn time. If L4S mode is enabled
then if the packet is ECT1 is checked and if delay is greater than CE threshold then the packet is
marked and returns false. If the sojourn time goes above m_target and remains above continuously for
at least m_interval, the routine returns true indicating that it is OK to drop the packet. Otherwise,
it returns ``false. If L4S mode is turned off and CE threshold marking is enabled, then if the delay
is greater than CE threshold, packet is marked. This routine decides if a packet should be dropped
based on the dropping state of CoDel and drop probability of BLUE. The idea is to have both algorithms
running in parallel and their effectiveness is decided by their respective parameters (Pdrop of BLUE
and dropping state of CoDel). If either of them decide to drop the packet, the packet is dropped.
• CobaltQueueDisc::DoDequeue (): This routine performs the actual packet drop based on
``CobaltQueueDisc::ShouldDrop ()’s return value and schedules the next drop. Cobalt will decrease
BLUE’s drop probability if the queue is empty. This will ensure that the queue does not underflow.
Otherwise Cobalt will take the next packet from the queue and calculate its drop state by running CoDel
and BLUE in parallel till there are none left to drop.
References
[Cake16]
Linux implementation of Cobalt as a part of the cake framework. Available online at
https://github.com/dtaht/sch_cake/blob/master/cobalt.c.
[Kath17]
Controlled Delay Active Queue Management (draft-ietf-aqm-fq-codel-07) Available online at
https://tools.ietf.org/html/draft-ietf-aqm-codel-07.
[BLUE02]
Feng, W. C., Shin, K. G., Kandlur, D. D., & Saha, D. (2002). The BLUE Active Queue Management
Algorithms. IEEE/ACM Transactions on Networking (ToN), 10(4), 513-528.
[Cobalt19]
Jendaipou Palmei, Shefali Gupta, Pasquale Imputato, Jonathan Morton, Mohit P. Tahiliani, Stefano
Avallone and Dave Taht (2019). Design and Evaluation of COBALT Queue Discipline. IEEE International
Symposium on Local and Metropolitan Area Networks (LANMAN), July 2019.
Attributes
The key attributes that the CobaltQueue Disc class holds include the following:
• MaxSize: The maximum number of packets/bytes accepted by this queue disc.
• Interval: The sliding-minimum window. The default value is 100 ms.
• Target: The Cobalt algorithm target queue delay. The default value is 5 ms.
• Pdrop: Value of drop probability.
• Increment: Increment value of drop probability. Default value is 1./256 .
• Decrement: Decrement value of drop probability. Default value is 1./4096 .
• CeThreshold: The CoDel CE threshold for marking packets.
• UseL4s: True to use L4S (only ECT1 packets are marked at CE threshold).
• Count: Cobalt count.
• DropState: Dropping state of Cobalt. Default value is false.
• Sojourn: Per packet time spent in the queue.
• DropNext: Time until next packet drop.
Examples
An example program named cobalt-vs-codel.cc is located in src/traffic-control/examples. Use the following
command to run the program.
$ ./waf --run cobalt-vs-codel
Validation
The COBALT model is tested using CobaltQueueDiscTestSuite class defined in
src/traffic-control/test/cobalt-queue-test-suite.cc. The suite includes 2 test cases:
• Test 1: Simple enqueue/dequeue with no drops.
• Test 2: Change of BLUE’s drop probability upon queue full (Activation of Blue).
• Test 3: This test verfies ECN marking.
• Test 4: CE threshold marking test.
The test suite can be run using the following commands:
$ ./waf configure --enable-examples --enable-tests
$ ./waf build
$ ./test.py -s cobalt-queue-disc
or
$ NS_LOG="CobaltQueueDisc" ./waf --run "test-runner --suite=cobalt-queue-disc"
FqCobalt queue disc
This chapter describes the FqCobalt ([Pal19]) queue disc implementation in ns-3.
The FlowQueue-Cobalt (FQ-Cobalt) algorithm is similar to FlowQueue-CoDel (FQ-CoDel) algorithm available
in :sec-fq-codel. The documentation for Cobalt is available in
ns-3-dev/src/traffic-control/doc/cobalt.rst.
FqCobalt is one of the key components of the CAKE smart queue management framework ([Hoe18]). The
COBALT AQM is preferred to the CoDel AQM for CAKE because it adds a heuristic called BLUE to cover cases
in which the CoDel control law is too sluggish to respond to queue growth.
Model Description
The source code for the FqCobalt queue disc is located in the directory src/traffic-control/model and
consists of 2 files fq-cobalt-queue-disc.h and fq-cobalt-queue-disc.cc defining a FqCobaltQueueDisc class
and a helper FqCobaltFlow class. The code was ported to ns-3 based on Linux kernel code implemented by
Jonathan Morton (https://github.com/torvalds/linux/blob/master/net/sched/sch_cake.c).
The Model Description is similar to the FqCoDel documentation mentioned above.
References
[Pal19]
J. Palmei, S. Gupta, P. Imputato, J. Morton, M. Tahiliani, S. Avallone, and D. Taht, Design and
Evaluation of COBALT Queue Discipline, 2019 IEEE International Symposium on Local and Metropolitan
Area Networks (LANMAN), Paris, France, 2019.
[Hoe18]
T. Hoiland-Jørgensen, D. Taht and J. Morton, “Piece of CAKE: A Comprehensive Queue Management Solution
for Home Gateways,” 2018 IEEE International Symposium on Local and Metropolitan Area Networks
(LANMAN), Washington, DC, USA, 2018.
Attributes
Most of the key attributes are similar to the FqCoDel implementation mentioned above. One difference is
the absence of the MinBytes parameter.
Some additional parameters implemented as attributes are:
• Pdrop: Value of drop probability.
• Increment: Increment value of drop probability. Default value is 1./256 .
• Decrement: Decrement value of drop probability. Default value is 1./4096 .
• BlueThreshold: The threshold after which Blue is enabled. Default value is 400ms.
Note that if the user wants to disable Blue Enhancement then the user can set it to a large value; for
example, to Time::Max ().
Examples
A typical usage pattern is to create a traffic control helper and to configure the type and attributes of
the queue disc and filters from the helper. For example, FqCobalt can be configured as follows:
TrafficControlHelper tch;
tch.SetRootQueueDisc ("ns3::FqCobaltQueueDisc", "DropBatchSize", UintegerValue (1)
"Perturbation", UintegerValue (256));
QueueDiscContainer qdiscs = tch.Install (devices);
Validation
The FqCobalt model is tested using FqCobaltQueueDiscTestSuite class defined in
src/test/ns3tc/codel-queue-test-suite.cc.
The tests are similar to the ones for FqCoDel queue disc mentioned in first section of this document.
The test suite can be run using the following commands:
$ ./waf configure --enable-examples --enable-tests
$ ./waf build
$ ./test.py -s fq-cobalt-queue-disc
or:
$ NS_LOG="FqCobaltQueueDisc" ./waf --run "test-runner --suite=fq-cobalt-queue-disc"
PIE queue disc
This chapter describes the PIE ([Pan13], [Pan16]) queue disc implementation in ns-3.
Proportional Integral controller Enhanced (PIE) is a queuing discipline that aims to solve the
bufferbloat [Buf14] problem. The model in ns-3 is a port of Preethi Natarajan’s ns-2 PIE model.
Model Description
The source code for the PIE model is located in the directory src/traffic-control/model and consists of 2
files pie-queue-disc.h and pie-queue-disc.cc defining a PieQueueDisc class. The code was ported to ns-3
by Mohit P. Tahiliani, Shravya K. S. and Smriti Murali based on ns-2 code implemented by Preethi
Natarajan, Rong Pan, Chiara Piglione, Greg White and Takashi Hayakawa. The implementation was aligned
with RFC 8033 by Vivek Jain and Mohit P. Tahiliani for the ns-3.32 release, with additional unit test
cases contributed by Bhaskar Kataria.
• class PieQueueDisc: This class implements the main PIE algorithm:
• PieQueueDisc::DoEnqueue (): This routine checks whether the queue is full, and if so, drops the
packets and records the number of drops due to queue overflow. If queue is not full then if
ActiveThreshold is set then it checks if queue delay is higher than ActiveThreshold and if it is
then, this routine calls PieQueueDisc::DropEarly(), and depending on the value returned, the incoming
packet is either enqueued or dropped.
• PieQueueDisc::DropEarly (): The decision to enqueue or drop the packet is taken by invoking this
routine, which returns a boolean value; false indicates enqueue and true indicates drop.
• PieQueueDisc::CalculateP (): This routine is called at a regular interval of m_tUpdate and updates
the drop probability, which is required by PieQueueDisc::DropEarly()
• PieQueueDisc::DoDequeue (): This routine calculates queue delay using timestamps (by default) or,
optionally with the UseDequeRateEstimator attribute enabled, calculates the average departure rate to
estimate queue delay. A queue delay estimate required for updating the drop probability in
PieQueueDisc::CalculateP (). Starting with the ns-3.32 release, the default approach to calculate
queue delay has been changed to use timestamps.
References
[Pan13]
Pan, R., Natarajan, P., Piglione, C., Prabhu, M. S., Subramanian, V., Baker, F., & VerSteeg, B.
(2013, July). PIE: A lightweight control scheme to address the bufferbloat problem. In High
Performance Switching and Routing (HPSR), 2013 IEEE 14th International Conference on (pp. 148-155).
IEEE. Available online at https://www.ietf.org/mail-archive/web/iccrg/current/pdfB57AZSheOH.pdf.
[Pan16]
R. Pan, P. Natarajan, F. Baker, G. White, B. VerSteeg, M.S. Prabhu, C. Piglione, V. Subramanian,
Internet-Draft: PIE: A lightweight control scheme to address the bufferbloat problem, April 2016.
Available online at https://tools.ietf.org/html/draft-ietf-aqm-pie-07.
Attributes
The key attributes that the PieQueue class holds include the following:
• MaxSize: The maximum number of bytes or packets the queue can hold.
• MeanPktSize: Mean packet size in bytes. The default value is 1000 bytes.
• Tupdate: Time period to calculate drop probability. The default value is 30 ms.
• Supdate: Start time of the update timer. The default value is 0 ms.
• DequeueThreshold: Minimum queue size in bytes before dequeue rate is measured. The default value is
10000 bytes.
• QueueDelayReference: Desired queue delay. The default value is 20 ms.
• MaxBurstAllowance: Current max burst allowance in seconds before random drop. The default value is 0.1
seconds.
• A: Value of alpha. The default value is 0.125.
• B: Value of beta. The default value is 1.25.
• UseDequeueRateEstimator: Enable/Disable usage of Dequeue Rate Estimator (Default: false).
• UseEcn: True to use ECN. Packets are marked instead of being dropped (Default: false).
• MarkEcnThreshold: ECN marking threshold (Default: 10% as suggested in RFC 8033).
• UseDerandomization: Enable/Disable Derandomization feature mentioned in RFC 8033 (Default: false).
• UseCapDropAdjustment: Enable/Disable Cap Drop Adjustment feature mentioned in RFC 8033 (Default: true).
• ActiveThreshold: Threshold for activating PIE (disabled by default).
Examples
The example for PIE is pie-example.cc located in src/traffic-control/examples. To run the file (the
first invocation below shows the available command-line options):
$ ./waf --run "pie-example --PrintHelp"
$ ./waf --run "pie-example --writePcap=1"
The expected output from the previous commands are ten .pcap files.
Validation
The PIE model is tested using PieQueueDiscTestSuite class defined in
src/traffic-control/test/pie-queue-test-suite.cc. The suite includes the following test cases:
• Test 1: simple enqueue/dequeue with defaults, no drops
• Test 2: more data with defaults, unforced drops but no forced drops
• Test 3: same as test 2, but with higher QueueDelayReference
• Test 4: same as test 2, but with reduced dequeue rate
• Test 5: same dequeue rate as test 4, but with higher Tupdate
• Test 6: same as test 2, but with UseDequeueRateEstimator enabled
• Test 7: test with CapDropAdjustment disabled
• Test 8: test with CapDropAdjustment enabled
• Test 9: PIE queue disc is ECN enabled, but packets are not ECN capable
• Test 10: Packets are ECN capable, but PIE queue disc is not ECN enabled
• Test 11: Packets and PIE queue disc both are ECN capable
• Test 12: test with Derandomization enabled
• Test 13: same as test 11 but with accumulated drop probability set below the low threshold
• Test 14: same as test 12 but with accumulated drop probability set above the high threshold
• Test 15: Tests Active/Inactive feature, ActiveThreshold set to a high value so PIE never starts.
• Test 16: Tests Active/Inactive feature, ActiveThreshold set to a low value so PIE starts early.
The test suite can be run using the following commands:
$ ./waf configure --enable-examples --enable-tests
$ ./waf build
$ ./test.py -s pie-queue-disc
or alternatively (to see logging statements in a debug build):
$ NS_LOG="PieQueueDisc" ./waf --run "test-runner --suite=pie-queue-disc"
FQ-PIE queue disc
This chapter describes the FQ-PIE ([Ram19]) queue disc implementation in ns-3.
The FQ-PIE queue disc combines the Proportional Integral Controller Enhanced (PIE) AQM algorithm with the
FlowQueue scheduler that is part of FQ-CoDel (also available in ns-3). FQ-PIE was introduced to Linux
kernel version 5.6.
Model Description
The source code for the FqPieQueueDisc is located in the directory src/traffic-control/model and consists
of 2 files fq-pie-queue-disc.h and fq-pie-queue-disc.cc defining a FqPieQueueDisc class and a helper
FqPieFlow class. The code was ported to ns-3 based on Linux kernel code implemented by Mohit P.
Tahiliani.
This model calculates drop probability independently in each flow queue. One difficulty, as pointed out
by [CableLabs14], is that PIE calculates drop probability based on the departure rate of a (flow) queue,
which may be more highly variable than the aggregate queue. An alternative, which CableLabs has called
SFQ-PIE, is to calculate an overall drop probability for the entire queue structure, and then scale this
drop probability based on the ratio of the queue depth of each flow queue compared with the depth of the
current largest queue. This ns-3 model does not implement the SFQ-PIE variant described by CableLabs.
References
[Ram19]
G. Ramakrishnan, M. Bhasi, V. Saicharan, L. Monis, S. D. Patil and M. P. Tahiliani, “FQ-PIE Queue
Discipline in the Linux Kernel: Design, Implementation and Challenges,” 2019 IEEE 44th LCN Symposium
on Emerging Topics in Networking (LCN Symposium), Osnabrueck, Germany, 2019, pp. 117-124,
[CableLabs14]
G. White, Active Queue Management in DOCSIS 3.X Cable Modems, CableLabs white paper, May 2014.
Attributes
The key attributes that the FqPieQueue class holds include the following. First, there are PIE-specific
attributes that are copied into the individual PIE flow queues:
• UseEcn: Whether to use ECN marking
• MarkEcnThreshold: ECN marking threshold (RFC 8033 suggests 0.1 (i.e., 10%) default).
• UseL4s: Whether to use L4S (only mark ECT1 packets at CE threshold)
• MeanPktSize: Constant used to roughly convert bytes to packets
• A: Alpha value in PIE algorithm drop probability calculation
• B: Beta value in PIE algorithm drop probability calculation
• Tupdate: Time period to calculate drop probability
• Supdate: Start time of the update timer
• DequeueThreshold: Minimum queue size in bytes before dequeue rate is measured
• QueueDelayReference: AQM latency target
• MaxBurstAllowance: AQM max burst allowance before random drop
• UseDequeueRateEstimator: Enable/Disable usage of Dequeue Rate Estimator
• UseCapDropAdjustment: Enable/Disable Cap Drop Adjustment feature mentioned in RFC 8033
• UseDerandomization: Enable/Disable Derandomization feature mentioned in RFC 8033
Second, there are QueueDisc level, or FQ-specific attributes:: * MaxSize: Maximum number of packets in
the queue disc * Flows: Maximum number of flow queues * DropBatchSize: Maximum number of packets dropped
from the fat flow * Perturbation: Salt value used as hash input when classifying flows *
EnableSetAssociativeHash: Enable or disable set associative hash * SetWays: Size of a set of queues in
set associative hash
Examples
A typical usage pattern is to create a traffic control helper and to configure the type and attributes of
queue disc and filters from the helper. For example, FqPIE can be configured as follows:
TrafficControlHelper tch;
tch.SetRootQueueDisc ("ns3::FqPieQueueDisc", "DropBatchSize", UintegerValue (1)
"Perturbation", UintegerValue (256));
QueueDiscContainer qdiscs = tch.Install (devices);
Validation
The FqPie model is tested using FqPieQueueDiscTestSuite class defined in
src/test/ns3tc/fq-pie-queue-test-suite.cc.
The tests are similar to the ones for FqCoDel queue disc mentioned in first section of this document.
The test suite can be run using the following commands:
$ ./waf configure --enable-examples --enable-tests
$ ./waf build
$ ./test.py -s fq-pie-queue-disc
or:
$ NS_LOG="FqPieQueueDisc" ./waf --run "test-runner --suite=fq-pie-queue-disc"
Mq queue disc
This chapter describes the mq queue disc implementation in ns-3.
mq is a classful multiqueue dummy scheduler developed to best fit the multiqueue traffic control API in
Linux. The mq scheduler presents device transmission queues as classes, allowing to attach different
queue discs to them, which are grafted to the device transmission queues.
Mq is installed by default on multi-queue devices (such as Wifi) with as many FqCodel child queue discs
as the number of device queues.
Model Description
mq is a multi-queue aware queue disc, meaning that it has as many child queue discs as the number of
device transmission queues. Each child queue disc maps to a distinct device transmission queue. Every
packet is enqueued into the child queue disc which maps to the device transmission queue in which the
device will enqueue the packet.
In ns-3, MqQueueDisc has a wake mode of WAKE_CHILD, which means that the traffic control layer enqueues
packets directly into one of the child queue discs (multi-queue devices can provide a callback to inform
the traffic control layer of the device transmission queue that will be selected for a given packet).
Therefore, MqQueueDisc::DoEnqueue () shall never be called (in fact, it raises a fatal error). Given
that dequeuing packets is triggered by enqueuing a packet in the queue disc or by the device invoking the
wake callback, it turns out that MqQueueDisc::DoDequeue () is never called as well (in fact, it raises a
fatal error, too).
The mq queue disc does not require packet filters, does not admit internal queues and must have as many
child queue discs as the number of device transmission queues.
Examples
A typical usage pattern is to create a traffic control helper used to add the required number of queue
disc classes, attach child queue discs to the classes and (if needed) add packet filters to the child
queue discs. The following code shows how to install an mq queue disc having FqCodel child queue discs:
TrafficControlHelper tch;
uint16_t handle = tch.SetRootQueueDisc ("ns3::MqQueueDisc");
TrafficControlHelper::ClassIdList cls = tch.AddQueueDiscClasses (handle, numTxQueues, "ns3::QueueDiscClass");
tch.AddChildQueueDiscs (handle, cls, "ns3::FqCoDelQueueDisc");
QueueDiscContainer qdiscs = tch.Install (devices);
Note that the child queue discs attached to the classes do not necessarily have to be of the same type.
Validation
The mq model is tested using WifiAcMappingTestSuite class defined in
src/test/wifi-ac-mapping-test-suite.cc. The suite considers a node with a QoS-enabled wifi device (which
has 4 transmission queues) and includes 4 test cases:
• Test 1: EF-marked packets are enqueued in the queue disc which maps to the AC_VI queue
• Test 2: AF11-marked packets are enqueued in the queue disc which maps to the AC_BK queue
• Test 3: AF32-marked packets are enqueued in the queue disc which maps to the AC_BE queue
• Test 4: CS7-marked packets are enqueued in the queue disc which maps to the AC_VO queue
The test suite can be run using the following commands:
$ ./waf configure --enable-examples --enable-tests
$ ./waf build
$ ./test.py -s ns3-wifi-ac-mapping
or
$ NS_LOG="WifiAcMappingTest" ./waf --run "test-runner --suite=ns3-wifi-ac-mapping"
UAN FRAMEWORK
The main goal of the UAN Framework is to enable researchers to model a variety of underwater network
scenarios. The UAN model is broken into four main parts: The channel, PHY, MAC and Autonomous
Underwater Vehicle (AUV) models.
The need for underwater wireless communications exists in applications such as remote control in offshore
oil industry [1], pollution monitoring in environmental systems, speech transmission between divers,
mapping of the ocean floor, mine counter measures [2] [4], seismic monitoring of ocean faults as well as
climate changes monitoring. Unfortunately, making on-field measurements is very expensive and there are
no commonly accepted standard to base on. Hence, the priority to make research work going on, it is to
realize a complete simulation framework that researchers can use to experiment, make tests and make
performance evaluation and comparison.
The NS-3 UAN module is a first step in this direction, trying to offer a reliable and realistic tool. In
fact, the UAN module offers accurate modelling of the underwater acoustic channel, a model of the WHOI
acoustic modem (one of the widely used acoustic modems) [6] and its communications performance, and some
MAC protocols.
Model Description
The source code for the UAN Framework lives in the directory src/uan and in src/energy for the
contribution on the li-ion battery model.
The UAN Framework is composed of two main parts:
• the AUV mobility models, including Electric motor propelled AUV (REMUS class [3] [4] ) and Seaglider
[5] models
• the energy models, including AUV energy models, AUV energy sources (batteries) and an acoustic modem
energy model
As enabling component for the energy models, a Li-Ion batteries energy source has been implemented basing
on [7] [8].
Design
UAN Propagation Models
Modelling of the underwater acoustic channel has been an active area of research for quite some time.
Given the complications involved, surface and bottom interactions, varying speed of sound, etc…, the
detailed models in use for ocean acoustics research are much too complex (in terms of runtime) for use in
network level simulations. We have attempted to provide the often used models as well as make an attempt
to bridge, in part, the gap between complicated ocean acoustic models and network level simulation. The
three propagation models included are the ideal channel model, the Thorp propagation model and the
Bellhop propagation model (Available as an addition).
All of the Propagation Models follow the same simple interface in ns3::UanPropModel. The propagation
models provide a power delay profile (PDP) and pathloss information. The PDP is retrieved using the
GetPdp method which returns type UanPdp. ns3::UanPdp utilises a tapped delay line model for the acoustic
channel. The UanPdp class is a container class for Taps, each tap has a delay and amplitude member
corresponding to the time of arrival (relative to the first tap arrival time) and amplitude. The
propagation model also provides pathloss between the source and receiver in dB re 1uPa. The PDP and
pathloss can then be used to find the received signal power over a duration of time (i.e. received signal
power in a symbol duration and ISI which interferes with neighbouring signals). Both UanPropModelIdeal
and UanPropModelThorp return a single impulse for a PDP.
a. Ideal Channel Model ns3::UanPropModelIdeal
The ideal channel model assumes 0 pathloss inside a cylindrical area with bounds set by attribute. The
ideal channel model also assumes an impulse PDP.
b. Thorp Propagation Model ns3::UanPropModelThorp
The Thorp Propagation Model calculates pathloss using the well-known Thorp approximation. This model is
similar to the underwater channel model implemented in ns2 as described here:
Harris, A. F. and Zorzi, M. 2007. Modeling the underwater acoustic channel in ns2. In Proceedings of the
2nd international Conference on Performance Evaluation Methodologies and Tools (Nantes, France, October
22 - 27, 2007). ValueTools, vol. 321. ICST (Institute for Computer Sciences Social-Informatics and
Telecommunications Engineering), ICST, Brussels, Belgium, 1-8.
The frequency used in calculation however, is the center frequency of the modulation as found from
ns3::UanTxMode. The Thorp Propagation Model also assumes an impulse channel response.
c. Bellhop Propagation Model ns3::UanPropModelBh (Available as an addition)
The Bellhop propagation model reads propagation information from a database. A configuration file
describing the location, and resolution of the archived information must be supplied via attributes. We
have included a utility, create-dat, which can create these data files using the Bellhop Acoustic Ray
Tracing software (http://oalib.hlsresearch.com/).
The create-dat utility requires a Bellhop installation to run. Bellhop takes environment information
about the channel, such as sound speed profile, surface height bottom type, water depth, and uses a
Gaussian ray tracing algorithm to determine propagation information. Arrivals from Bellhop are grouped
together into equal length taps (the arrivals in a tap duration are coherently summed). The maximum taps
are then aligned to take the same position in the PDP. The create-dat utility averages together several
runs and then normalizes the average such that the sum of all taps is 1. The same configuration file
used to create the data files using create-dat should be passed via attribute to the Bellhop Propagation
Model.
The Bellhop propagation model is available as a patch. The link address will be made available here when
it is posted online. Otherwise email lentracy@gmail.com for more information.
UAN PHY Model Overview
The PHY has been designed to allow for relatively easy extension to new networking scenarios. We feel
this is important as, to date, there has been no commonly accepted network level simulation model for
underwater networks. The lack of commonly accepted network simulation tools has resulted in a wide array
of simulators and models used to report results in literature. The lack of standardization makes
comparing results nearly impossible.
The main component of the PHY Model is the generic PHY class, ns3::UanPhyGen. The PHY class’s general
responsibility is to handle packet acquisition, error determination, and forwarding of successful packets
up to the MAC layer. The Generic PHY uses two models for determination of signal to noise ratio (SINR)
and packet error rate (PER). The combination of the PER and SINR models determine successful reception
of packets. The PHY model connects to the channel via a Transducer class. The Transducer class is
responsible for tracking all arriving packets and departing packets over the duration of the events. How
the PHY class and the PER and SINR models respond to packets is based on the “Mode” of the transmission
as described by the ns3::UanTxMode class.
When a MAC layer sends down a packet to the PHY for transmission it specifies a “mode number” to be used
for the transmission. The PHY class accepts, as an attribute, a list of supported modes. The mode
number corresponds to an index in the supported modes. The UanTxMode contains simple modulation
information and a unique string id. The generic PHY class will only acquire arriving packets which use a
mode which is in the supported modes list of the PHY. The mode along with received signal power, and
other pertinent attributes (e.g. possibly interfering packets and their modes) are passed to the SINR and
PER models for calculation of SINR and probability of error.
Several simple example PER and SINR models have been created. a) The PER models - Default (simple) PER
model (ns3::UanPhyPerGenDefault): The Default PER model tests the packet against a threshold and assumes
error (with prob. 1) if the SINR is below the threshold or success if the SINR is above the threshold -
Micromodem FH-FSK PER (ns3::UanPhyPerUmodem). The FH-FSK PER model calculates probability of error
assuming a rate 1/2 convolutional code with constraint length 9 and a CRC check capable of correcting up
to 1 bit error. This is similar to what is used in the receiver of the WHOI Micromodem.
b) SINR models - Default Model (ns3::UanPhyCalcSinrDefault), The default SINR model assumes that all
transmitted energy is captured at the receiver and that there is no ISI. Any received signal power from
interferes acts as additional ambient noise. - FH-FSK SINR Model (ns3::UanPhyCalcSinrFhFsk), The WHOI
Micromodem operating in FH-FSK mode uses a predetermined hopping pattern that is shared by all nodes in
the network. We model this by only including signal energy receiving within one symbol time (as given by
ns3::UanTxMode) in calculating the received signal power. A channel clearing time is given to the FH-FSK
SINR model via attribute. Any signal energy arriving in adjacent signals (after a symbol time and the
clearing time) is considered ISI and is treated as additional ambient noise. Interfering signal
arrivals inside a symbol time (any symbol time) is also counted as additional ambient noise - Frequency
filtered SINR (ns3::UanPhyCalcSinrDual). This SINR model calculates SINR in the same manner as the
default model. This model however only considers interference if there is an overlap in frequency of the
arriving packets as determined by UanTxMode.
In addition to the generic PHY a dual phy layer is also included (ns3::UanPhyDual). This wraps two
generic phy layers together to model a net device which includes two receivers. This was primarily
developed for UanMacRc, described in the next section.
UAN MAC Model Overview
Over the last several years there have been a myriad of underwater MAC proposals in the literature. We
have included three MAC protocols with this distribution: a) CW-MAC, a MAC protocol which uses a slotted
contention window similar in nature to the IEEE 802.11 DCF. Nodes have a constant contention window
measured in slot times (configured via attribute). If the channel is sensed busy, then nodes backoff by
randomly (uniform distribution) choose a slot to transmit in. The slot time durations are also
configured via attribute. This MAC was described in
Parrish N.; Tracy L.; Roy S. Arabshahi P.; and Fox, W., System Design Considerations for Undersea
Networks: Link and Multiple Access Protocols , IEEE Journal on Selected Areas in Communications (JSAC),
Special Issue on Underwater Wireless Communications and Networks, Dec. 2008.
b) RC-MAC (ns3::UanMacRc ns3::UanMacRcGw) a reservation channel protocol which dynamically divides the
available bandwidth into a data channel and a control channel. This MAC protocol assumes there is a
gateway node which all network traffic is destined for. The current implementation assumes a single
gateway and a single network neighborhood (a single hop network). RTS/CTS handshaking is used and time
is divided into cycles. Non-gateway nodes transmit RTS packets on the control channel in parallel to
data packet transmissions which were scheduled in the previous cycle at the start of a new cycle, the
gateway responds on the data channel with a CTS packet which includes packet transmission times of data
packets for received RTS packets in the previous cycle as well as bandwidth allocation information. At
the end of a cycle ACK packets are transmitted for received data packets.
When a publication is available it will be cited here.
c. Simple ALOHA (ns3::UanMacAloha) Nodes transmit at will.
AUV mobility models
The AUV mobility models have been designed as in the follows.
Use cases
The user will be able to:
• program the AUV to navigate over a path of waypoints
• control the velocity of the AUV
• control the depth of the AUV
• control the direction of the AUV
• control the pitch of the AUV
• tell the AUV to emerge or submerge to a specified depth
AUV mobility models design
Implement a model of the navigation of AUV. This involves implementing two classes modelling the two
major categories of AUVs: electric motor propelled (like REMUS class [3] [4]) and “sea gliders” [5]. The
classic AUVs are submarine-like devices, propelled by an electric motor linked with a propeller. Instead,
the “sea glider” class exploits small changes in its buoyancy that, in conjunction with wings, can
convert vertical motion to horizontal. So, a glider will reach a point into the water by describing a
“saw-tooth” movement. Modelling the AUV navigation, involves in considering a real-world AUV class thus,
taking into account maximum speed, directional capabilities, emerging and submerging times. Regarding
the sea gliders, it is modelled the characteristic saw-tooth movement, with AUV’s speed driven by
buoyancy and glide angle.
[image] AUV’s mobility model classes overview.UNINDENT
An ns3::AuvMobilityModel interface has been designed to give users a generic interface to access AUV’s
navigation functions. The AuvMobilityModel interface is implemented by the RemusMobilityModel and the
GliderMobilityModel classes. The AUV’s mobility models organization it is shown in AUV’s mobility model
classes overview. Both models use a constant velocity movement, thus the AuvMobilityModel interface
derives from the ConstantVelocityMobilityModel. The two classes hold the navigation parameters for the
two different AUVs, like maximum pitch angles, maximum operating depth, maximum and minimum speed
values. The Glider model holds also some extra parameters like maximum buoyancy values, and maximum and
minimum glide slopes. Both classes, RemusMobilityModel and GliderMobilityModel, handle also the AUV
power consumption, utilizing the relative power models. Has been modified the WaypointMobilityModel to
let it use a generic underlying ConstantVelocityModel to validate the waypoints and, to keep trace of
the node’s position. The default model is the classic ConstantVelocityModel but, for example in case of
REMUS mobility model, the user can install the AUV mobility model into the waypoint model and then
validating the waypoints against REMUS navigation constraints.
Energy models
The energy models have been designed as in the follows.
Use cases
The user will be able to:
• use a specific power profile for the acoustic modem
• use a specific energy model for the AUV
• trace the power consumption of AUV navigation, through AUV’s energy model
• trace the power consumption underwater acoustic communications, through acoustic modem power profile
We have integrated the Energy Model with the UAN module, to implement energy handling. We have
implemented a specific energy model for the two AUV classes and, an energy source for Lithium batteries.
This will be really useful for researchers to keep trace of the AUV operational life. We have
implemented also an acoustic modem power profile, to keep trace of its power consumption. This can be
used to compare protocols specific power performance. In order to use such power profile, the acoustic
transducer physical layer has been modified to use the modem power profile. We have decoupled the
physical layer from the transducer specific energy model, to let the users change the different energy
models without changing the physical layer.
AUV energy models
Basing on the Device Energy Model interface, it has been implemented a specific energy model for the two
AUV classes (REMUS and Seaglider). This models reproduce the AUV’s specific power consumption to give
users accurate information. This model can be naturally used to evaluates the AUV operating life, as well
as mission-related power consumption, etc. Have been developed two AUV energy models:
• GliderEnergyModel, computes the power consumption of the vehicle based on the current buoyancy value
and vertical speed [5]
• RemusEnergyModel, computes the power consumption of the vehicle based on the current speed, as it is
propelled by a brush-less electric motor
NOTE:
TODO extend a little bit
AUV energy sources
NOTE:
[TODO]
Acoustic modem energy model
Basing on the Device Energy Model interface, has been implemented a generic energy model for acoustic
modem. The model allows to trace four modem’s power-states: Sleep, Idle, Receiving, Transmitting. The
default parameters for the energy model are set to fit those of the WHOI \mu-modem. The class follows
pretty closely the RadioEnergyModel class as the transducer behaviour is pretty close to that of a Wi-Fi
radio.
The default power consumption values implemented into the model are as follows [6]:
┌────────────┬───────────────────┐
│Modem State │ Power Consumption │
├────────────┼───────────────────┤
│TX │ 50 W │
├────────────┼───────────────────┤
│RX │ 158 mW │
├────────────┼───────────────────┤
│Idle │ 158 mW │
├────────────┼───────────────────┤
│Sleep │ 5.8 mW │
└────────────┴───────────────────┘
UAN module energy modifications
The UAN module has been modified in order to utilize the implemented energy classes. Specifically, it has
been modified the physical layer of the UAN module. It Has been implemented an UpdatePowerConsumption
method that takes the modem’s state as parameter. It checks if an energy source is installed into the
node and, in case, it then use the AcousticModemEnergyModel to update the power consumption with the
current modem’s state. The modem power consumption’s update takes place whenever the modem changes its
state.
A user should take into account that, if the power consumption handling is enabled (if the node has an
energy source installed), all the communications processes will terminate whether the node depletes all
the energy source.
Li-Ion batteries model
A generic Li-Ion battery model has been implemented based on [7] [8]. The model can be fitted to any type
of Li-Ion battery simply changing the model’s parameters The default values are fitted for the Panasonic
CGR18650DA Li-Ion Battery [9]. [TODO insert figure] As shown in figure the model approximates very well
the Li-Ion cells. Regarding Seagliders, the batteries used into the AUV are Electrochem 3B36 Lithium /
Sulfuryl Chloride cells [10]. Also with this cell type, the model seems to approximates the different
discharge curves pretty well, as shown in the figure.
NOTE:
should I insert the li-ion model deatils here? I think it is better to put them into an Energy-related
chapter..
Scope and Limitations
The framework is designed to simulate AUV’s behaviour. We have modeled the navigation and power
consumption behaviour of REMUS class and Seaglider AUVs. The communications stack, associated with the
AUV, can be modified depending on simulation needs. Usually, the default underwater stack is being used,
composed of an half duplex acoustic modem, an Aloha MAC protocol and a generic physical layer.
Regarding the AUV energy consumption, the user should be aware that the level of accuracy differs for the
two classes:
• Seaglider, high level of accuracy, thanks to the availability of detailed information on AUV’s
components and behaviour [5] [10]. Have been modeled both the navigation power consumption and the Li
battery packs (according to [5]).
• REMUS, medium level of accuracy, due to the lack of publicly available information on AUV’s components.
We have approximated the power consumption of the AUV’s motor with a linear behaviour and, the energy
source uses an ideal model (BasicEnergySource) with a power capacity equal to that specified in [4].
Future Work
Some ideas could be :
• insert a data logging capability
• modify the framework to use sockets (enabling the possibility to use applications)
• introduce some more MAC protocols
• modify the physical layer to let it consider the Doppler spread (problematic in underwater
environments)
• introduce OFDM modulations
References
[1] BINGHAM, D.; DRAKE, T.; HILL, A.; LOTT, R.; The Application of Autonomous Underwater Vehicle (AUV)
Technology in the Oil Industry – Vision and Experiences, URL:
http://www.fig.net/pub/fig_2002/Ts4-4/TS4_4_bingham_etal.pdf
[2] AUVfest2008: Underwater mines; URL:
http://oceanexplorer.noaa.gov/explorations/08auvfest/background/mines/mines.html
[3] Hydroinc Products; URL: http://www.hydroidinc.com/products.html
[4] WHOI, Autonomous Underwater Vehicle, REMUS; URL: http://www.whoi.edu/page.do?pid=29856
[5] Eriksen, C.C., T.J. Osse, R.D. Light, T. Wen, T.W. Lehman, P.L. Sabin, J.W. Ballard, and A.M.
Chiodi. Seaglider: A Long-Range Autonomous Underwater Vehicle for Oceanographic Research, IEEE
Journal of Oceanic Engineering, 26, 4, October 2001. URL:
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=972073&userType=inst
[6] L. Freitag, M. Grund, I. Singh, J. Partan, P. Koski, K. Ball, and W. Hole, The whoi micro-modem: an
acoustic communications and navigation system for multiple platforms, In Proc. IEEE OCEANS05 Conf,
2005. URL: http://ieeexplore.ieee.org/iel5/10918/34367/01639901.pdf
[7] C. M. Shepherd, “Design of Primary and Secondary Cells - Part 3. Battery discharge equation,” U.S.
Naval Research Laboratory, 1963
[8] Tremblay, O.; Dessaint, L.-A.; Dekkiche, A.-I., “A Generic Battery Model for the Dynamic Simulation
of Hybrid Electric Vehicles,” Ecole de Technologie Superieure, Universite du Quebec, 2007 URL:
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4544139
[9] Panasonic CGR18650DA Datasheet, URL:
http://www.panasonic.com/industrial/includes/pdf/Panasonic_LiIon_CGR18650DA.pdf
[10] Electrochem 3B36 Datasheet, URL:
http://www.electrochem.com.cn/products/Primary/HighRate/CSC/3B36.pdf
Usage
The main way that users who write simulation scripts will typically interact with the UAN Framework is
through the helper API and through the publicly visible attributes of the model.
The helper API is defined in src/uan/helper/acoustic-modem-energy-model-helper.{cc,h} and in
/src/uan/helper/...{cc,h}.
The example folder src/uan/examples/ contain some basic code that shows how to set up and use the models.
further examples can be found into the Unit tests in src/uan/test/...cc
Examples
Examples of the Framework’s usage can be found into the examples folder. There are mobility related
examples and UAN related ones.
Mobility Model Examples
•
auv-energy-model:
In this example we show the basic usage of an AUV energy model. Specifically, we show how to
create a generic node, adding to it a basic energy source and consuming energy from the energy
source. In this example we show the basic usage of an AUV energy model.
The Seaglider AUV power consumption depends on buoyancy and vertical speed values, so we
simulate a 20 seconds movement at 0.3 m/s of vertical speed and 138g of buoyancy. Then a 20
seconds movement at 0.2 m/s of vertical speed and 138g of buoyancy and then a stop of 5 seconds.
The required energy will be drained by the model basing on the given buoyancy/speed values, from
the energy source installed onto the node. We finally register a callback to the
TotalEnergyConsumption traced value.
•
auv-mobility:
In this example we show how to use the AuvMobilityHelper to install an AUV mobility model into a
(set of) node. Then we make the AUV to submerge to a depth of 1000 meters. We then set a
callback function called on reaching of the target depth. The callback then makes the AUV to
emerge to water surface (0 meters). We set also a callback function called on reaching of the
target depth. The emerge callback then, stops the AUV.
During the whole navigation process, the AUV’s position is tracked by the TracePos function and
plotted into a Gnuplot graph.
•
waypoint-mobility:
We show how to use the WaypointMobilityModel with a non-standard ConstantVelocityMobilityModel.
We first create a waypoint model with an underlying RemusMobilityModel setting the mobility
trace with two waypoints. We then create a waypoint model with an underlying
GliderMobilityModel setting the waypoints separately with the AddWaypoint method. The AUV’s
position is printed out every seconds.
UAN Examples
•
li-ion-energy-source
In this simple example, we show how to create and drain energy from a LiIonEnergySource. We
make a series of discharge calls to the energy source class, with different current drain and
durations, until all the energy is depleted from the cell (i.e. the voltage of the cell goes
below the threshold level). Every 20 seconds we print out the actual cell voltage to verify
that it follows the discharge curve [9]. At the end of the example it is verified that after
the energy depletion call, the cell voltage is below the threshold voltage.
•
uan-energy-auv
This is a comprehensive example where all the project’s components are used. We setup two
nodes, one fixed surface gateway equipped with an acoustic modem and a moving Seaglider AUV with
an acoustic modem too. Using the waypoint mobility model with an underlying
GliderMobilityModel, we make the glider descend to -1000 meters and then emerge to the water
surface. The AUV sends a generic 17-bytes packet every 10 seconds during the navigation
process. The gateway receives the packets and stores the total bytes amount. At the end of the
simulation are shown the energy consumptions of the two nodes and the networking stats.
Helpers
In this section we give an overview of the available helpers and their behaviour.
AcousticModemEnergyModelHelper
This helper installs AcousticModemEnergyModel into UanNetDevice objects only. It requires an UanNetDevice
and an EnergySource as input objects.
The helper creates an AcousticModemEnergyModel with default parameters and associate it with the given
energy source. It configures an EnergyModelCallback and an EnergyDepletionCallback. The depletion
callback can be configured as a parameter.
AuvGliderHelper
Installs into a node (or set of nodes) the Seaglider’s features:
• waypoint model with underlying glider mobility model
• glider energy model
• glider energy source
• micro modem energy model
The glider mobility model is the GliderMobilityModel with default parameters. The glider energy model is
the GliderEnergyModel with default parameters.
Regarding the energy source, the Seaglider features two battery packs, one for motor power and one for
digital-analog power. Each pack is composed of 12 (10V) and 42 (24V) lithium chloride DD-cell batteries,
respectively [5]. The total power capacity is around 17.5 MJ (3.9 MJ + 13.6 MJ). In the original version
of the Seaglider there was 18 + 63 D-cell with a total power capacity of 10MJ.
The packs design is as follows:
• 10V - 3 in-series string x 4 strings = 12 cells - typical capacity ~100 Ah
• 24V - 7 in-series-strings x 6 strings = 42 cells - typical capacity ~150 Ah
Battery cells are Electrochem 3B36, with 3.6 V nominal voltage and 30.0 Ah nominal capacity. The 10V
battery pack is associated with the electronic devices, while the 24V one is associated with the pump
motor.
The micro modem energy model is the MicroModemEnergyModel with default parameters.
AuvRemusHelper
Install into a node (or set of nodes) the REMUS features:
• waypoint model with REMUS mobility model validation
• REMUS energy model
• REMUS energy source
• micro modem energy model
The REMUS mobility model is the RemusMobilityModel with default parameters. The REMUS energy model is
the RemusEnergyModel with default parameters.
Regarding the energy source, the REMUS features a rechargeable lithium ion battery pack rated 1.1 kWh @
27 V (40 Ah) in operating conditions (specifications from [3] and Hydroinc European salesman). Since
more detailed information about battery pack were not publicly available, the energy source used is a
BasicEnergySource.
The micro modem energy model is the MicroModemEnergyModel with default parameters.
Attributes
NOTE:
TODO
Tracing
NOTE:
TODO
Logging
NOTE:
TODO
Caveats
NOTE:
TODO
Validation
This model has been tested with three UNIT test:
• auv-energy-model
• auv-mobility
• li-ion-energy-source
Auv Energy Model
Includes test cases for single packet energy consumption, energy depletion, Glider and REMUS energy
consumption. The unit test can be found in src/uan/test/auv-energy-model-test.cc.
The single packet energy consumption test do the following:
• creates a two node network, one surface gateway and one fixed node at -500 m of depth
• install the acoustic communication stack with energy consumption support into the nodes
• a packet is sent from the underwater node to the gateway
• it is verified that both, the gateway and the fixed node, have consumed the expected amount of energy
from their sources
The energy depletion test do the following steps:
• create a node with an empty energy source
• try to send a packet
• verify that the energy depletion callback has been invoked
The Glider energy consumption test do the following:
• create a node with glider capabilities
• make the vehicle to move to a predetermined waypoint
• verify that the energy consumed for the navigation is correct, according to the glider specifications
The REMUS energy consumption test do the following:
• create a node with REMUS capabilities
• make the vehicle to move to a predetermined waypoint
• verify that the energy consumed for the navigation is correct, according to the REMUS specifications
Auv Mobility
Includes test cases for glider and REMUS mobility models. The unit test can be found in
src/uan/test/auv-mobility-test.cc.
• create a node with glider capabilities
• set a specified velocity vector and verify if the resulting buoyancy is the one that is supposed to be
• make the vehicle to submerge to a specified depth and verify if, at the end of the process the position
is the one that is supposed to be
• make the vehicle to emerge to a specified depth and verify if, at the end of the process the position
is the one that is supposed to be
• make the vehicle to navigate to a specified point, using direction, pitch and speed settings and,
verify if at the end of the process the position is the one that is supposed to be
• make the vehicle to navigate to a specified point, using a velocity vector and, verify if at the end of
the process the position is the one that is supposed to be
The REMUS mobility model test do the following: * create a node with glider capabilities * make the
vehicle to submerge to a specified depth and verify if, at the end of the process the position is the one
that is supposed to be * make the vehicle to emerge to a specified depth and verify if, at the end of the
process the position is the one that is supposed to be * make the vehicle to navigate to a specified
point, using direction, pitch and speed settings and, verify if at the end of the process the position is
the one that is supposed to be * make the vehicle to navigate to a specified point, using a velocity
vector and, verify if at the end of the process the position is the one that is supposed to be
Li-Ion Energy Source
Includes test case for Li-Ion energy source. The unit test can be found in
src/energy/test/li-ion-energy-source-test.cc.
The test case verify that after a well-known discharge time with constant current drain, the cell voltage
has followed the datasheet discharge curve [9].
WAVE MODELS
WAVE is a system architecture for wireless-based vehicular communications, specified by the IEEE. This
chapter documents available models for WAVE within ns-3. The focus is on the MAC layer and MAC extension
layer defined by [ieee80211p] and [ieee1609dot4].
Model Description
WAVE is an overall system architecture for vehicular communications. The standards for specifying WAVE
include a set of extensions to the IEEE 802.11 standard, found in IEEE Std 802.11p-2010 [ieee80211p], and
the IEEE 1609 standard set, consisting of four documents: resource manager: IEEE 1609.1 [ieee1609dot1],
security services: IEEE 1609.2 [ieee1609dot2], network and transport layer services: IEEE 1609.3
[ieee1609dot3], and multi-channel coordination: IEEE 1609.4 [ieee1609dot4]. Additionally, SAE standard
J2735 [saej2735] describes a Dedicated Short Range Communications (DSRC) application message set that
allows applications to transmit information using WAVE.
In ns-3, the focus of the wave module is on both the MAC layer and the multi-channel coordination layer.
The key design aspect of 802.11p-compilant MAC layer is that they allow communications outside the
context of a basic service set (BSS). The literature uses the acronym OCB to denote “outside the context
of a BSS”, and the class ns3::OcbWifiMac models this in ns-3. This MAC does not require any association
between devices (similar to an adhoc WiFi MAC). Many management frames will not be used, but when used,
the BSSID field needs to be set to a wildcard BSSID value. Management information is transmitted by what
is called a vendor specific action (VSA) frame. With these changes, the packet transmissions (for a
moving vehicle) can be fast with small delay in the MAC layer. Users can create IEEE802.11p-compliant
device (the object of the class ns3::WifiNetDevice associating with ns3::OcbWifiMac) .
The key design aspect of the WAVE-compilant MAC layer (including 802.11p MAC layer and 1609.4 MAC
extension layer) is that, based on OCB features, they provide devices with the capability of switching
between control and service channels, using a single radio or using multiple radios. Therefore devices
can communicate with others in single or multiple channels, which can support both safety related and
non-safety related service for vehicular environments.
At the physical layer, the biggest difference is the use of the 5.9 GHz band with a channel bandwidth of
10 MHz. These physical layer changes can make the wireless signal relatively more stable, without
degrading throughput too much (ranging from 3 Mbps to 27 Mbps).
The source code for the WAVE MAC models lives in the directory src/wave.
For better modeling WAVE and VANET, the WAVE models for high layers (mainly [ieee1609dot3] ) are planned
for a later patch.
Design
In ns-3, support for 802.11p involves the MAC and PHY layers. To use an 802.11p NetDevice,
ns3::Wifi80211pHelper is suggested.
In ns-3, support for WAVE involves the MAC, its MAC extension and PHY layers. To use a WAVE NetDevice,
ns3::WaveHelper is suggested.
MAC layer
The classes used to model the MAC layer are ns3::OrganizationIdentifier, ns3::VendorSpecificActionHeader
and ns3::OcbWifiMac.
The OrganizationIdentifier and VendorSpecificActionHeader are used to support the sending of a Vendor
Specific Action frame.
OcbWifiMac is very similar to AdhocWifiMac, with some modifications. The ns-3 AdhocWifiMac class is
implemented very close to the 802.11p OCB mode rather than a real 802.11 ad-hoc mode. The AdhocWifiMac
has no BSS context that is defined in 802.11 standard, so it will not take time to send beacons and to
authenticate, making its behavior similar to that of an OcbWifiMac.
1. SetBssid, GetBssid, SetSsid, GetSsid
These methods are related to 802.11 BSS context, and are unused in the OCB context.
2. SetLinkUpCallback, SetLinkDownCallback
WAVE device can send packets directly, so the WiFi link is never down.
3. SendVsc, AddReceiveVscCallback
WAVE management information shall be sent by vendor specific action frames, sent by the upper layer
1609.4 standard as WSA (WAVE Service Advertisement) packets or other vendor specific information.
4. SendTimingAdvertisement (not implemented)
Although Timing Advertisement is very important and specifically defined in 802.11p standard, it is
not useful in a simulation environment. Every node in ns-3 vehicular simulation is assumed to be
already time synchronized (perhaps by GPS).
5. ConfigureEdca
This method will allow the user to set EDCA parameters of WAVE channels including CCH ans SCHs. And
the OcbWifiMac itself also uses this method to configure default 802.11p EDCA parameters.
6. Wildcard BSSID
The Wildcard BSSID is set to “ff:ff:ff:ff:ff:ff”. As defined in IEEE 802.11-2007, a wildcard BSSID
shall not be used in the BSSID field except for management frames of subtype probe request. But Adhoc
mode of ns-3 simplifies this mechanism: when stations receive packets, they will be forwarded up to
the higher layer, regardless of BSSID. This process is very close to OCB mode as defined in
802.11p-2010, in which stations use the wildcard BSSID to allow the higher layer of other stations to
hear directly.
7. Enqueue, Receive
The most important methods are send and receive methods. According to the standard, we should filter
the frames that are not permitted. Thus here we just identify the frames we care about; the other
frames will be discarded.
MAC extension layer
Although 1609.4 is still in the MAC layer, the implementation approach for ns-3 does not do much
modification in the source code of the wifi module. Instead, if some feature is related to wifi MAC
classes, then a relevant subclass is defined; if some feature has no relation to wifi MAC classes, then a
new class will be defined. This approach was selected to be non-intrusive to the ns-3 wifi module. All of
these classes will be hosted in a ‘container’ class called ns3:: WaveNetDevice. This class is a subclass
inherting from ns3::NetDeivce, composed of the objects of ns3::ChannelScheduler, ns3::ChannelManager,
ns3::ChannelCoordinator and ns3::VsaManager classes to provide the features described in 1609.4 while
still containing the objects of ns3::OcbWifiMac and ns3::WifiPhy classes. Moreover, ns3::OcbWifiMac
class is further extended with support for IEEE 1609.4 associating with ns3::HigherLayerTxVectorTag and
ns3::WaveMacLow. The main work of the WaveNetDevice is to create objects, configure, check arguments and
provide new APIs for multiple channel operation as follows:
1. AddMac, GetMac and GetMacs
Different from ns3::WifiNetDevice, the WAVE device will have multiple internal MAC entities rather
than a single one. Each MAC entity is used to support each WAVE channel. Thus, when devices switch
from the current channel to the next channel in different channel intervals, the packets in the
internal queue will not be flushed and the current MAC entity will perform a suspend operation until
woken up in next appropriate channel interval.
2. AddPhy, GetPhy and GetPhys
Also in contrast to ns3::WifiNetDevice, the WAVE device here can allow more than one PHY entity,
which permits the use cases of of single-PHY devices or multiple-PHY devices.
3. SetChannelScheduler and GetChannelScheduler
How to deal with multiple MAC entities and PHY entities to assign channel access for different
requests from higher layer? IEEE 1609.4 [ieee1609dot4] does not seem to give a very clear and detailed
mechanism, deferring to the implementor. In this model, the class ns3::ChannelScheduler provides the
API and delegates to the subclasses to implement the virtual methods. In the current implementation,
the default assignment mechanism for channel access, called ns3::DefaultChannelScheduler, gives a
simple answer that only deals with multiple channel operation in the context of a single-PHY device.
If users define their own different assignment mechanisms such as in the context of two PHY entities,
they can easily reuse models using AddPhy and SetChannelScheduler methods to import a new assignment
mechanism.
4. SetChannelManager and GetChannelManager
class ns3::ChannelManager is a WAVE channel set which contains valid WAVE channel numbers. Moreover,
the tx information in this channel set such as data rate and tx power level is used for transmitting
management frames.
5. SetVsaManager and GetVsaManager
class ns3::VsaManager is used to deal with sending and receiving VSA frames. According to different
request parameters from the higher layer, this class may transmit VSA frames repeatedly in the
appropriate channel number and channel interval.
6. SetChannelCoordinator and GetChannelCoordinator
class ns3::ChannelCoordinator is used to deal with channel coordination. The WAVE device can be aware
of the channel interval at the current time or in the future. It can also notify listeners about
incoming channel coordination events. Generally this class is used in the case of assigning
alternating CCH and SCH access.
7. StartSch and StopSch
In contrast to the basic 802.11p device that allow transmission packets immediately after the device
is created, the WAVE device should assign channel access for sending packets. This method will call
class ns3::ChannelScheduler to assign radio resources for the relevant channel.
8. ChangeAddress
The WAVE device can support a change of address after devices are already initialized, which will
cause all of MAC entities reset their status.
9. CancelTx
The WAVE device can support a request to cancel all transmissions associated with the particular
category and channel number, which will reset the particular interval queue and drop all of the queued
packets in this queue.
10.
RegisterTxProfile and DeleteTxProfile
After channel access is assigned, we still cannot send IP-based (or other protocol) packets by the
Send () method. A tx profile should be registered to specify tx parameters before transmission.
11. StartVsa, StopVsa and SetWaveVsaCallback
These methods will call an object from class ns3::VsaManager to send and receive VSA frames.
Generally these methods are used by IEEE 1609.3 for WSA management information.
12. SendX
After channel access is assigned, we can send WSMP (or other protocol) packets via the SendX ()
method. We should specify the tx parameters for each packet, e.g. the channel number for transmit.
13. Send and SetReceiveCallback
This method is the abstract method defined in the parent class ns3::NetDevice, defined to allow the
sending of IP-based packets. The channel access should be already assigned and tx profile should be
registered, otherwise incoming packets from the higher layer will be discarded. No matter whether
packets are sent by Send method or SendX method, the received packets will be only be delivered to the
higher layer by the registered ReceiveCallback.
14. other methods from its parent class ns3::NetDevice
These methods are implemented very similar to the code in ns3::WifiNetDevice.
In the above numbered list, we can categorize the methods into three types: the first type, from 1 to 6
and also 14, is the configuration for modeling and creating a WAVE device; the second type, from 7 to 11,
is the management plane of the standard; and the third type, 12 and 13, is the data plane of the
standard.
Channel coordination
The class ns3::ChannelCoordinator defines the CCH Interval, SCH Interval and GuardInteval. Users can be
aware of which interval the current time or future time will be in. If channel access mode is assigned to
alternating CCH and SCH access, channel interval events will be notified repeatedly for class
ns3::ChannelCoordinator to switch channels. Current default values are for CCHI with 50ms interval, SCHI
with 50ms interval, and GuardI with 4ms interval. Users can change these values by configuring the class
attributes.
Channel routing
Channel routing service means different transmission approaches for WSMP data, IP datagram and management
information. For WSMP data, the SendX () method implements the service primitive MA-UNITDATAX, and users
can set transmission parameters for each individual packet. The parameters include channel number,
priority, data rate and tx power level (expiration time is not supported now). For IP datagrams, the
Send () method is a virtual method from ns3::NetDevice that implements the service primitive MA-UNITDATA.
Users should insert QoS tags into packets themselves if they want to use QoS. Moreover, a tx profile
should be registered before the Send method is called for transmit; the profile contains SCH number, data
rate, power level and adaptable mode. For management information, StartVsa method implements the service
primitive MLMEX-VSA. The tx information is already configured in ns3::ChannelManager, including data
rate, power level and adaptable mode.
Channel access assignment
The channel access assignment is done by class ns3::ChannelScheduler to assign ContinuousAccess,
ExtendedAccess and AlternatingAccess. Besides that, immediate access is achieved by enabling the
“immediate” parameter, in which case the request channel will be switched to immediately. However this
class is a virtual parent class. The current module provides a subclass ns3::DefaultChannelScheduler to
assign channel access in the context of a single-PHY device. In this subclass, if the channel access is
already assigned for another request, the next coming request will fail until the previous channel access
is released. Users can implement different assignment mechanisms to deal with multiple MAC entities and
multiple PHY entities by inheriting from parent class ns3::ChannelScheduler. An important point is that
channel access should be assigned before sending routing packets, otherwise the packets will be discard.
Vendor Specific Action frames
When users want to send VSA repeatedly by calling WaveNetDevice::StartVsa, VSA will be sent repeatedly by
ns3::VsaManager. It is worth noting that if the peer MAC address is a unicast address, the VSA can only
be transmitted once even there is a repeat request. The tx parameters for VSA management frames can be
obtained from the ns3::ChannelManager.
User priority and Multi-channel synchronization
Since wifi module has already implemented a QoS mechanism, the wave module reuses the mechanism; VSA
frames will be assigned the default value with the highest AC according to the standard.
Multiple-channel synchronization is very important in practice for devices without a local timing source.
However, in simulation, every node is supposed to have the same system clock, which could be provided by
GPS devices in a real environment, so this feature is not modelled in ns-3. During the guard interval,
the device can only be in receive state, except for the switch state when the device does channel
switching operation.
PHY layer
No modification or extension is made to the ns-3 PHY layer corresponding to this model. In the 802.11p
standard, the PHY layer wireless technology is still 80211a OFDM with a 10MHz channel width, so
Wifi80211pHelper will only allow the user to set the standard to WIFI_PHY_STANDARD_80211_10MHZ or
WIFI_PHY_STANDARD_80211_20MHZ, while WaveHelper will only support WIFI_PHY_STANDARD_80211_10MHZ. The
maximum station transmit power and maximum permitted EIRP defined in 802.11p is larger than that of WiFi,
so transmit range can normally become longer than the usual WiFi. However, this feature will not be
implemented. Users who want to obtain longer range should configure attributes “TxPowerStart”,
“TxPowerEnd” and “TxPowerLevels” of the YansWifiPhy class by themselves.
Scope and Limitations
1. Does the model involve vehicular mobility of some sort?
Vehicular networks involve not only communication protocols, but also a communication environment
including vehicular mobility and propagation loss models. Because of specific features of the latter, the
protocols need to change. The MAC layer model in this project just adapts MAC changes to vehicular
environment. However this model does not involve any vehicular mobility with time limit. While users
should understand that vehicular mobility is out of scope for the current WAVE module, they can use any
mobility model in ns-3. For example, users may use a ns3::RandomWaypointMobilityModel (RWP) for node
mobilty or may generate ns-2-style playback files using other third-party tools and then playback those
mobility traces using ns3::Ns2MobilityHelper.
2. Does this model use different propagation models?
Referring to the first issue, some more realistic propagation loss models for vehicualr environment are
suggested and welcome. Some existing propagation los models in ns-3 are also suitable. Normally, users
can use Friis, Two-Ray Ground, and Nakagami models. The ns3::VanetRoutingExample example defaults to
Two-Ray Ground propagation loss with no additional fading, although adding stochastic Nakagami-m fading
is parametrically supported.
3. Are there any vehicular application models to drive the code?
About vehicular application models, SAE J2375 depends on WAVE architecture and is an application message
set in US. Cooperative Awareness Messages (CAM) and Decentralized Environment Notification Messages
(DENM) can be sent Europe between network and application layer, and is very close to being an
application model. The BSM in J2375 [saej2735] and CAM send alert messages that every vehicle node will
sent periodically about its status information to cooperate with others. The ns3::VanetRoutingExample
example sets up a network of (vehicular) nodes that each broadcast BSMs at regular intervals and may
additionally attempt to route non-BSM data through the network using select IP-based routing protocols.
5. Why are there two kinds of NetDevice helpers?
The current module provides two helpers to create two kinds of NetDevice. The first is an object of
WifiNetDevice (802.11p device) which mainly contains class ns3::OcbWifiMac to enable OCB mode. The second
is an object of WaveNetDevice (WAVE device) which contains additional classes ns3::ChannelScheduler,
ns3::ChannelManager, ns3::ChannelCoordinator and ns3::VsaManager to support multi-channel operation mode.
The reason to provide a special 802.11p device helper is that, considering the fact that many researchers
are interested in routing protocols or other aspects of vehicular environment in a single channel
context, they need neither multi-channel operation nor WAVE architectures. Besides that, the European
standard may also reuse an 802.11p device in an modified ITS-G5 implementation (maybe called
ItsG5NetDevice). Hence, the model supports configuration of both types of devices.
References
[ieee80211p]
IEEE Std 802.11p-2010 “IEEE Standard for Information technology– Local and metropolitan area
networks– Specific requirements– Part 11: Wireless LAN Medium Access Control (MAC) and Physical
Layer (PHY) Specifications Amendment 6: Wireless Access in Vehicular Environments”
[ieee1609dot1]
IEEE Std 1609.1-2010 “IEEE Standard for Wireless Access in Vehicular Environments (WAVE) - Resource
Manager, 2010”
[ieee1609dot2]
IEEE Std 1609.2-2010 “IEEE Standard for Wireless Access in Vehicular Environments (WAVE) - Security
Services for Applications and Management Messages, 2010”
[ieee1609dot3]
IEEE Std 1609.3-2010 “IEEE Standard for Wireless Access in Vehicular Environments (WAVE) -
Networking Services, 2010”
[ieee1609dot4]
IEEE Std 1609.4-2010 “IEEE Standard for Wireless Access in Vehicular Environments (WAVE) -
Multi-Channel Operation, 2010”
[saej2735]
SAE Std J2735 “J2735 dedicated short range communications (DSRC) message set dictionary. 2009”
Usage
Helpers
The helpers include a) lower-level MAC and PHY channel helpers and b) higher-level application helpers
that handle the sending and receiving of the Basic Safety Message (BSM).
The lower-level helpers include ns3::YansWavePhyHelper, ns3::NqosWaveMacHelper, ns3::QosWaveMacHelper,
ns3::Wifi80211pHelper and ns3::WaveHelper.
Wifi80211pHelper is used to create 802.11p devices that follow the 802.11p-2010 standard. WaveHelper is
used to create WAVE devices that follow both 802.11p-2010 and 1609.4-2010 standards which are the MAC and
PHY layers of the WAVE architecture.
The relation of ns3::NqosWaveMacHelper, ns3::QosWaveMacHelper and ns3::Wifi80211pHelper is described as
below:
WifiHelper ------------use--------------> WifiMacHelper
^ ^ ^
| | |
| | |
inherit inherit inherit
| | |
Wifi80211pHelper ------use-----> QosWaveMacHelper or NqosWaveHelper
From the above diagram, there are two Mac helper classes that both inherit from the WifiMacHelper; when
the WAVE module was originally written, there were specialized versions (QoS and Nqos) of WifiMacHelper
that have since been removed from the Wifi codebase, but the distinction remains in the WAVE helpers.
The functions of WiFi 802.11p device can be achieved by WaveNetDevice’s ContinuousAccess assignment,
Wifi80211pHelper is recommended if there is no need for multiple channel operation. Usage is as follows:
NodeContainer nodes;
NetDeviceContainer devices;
nodes.Create (2);
YansWifiPhyHelper wifiPhy = YansWifiPhyHelper::Default ();
YansWifiChannelHelper wifiChannel = YansWifiChannelHelper::Default ();
wifiPhy.SetChannel (wifiChannel.Create ());
NqosWave80211pMacHelper wifi80211pMac = NqosWaveMacHelper::Default();
Wifi80211pHelper 80211pHelper = Wifi80211pHelper::Default ();
devices = 80211pHelper.Install (wifiPhy, wifi80211pMac, nodes);
The relation of ns3::YansWavePhyHelper, ns3::QosWaveMacHelper and ns3::WaveHelper is described as below:
WifiMacHelper
^
|
inherit
|
WaveHelper -------- only use --------> QosWaveMacHelper
From the above diagram, WaveHelper is not the subclass of WifiHelper and should only use QosWaveMacHelper
because WAVE MAC layer is based on QoS mechanism. But the WaveHelper is recommended if there is a need
for multiple channel operation. Usage is as follows:
NodeContainer nodes;
NetDeviceContainer devices;
nodes.Create (2);
YansWifiChannelHelper wifiChannel = YansWifiChannelHelper::Default ();
YansWavePhyHelper wavePhy = YansWavePhyHelper::Default ();
wavePhy.SetChannel (wifiChannel.Create ());
QosWaveMacHelper waveMac = QosWaveMacHelper::Default ();
WaveHelper waveHelper = WaveHelper::Default ();
devices = waveHelper.Install (wavePhy, waveMac, nodes);
The higher-level helpers include ns3::WaveBsmStats and ns3::WaveBsmHelper.
WaveBsmStats is used to collect and manage statistics, such as packet and byte counts and Packet Delivery
Ratio (PDR), regarding the sending and receiving of WAVE BSM packets. WaveBsmHelper is used by
applications that wish to send and receive BSMs.
The relation of ns3::WaveBsmHelper and WaveBsmStats is described below:
<Your Vanet Routing Application> ----use----> WaveBsmHelper ----use----> WaveBsmStats
From <Your Vanet Routing Application>, usage is as follows:
// declare WAVE BSM helper instance WaveBsmHelper m_waveBsmHelper;
// the following are passed to the WaveBsmHelpe::Install() // method, and they are thus assumed to be
created and // initialized themselves, based on the user’s // simulation setup criteria. // container
of network node NodeContainer m_adhocTxNodes; // (transmitting) devices (1 per node)
NetDeviceContainer m_adhocTxDevices; // IPv4 interfaces (1 per device) Ipv4InterfaceContainer
m_adhocTxInterfaces; // total simulation time (in seconds) double m_TotalSimTime; // WAVE BSM
broadcast interval. E.g., 100ms = 0.1 seconds double m_waveInterval; // seconds //
time-synchronization accuracy of GPS devices. E.g., +/- 40ns double m_gpsAccuracyNs; // array of
distances (m) at which safety PDR shall be determined, // e.g. 50m, 100m, 200m, 300m, 400m, 500m,
600m, 800m, 1000m, and 1500m std::vector <double> m_txSafetyRanges; // used to get consistent random
numbers across scenarios int64_t m_streamIndex;
m_waveBsmHelper.Install (m_adhocTxNodes,
m_adhocTxDevices, m_adhocTxInterfaces, Seconds(m_TotalSimTime), m_wavePacketSize,
Seconds(m_waveInterval), // convert GPS accuracy, in ns, to Time Seconds(m_gpsAccuracyNs /
1000000.0), m_txSafetyRanges);
// fix random number streams m_streamIndex += m_waveBsmHelper.AssignStreams (m_streamIndex);
Example usages of BSM statistics are as follows:
// Get the cumulative PDR of the first safety Tx range (i.e, 50m in the // m_txSafetyRanges example
above). double bsm_pdr1 = m_waveBsmHelper.GetWaveBsmStats ()->GetBsmPdr (1);
// total WAVE BSM bytes sent uint32_t cumulativeWaveBsmBytes = m_waveBsmHelper.GetWaveBsmStats
()->GetTxByteCount ();
// get number of WAVE BSM packets sent int wavePktsSent = m_waveBsmHelper.GetWaveBsmStats
()->GetTxPktCount ();
// get number of WAVE BSM packets received int wavePktsReceived = m_waveBsmHelper.GetWaveBsmStats
()->GetRxPktCount ();
// reset count of WAVE BSM packets received m_waveBsmHelper.GetWaveBsmStats ()->SetRxPktCount (0);
// reset count of WAVE BSM packets sent m_waveBsmHelper.GetWaveBsmStats ()->SetTxPktCount (0);
// indicate that a node (nodeId) is moving. (set to 0 to “stop” node)
WaveBsmHelper::GetNodesMoving()[nodeId] = 1;
APIs
MAC layer
The 802.11p device can allow the upper layer to send different information over Vendor Specific Action
management frames by using different OrganizationIdentifier fields to identify differences.
1. create some Node objects and WifiNetDevice objects, e.g. one sender and one receiver.
2. receiver defines an OrganizationIdentifier
uint8_t oi_bytes[5] = {0x00, 0x50, 0xC2, 0x4A, 0x40};
OrganizationIdentifier oi(oi_bytes,5);
3. receiver defines a Callback for the defined OrganizationIdentifier
VscCallback vsccall = MakeCallback (&VsaExample::GetWsaAndOi, this);
4. receiver registers this identifier and function
Ptr<WifiNetDevice> device1 = DynamicCast<WifiNetDevice>(nodes.Get (i)->GetDevice (0));
Ptr<OcbWifiMac> ocb1 = DynamicCast<OcbWifiMac>(device->GetMac ());
ocb1->AddReceiveVscCallback (oi, vsccall);
5. sender transmits management information over VSA frames
Ptr<Packet> vsc = Create<Packet> ();
ocb2->SendVsc (vsc, Mac48Address::GetBroadcast (), m_16093oi);
6. then registered callbacks in the receiver will be called.
MAC extension layer
The WAVE devices allow the upper layer to route packets in different control approaches. However
dedicated APIs and invocation sequences should be followed; otherwise, the packets may be discarded by
devices.
1. create some Node objects and WaveNetDevice objects by helpers, e.g. one sender and one receiver.
2. receiver registers the receive callback if WSMP and IP-based packets are supposed to be received.
// the class ``ns3::WaveNetDeviceExample``here will has a receive method "Receive" to be registered.
receiver->SetReceiveCallback (MakeCallback (&WaveNetDeviceExample::Receive, this));
3. receiver registers the receive callback if WSA frames are supposed to be received.
// the class ``ns3::WaveNetDeviceExample``here will has a receive method "ReceiveVsa" to be registered.
receiver->SetWaveVsaCallback (MakeCallback (&WaveNetDeviceExample::ReceiveVsa, this));
4. sender and receiver assign channel access by StartSch method.
// in this case that alternating access with non-immediate mode is assigned for sender and receiver devices.
const SchInfo schInfo = SchInfo (SCH1, false, EXTENDED_ALTERNATING);
Simulator::Schedule (Seconds (0.0), &WaveNetDevice::StartSch, sender, schInfo);
Simulator::Schedule (Seconds (0.0), &WaveNetDevice::StartSch, receiver, schInfo);
or
// in this case that continuous access with immediate mode is assigned for sender and receiver devices.
const SchInfo schInfo = SchInfo (SCH1, true, EXTENDED_CONTINUOUS);
Simulator::Schedule (Seconds (0.0), &WaveNetDevice::StartSch, sender, schInfo);
Simulator::Schedule (Seconds (0.0), &WaveNetDevice::StartSch, receiver, schInfo)
or
// in this case that extended access with non-immediate mode is assigned for sender and receiver devices.
const SchInfo schInfo = SchInfo (SCH1, false, 100);
Simulator::Schedule (Seconds (0.0), &WaveNetDevice::StartSch, sender, schInfo);
Simulator::Schedule (Seconds (0.0), &WaveNetDevice::StartSch, receiver, schInfo)
5. sender registers a tx profile if IP-based packets are planned to be transmitted
// the IP-based packets will be transmitted in SCH1 with 6Mbps and 4 txPowerLevel with adaptable mode.
const TxProfile txProfile = TxProfile (SCH1, true, 4, WifiMode("OfdmRate6MbpsBW10MHz"));
Simulator::Schedule (Seconds (2.0), &WaveNetDevice::RegisterTxProfile, sender, txProfile);
6. sender transmits WSMP packets by SendX method.
// the data rate and txPowerLevel is controlled by the high layer which are 6Mbps and 0 level here.
const TxInfo txInfo = TxInfo (CCH, 7, WifiMode("OfdmRate6MbpsBW10MHz"), 0);
// this packet will contain WSMP header when IEEE 1609.3 model is implemented
const static uint16_t WSMP_PROT_NUMBER = 0x88DC;
Ptr<Packet> wsaPacket = Create<Packet> (100);
const Address dest = receiver->GetAddress ();
Simulator::Schedule (Seconds (2.0), &WaveNetDevice::SendX, sender, wsaPacket, dest, WSMP_PROT_NUMBER, txInfo);
or
// the data rate and txPowerLevel is controlled by the MAC layer which are decided by WifiRemoteStationManager
const TxInfo txInfo = TxInfo (CCH, 7, WifiMode(), 8);
// this packet will contain WSMP header when IEEE 1609.3 model is implemented
const static uint16_t WSMP_PROT_NUMBER = 0x88DC;
Ptr<Packet> wsaPacket = Create<Packet> (100);
const Address dest = receiver->GetAddress ();
Simulator::Schedule (Seconds (2.0), &WaveNetDevice::SendX, sender, wsaPacket, dest, WSMP_PROT_NUMBER, txInfo);
7. sender transmits IP-based packets by Send method.
const static uint16_t IPv6_PROT_NUMBER = 0x86DD;
Ptr<Packet> packet = Create<Packet> (100);
const Address dest = receiver->GetAddress ();
Simulator::Schedule (Seconds (2.0), &WaveNetDevice::Send, sender, packet, dest, IPv6_PROT_NUMBER);
8. send transmits WSA frames repeatedly by StartVsa method.
// this packet will contain WSA management information when IEEE 1609.3 model is implemented
Ptr<Packet> wsaPacket = Create<Packet> (100);
Mac48Address dest = Mac48Address::GetBroadcast ();
const VsaInfo vsaInfo = VsaInfo (dest, OrganizationIdentifier (), 0, wsaPacket, SCH1, 100, VSA_TRANSMIT_IN_BOTHI);
Simulator::Schedule (Seconds (2.0), &WaveNetDevice::StartVsa, sender, vsaInfo);
9. sender stops WSA frames repeatedly transmit by StopVsa method.
Simulator::Schedule (Seconds (3.0), &WaveNetDevice::StopVsa, sender, SCH1);
10.
sender and receiver release assigned channel access by StopSch method.
Simulator::Schedule (Seconds (4.0), &WaveNetDevice::StopSch, sender, SCH1);
Simulator::Schedule (Seconds (4.0), &WaveNetDevice::StopSch, receiver, SCH1);
11. sender or receiver changes current MAC address by ChangeAddress method.
Address newAddress = Mac48Address::Allocate ();
Simulator::Schedule (Seconds (4.0), &WaveNetDevice::ChangeAddress, sender, newAddress);
12. sender cancels all transmissions with the particular category and channel number by CancelTx method.
Simulator::Schedule (Seconds (4.0), &WaveNetDevice::CancelTx, sender, CCH, AC_BE);
For transmitting and receiving these packets successfully, the normal and appropriate invocation
procedures should be performed.
(a) For WSMP, channel access should be assigned for transmit and receive. The channel access release
operation may be optional if there is no need for transmission in another channel.
StartSch -------------> SendX / ReceiveCallback --------------> StopSch
(b) For IP, a tx profile should be registered before transmit and receive operations. The delete
operation of tx profile may be optional if there is no need for transmission with other tx parameters.
The channel access assignment and release optional usage is the same with WSMP here.
StartSch -------------> RegisterTxProfile ----------> Send / ReceiveCallback --------------> DeleteTxProfile -------------> StopSch
(c) For WSA, StartVsa is called to transmit while StopVsa is an optional operation for canceling repeat
transmit. The channel access assignment and release optional usage is also the same with WSMP here. To
receive VSA, WaveVsaCallback should be registered; otherwise, the received VSA frames will be discard by
the MAC extension layer and not delivered to the higher layer.
StartSch -------------> StartVsa / WaveVsaCallback --------------> StopVsa ---------------> StopSch
(d) Here an important point is that if the higher layer wants to transmit these packets in a control
channel (the channel 178), there will be no need to request for CCH by the StartSch method, which means
that StartSch can be optional or should be avoided here. The reason is that the default continuous CCH
access has been assigned automatically after WAVE devices are created and initialized. Therefore, if
calling StartSch and StopSch method with CCH as a parameter, the request will be discarded by devices and
the method will return false to indicate failure.
Attributes
The channel interval duration’s default value is defined in the standard. However, the current
implementation allows users to configure these attributes with other values. These attributes are
included in the class ns3::ChannelCoodinator with config paths shown in the below. The method
IsValidConfig is suggested to test whether new configuration follows the standard.
/NodeList/[i]/DeviceList/[i]/$ns3::WaveNetDevice/ChannelCoordinator/$ns3::ChannelCoordinator/CchInterval
/NodeList/[i]/DeviceList/[i]/$ns3::WaveNetDevice/ChannelCoordinator/$ns3::ChannelCoordinator/SchInterval
/NodeList/[i]/DeviceList/[i]/$ns3::WaveNetDevice/ChannelCoordinator/$ns3::ChannelCoordinator/GuardInterval
The ns3::WaveNetDevice is a wrapper class that contains those classes to support for multiple channel
operation. To set or get the pointers of those objects, users can also use them by config paths shown in
the below.
/NodeList/[i]/DeviceList/[i]/$ns3::WaveNetDevice/Mtu
/NodeList/[i]/DeviceList/[i]/$ns3::WaveNetDevice/Channel
/NodeList/[i]/DeviceList/[i]/$ns3::WaveNetDevice/PhyEntities
/NodeList/[i]/DeviceList/[i]/$ns3::WaveNetDevice/MacEntities
/NodeList/[i]/DeviceList/[i]/$ns3::WaveNetDevice/ChannelScheduler
/NodeList/[i]/DeviceList/[i]/$ns3::WaveNetDevice/ChannelManager
/NodeList/[i]/DeviceList/[i]/$ns3::WaveNetDevice/ChannelCoordinator
/NodeList/[i]/DeviceList/[i]/$ns3::WaveNetDevice/VsaManager
Output
For the 802.11p device, current classes provide output of the same type as WiFi devices; namely, ASCII
and pcap traces, and logging output. The 802.11p logging components can be enabled globally via the call
to
Wifi80211pHelper::EnableLogComponents ();
For the WAVE device, current classes provide output of the same type as WiFi devices; namely, ASCII and
pcap traces, and logging output. The WAVE logging components can be enabled globally via the call to
WaveHelper::EnableLogComponents ();
Advanced Usage
Advanced WaveHelper configuration
If users can make sure in which channel this WAVE device will work, they can set specific channel numbers
to save resources of unused channels . Usage is as follows:
// in this case, the MAC entities for SCH2 to SCH6 will not be created
WaveHelper helper = WaveHelper::Default ();
uint32_t channels[] = {CCH, SCH1};
std::vector<uint32_t> channelsVector (channels, channels + 2);
helper.CreateMacForChannel (channelsVector);
If users can create other channel access assignment mechanism, e.g. in the context of more PHY entities,
which may be called “ns3::AnotherScheduler”, they can use this helper to create WAVE devices with new
assignment mechanisms. Usage is as follows:
WaveHelper helper = WaveHelper::Default ();
helper.helper.CreateMacForChannel (ChannelManager::GetWaveChannels ()); // create all 7 MAC entities for WAVE
helper.CreatePhys (2); // or other number which should be less than 7
helper.SetChannelScheduler ("ns3::AnotherScheduler"); // The AnotherScheduler should be implemented by users.
helper.SetRemoteStationManager ("ns3::ConstantRateWifiManager"); // or other rate control algorithms
Examples
A basic example exists called wave-simple-80211p.cc. This example shows basic construction of an 802.11p
node. Two nodes are constructed with 802.11p devices, and by default, one node sends a single packet to
another node (the number of packets and interval between them can be configured by command-line
arguments). The example shows typical usage of the helper classes for this mode of WiFi.
Another example exists called wave-simple-device.cc. This example shows how to create WAVE devices by
helpers and the routing service for different packets. After WAVE devices are configured and created by
helpers, these packets are transmitted in different approaches.
Another example exists called vanet-routing-compare.cc. This example shows how to create mobility nodes
in a VANET scenario and send Basic Safety Message (BSM) packets are regular intervals and/or additional
data traffic to be routed between nodes. BSMs are transmitted assuming the WAVE Short Message Protocol
(WSMP), whereas non-BSM data packets are relayed by using one of several different IP-based routing
protocols (e.g., AODV, OLSR, DSDV, or DSR).
Troubleshooting
To be defined.
Validation
A test suite named wifi-80211p-ocb is defined. This test case consists of a stationary node and a mobile
node. The mobile node moves towards the stationary mode, and time points are checked at which time the
physical layer starts to receive packets (and whether the MAC becomes associated, if applicable). The
same physical experiment is repeated for normal WiFi NetDevices in AP/STA mode, in Adhoc mode, and the
new OCB mode.
Another test suite named wave-mac-extension is defined. This test suite has four test cases, including
channel-coordination, channel-routing, channel-access and annex-c. The first case is to test channel
coordination feature. The second case is to test channel routing for three types of packets. The third
case is to test four channel access assignments. And the fourth case is to test the implemented feature
described in the Annex C of the standard. It is worth noting that the channel-routing and
channel-access test cases are both in the context of single-PHY device, which depends on the default
channel access assignment mechanism ns3:DefaultChannelScheduler, thus they may not be suitable for
testing when other channel access assignment mechanisms are used. Although they are test cases, they are
also good examples to show usage.
The ns3::VanetRoutingExample example was studied using mobility trace files in the Raleigh, NC USA area
generated using Simulation for Urban Mobility (SUMO). Three environments were studied: a) an open
highway scenario, b) a residential neighborhood scenario, and c) and urban downtown scenario. For each
environment, a constant number of 50-750 vehicles was maintained for 2000 simulation seconds (> 30
minutes). The mobility trace file were played back using ns3::Ns2MobilityHelper. All vehicular nodes
transmitted a 200-byte BSM at 10 Hz and the PDR was determined for transmission ranges of 50-1500m. No
additional non-BSM data was injected / routed through the network. The default propagation loss model
used was Two-Ray Ground. Different fading / shadowing models were evaluated, including a) no fading, b)
stochastic Nakagami-m fading, and c) an obstacle shadowing model (to be contributed to ns-3). 30 trials
of each scenario were run in the North Carolina State University (NCSU) High Performance Computing (HPC)
center, with each trial requiring from 8 hours to 6 days of CPU time to complete. Preliminary results
were presented at the PhD Forum, 22nd IEEE International Conference on Network Protocols (ICNP), October
24, 2014, Research Triangle Park, NC. See:
http://www4.ncsu.edu/~scarpen/Research_files/Final-PHD_Forum_SE_Carpenter_2014.pdf
WI-FI MODULE
Design Documentation
ns-3 nodes can contain a collection of NetDevice objects, much like an actual computer contains separate
interface cards for Ethernet, Wifi, Bluetooth, etc. This chapter describes the ns-3 WifiNetDevice and
related models. By adding WifiNetDevice objects to ns-3 nodes, one can create models of 802.11-based
infrastructure and ad hoc networks.
Overview of the model
The WifiNetDevice models a wireless network interface controller based on the IEEE 802.11 standard
[ieee80211]. We will go into more detail below but in brief, ns-3 provides models for these aspects of
802.11:
• basic 802.11 DCF with infrastructure and adhoc modes
• 802.11a, 802.11b, 802.11g, 802.11n (both 2.4 and 5 GHz bands), 802.11ac and 802.11ax (2.4, 5 and 6 GHz
bands) physical layers
• MSDU aggregation and MPDU aggregation extensions of 802.11n, and both can be combined together
(two-level aggregation)
• 802.11ax DL OFDMA and UL OFDMA (including support for the MU EDCA Parameter Set)
• QoS-based EDCA and queueing extensions of 802.11e
• the ability to use different propagation loss models and propagation delay models, please see the
chapter on Propagation for more detail
• packet error models and frame detection models that have been validated against link simulations and
other references
• various rate control algorithms including Aarf, Arf, Cara, Onoe, Rraa, ConstantRate, Minstrel and
Minstrel-HT
• 802.11s (mesh), described in another chapter
• 802.11p and WAVE (vehicular), described in another chapter
The set of 802.11 models provided in ns-3 attempts to provide an accurate MAC-level implementation of the
802.11 specification and to provide a packet-level abstraction of the PHY-level for different PHYs,
corresponding to 802.11a/b/e/g/n/ac/ax specifications.
In ns-3, nodes can have multiple WifiNetDevices on separate channels, and the WifiNetDevice can coexist
with other device types. With the use of the SpectrumWifiPhy framework, one can also build scenarios
involving cross-channel interference or multiple wireless technologies on a single channel.
The source code for the WifiNetDevice and its models lives in the directory src/wifi.
The implementation is modular and provides roughly three sublayers of models:
• the PHY layer models: they model amendment-specific and common PHY layer operations and functions.
• the so-called MAC low models: they model functions such as medium access (DCF and EDCA), frame
protection (RTS/CTS) and acknowledgment (ACK/BlockAck). In ns-3, the lower-level MAC is comprised of a
Frame Exchange Manager hierarchy, a Channel Access Manager and a MAC middle entity.
• the so-called MAC high models: they implement non-time-critical processes in Wifi such as the MAC-level
beacon generation, probing, and association state machines, and a set of Rate control algorithms. In
the literature, this sublayer is sometimes called the upper MAC and consists of more software-oriented
implementations vs. time-critical hardware implementations.
Next, we provide an design overview of each layer, shown in Figure WifiNetDevice architecture.
[image] WifiNetDevice architecture.UNINDENT
MAC high models
There are presently three MAC high models that provide for the three (non-mesh; the mesh equivalent,
which is a sibling of these with common parent ns3::RegularWifiMac, is not discussed here) Wi-Fi
topological elements - Access Point (AP) (ns3::ApWifiMac), non-AP Station (STA) (ns3::StaWifiMac), and
STA in an Independent Basic Service Set (IBSS) - also commonly referred to as an ad hoc network
(ns3::AdhocWifiMac).
The simplest of these is ns3::AdhocWifiMac, which implements a Wi-Fi MAC that does not perform any kind
of beacon generation, probing, or association. The ns3::StaWifiMac class implements an active probing and
association state machine that handles automatic re-association whenever too many beacons are missed.
Finally, ns3::ApWifiMac implements an AP that generates periodic beacons, and that accepts every attempt
to associate.
These three MAC high models share a common parent in ns3::RegularWifiMac, which exposes, among other MAC
configuration, an attribute QosSupported that allows configuration of 802.11e/WMM-style QoS support.
There are also several rate control algorithms that can be used by the MAC low layer. A complete list of
available rate control algorithms is provided in a separate section.
MAC low layer
The MAC low layer is split into three main components:
1. ns3::FrameExchangeManager a class hierarchy which implement the frame exchange sequences introduced by
the supported IEEE 802.11 amendments. It also handles frame aggregation, frame retransmissions,
protection and acknowledgment.
2. ns3::ChannelAccessManager which implements the DCF and EDCAF functions.
3. ns3::Txop and ns3::QosTxop which handle the packet queue. The ns3::Txop object is used by high MACs
that are not QoS-enabled, and for transmission of frames (e.g., of type Management) that the standard
says should access the medium using the DCF. ns3::QosTxop is used by QoS-enabled high MACs.
PHY layer models
In short, the physical layer models are mainly responsible for modeling the reception of packets and for
tracking energy consumption. There are typically three main components to packet reception:
• each packet received is probabilistically evaluated for successful or failed reception. The
probability depends on the modulation, on the signal to noise (and interference) ratio for the packet,
and on the state of the physical layer (e.g. reception is not possible while transmission or sleeping
is taking place);
• an object exists to track (bookkeeping) all received signals so that the correct interference power for
each packet can be computed when a reception decision has to be made; and
• one or more error models corresponding to the modulation and standard are used to look up probability
of successful reception.
ns-3 offers users a choice between two physical layer models, with a base interface defined in the
ns3::WifiPhy class. The YansWifiPhy class implements a simple physical layer model, which is described
in a paper entitled Yet Another Network Simulator The acronym Yans derives from this paper title. The
SpectrumWifiPhy class is a more advanced implementation based on the Spectrum framework used for other
ns-3 wireless models. Spectrum allows a fine-grained frequency decomposition of the signal, and permits
scenarios to include multiple technologies coexisting on the same channel.
Scope and Limitations
The IEEE 802.11 standard [ieee80211] is a large specification, and not all aspects are covered by ns-3;
the documentation of ns-3’s conformance by itself would lead to a very long document. This section
attempts to summarize compliance with the standard and with behavior found in practice.
The physical layer and channel models operate on a per-packet basis, with no frequency-selective
propagation nor interference effects when using the default YansWifiPhy model. Directional antennas are
also not supported at this time. For additive white Gaussian noise (AWGN) scenarios, or wideband
interference scenarios, performance is governed by the application of analytical models (based on
modulation and factors such as channel width) to the received signal-to-noise ratio, where noise combines
the effect of thermal noise and of interference from other Wi-Fi packets. Interference from other
wireless technologies is only modeled when the SpectrumWifiPhy is used. The following details pertain to
the physical layer and channel models:
• 802.11ax MU-RTS/CTS is not yet supported
• 802.11ac/ax MU-MIMO is not supported, and no more than 4 antennas can be configured
• 802.11n/ac/ax beamforming is not supported
• 802.11n RIFS is not supported
• 802.11 PCF/HCF/HCCA are not implemented
• Authentication and encryption are missing
• Processing delays are not modeled
• Channel bonding implementation only supports the use of the configured channel width and does not
perform CCA on secondary channels
• Cases where RTS/CTS and ACK are transmitted using HT/VHT/HE formats are not supported
• Energy consumption model does not consider MIMO
At the MAC layer, most of the main functions found in deployed Wi-Fi equipment for 802.11a/b/e/g/n/ac/ax
are implemented, but there are scattered instances where some limitations in the models exist. Support
for 802.11n, ac and ax is evolving.
Some implementation choices that are not imposed by the standard are listed below:
• BSSBasicRateSet for 802.11b has been assumed to be 1-2 Mbit/s
• BSSBasicRateSet for 802.11a/g has been assumed to be 6-12-24 Mbit/s
• OperationalRateSet is assumed to contain all mandatory rates (see issue 183)
• The wifi manager always selects the lowest basic rate for management frames.
Design Details
The remainder of this section is devoted to more in-depth design descriptions of some of the Wi-Fi
models. Users interested in skipping to the section on usage of the wifi module (User Documentation) may
do so at this point. We organize these more detailed sections from the bottom-up, in terms of layering,
by describing the channel and PHY models first, followed by the MAC models.
We focus first on the choice between physical layer frameworks. ns-3 contains support for a Wi-Fi-only
physical layer model called YansWifiPhy that offers no frequency-level decomposition of the signal. For
simulations that involve only Wi-Fi signals on the Wi-Fi channel, and that do not involve
frequency-dependent propagation loss or fading models, the default YansWifiPhy framework is a suitable
choice. For simulations involving mixed technologies on the same channel, or frequency dependent
effects, the SpectrumWifiPhy is more appropriate. The two frameworks are very similarly configured.
The SpectrumWifiPhy framework uses the sec-spectrum-module channel framework.
The YansWifiChannel is the only concrete channel model class in the ns-3 wifi module. The
ns3::YansWifiChannel implementation uses the propagation loss and delay models provided within the ns-3
Propagation module. In particular, a number of propagation models can be added (chained together, if
multiple loss models are added) to the channel object, and a propagation delay model also added. Packets
sent from a ns3::YansWifiPhy object onto the channel with a particular signal power, are copied to all of
the other ns3::YansWifiPhy objects after the signal power is reduced due to the propagation loss
model(s), and after a delay corresponding to transmission (serialization) delay and propagation delay due
to any channel propagation delay model (typically due to speed-of-light delay between the positions of
the devices).
Only objects of ns3::YansWifiPhy may be attached to a ns3::YansWifiChannel; therefore, objects modeling
other (interfering) technologies such as LTE are not allowed. Furthermore, packets from different
channels do not interact; if a channel is logically configured for e.g. channels 5 and 6, the packets do
not cause adjacent channel interference (even if their channel numbers overlap).
WifiPhy and related models
The ns3::WifiPhy is an abstract base class representing the 802.11 physical layer functions. Packets
passed to this object (via a Send() method) are sent over a channel object, and upon reception, the
receiving PHY object decides (based on signal power and interference) whether the packet was successful
or not. This class also provides a number of callbacks for notifications of physical layer events,
exposes a notion of a state machine that can be monitored for MAC-level processes such as carrier sense,
and handles sleep/wake/off models and energy consumption. The ns3::WifiPhy hooks to the
ns3::FrameExchangeManager object in the WifiNetDevice.
There are currently two implementations of the WifiPhy: the ns3::YansWifiPhy and the
ns3::SpectrumWifiPhy. They each work in conjunction with five other objects:
• PhyEntity: Contains the amendment-specific part of the PHY processing
• WifiPpdu: Models the amendment-specific PHY protocol data unit (PPDU)
• WifiPhyStateHelper: Maintains the PHY state machine
• InterferenceHelper: Tracks all packets observed on the channel
• ErrorModel: Computes a probability of error for a given SNR
PhyEntity
A bit of background
Some restructuring of ns3::WifiPhy and ns3::WifiMode (among others) was necessary considering the size
and complexity of the corresponding files. In addition, adding and maintaining new PHY amendments had
become a complex task (especially those implemented inside other modules, e.g. DMG). The adopted
solution was to have PhyEntity classes that contain the “clause” specific (i.e. HT/VHT/HE etc) parts of
the PHY process.
The notion of “PHY entity” is in the standard at the beginning of each PHY layer description clause, e.g.
section 21.1.1 of IEEE 802.11-2016:
:: Clause 21 specifies the PHY entity for a very high throughput (VHT) orthogonal frequency division
multiplexing (OFDM) system.
Note that there is already such a name inside the wave module (e.g. ``WaveNetDevice::AddPhy``) to
designate the WifiPhys on each 11p channel, but the wording is only used within the classes and there is
no file using that name, so no ambiguity in using the name for 802.11 amendments.
Architecture
The abstract base class ns3::PhyEntity enables to have a unique set of APIs to be used by each PHY
entity, corresponding to the different amendments of the IEEE 802.11 standard. The currently implemented
PHY entities are:
• ns3::DsssPhy: PHY entity for DSSS and HR/DSSS (11b)
• ns3::OfdmPhy: PHY entity for OFDM (11a and 11p)
• ns3::ErpOfdmPhy: PHY entity for ERP-OFDM (11g)
• ns3::HtPhy: PHY entity for HT (11n)
• ns3::VhtPhy: PHY entity for VHT (11ac)
• ns3::HePhy: PHY entity for HE (11ax)
Their inheritance diagram is given in Figure PhyEntity hierarchy and closely follows the standard’s
logic, e.g. section 21.1.1 of IEEE 802.11-2016:
:: The VHT PHY is based on the HT PHY defined in Clause 19, which in turn is based on the OFDM PHY
defined in Clause 17.
[image] PhyEntity hierarchy.UNINDENT
Such an architecture enables to handle the following operations in an amendment- specific manner:
• WifiMode handling and data/PHY rate computation,
• PPDU field size and duration computation, and
• Transmit and receive paths.
WifiPpdu
In the same vein as PhyEntity, the ns3::WifiPpdu base class has been specialized into the following
amendment-specific PPDUs:
• ns3::DsssPpdu: PPDU for DSSS and HR/DSSS (11b)
• ns3::OfdmPpdu: PPDU for OFDM (11a and 11p)
• ns3::ErpOfdmPpdu: PPDU for ERP-OFDM (11g)
• ns3::HtPpdu: PPDU for HT (11n)
• ns3::VhtPpdu: PPDU for VHT (11ac)
• ns3::HePpdu: PPDU for HE (11ax)
Their inheritance diagram is given in Figure WifiPpdu hierarchy and closely follows the standard’s logic,
e.g. section 21.3.8.1 of IEEE 802.11-2016:
:: To maintain compatibility with non-VHT STAs, specific non-VHT fields are defined that can be received
by non-VHT STAs compliant with Clause 17 [OFDM] or Clause 19 [HT].
[image] WifiPpdu hierarchy.UNINDENT
YansWifiPhy and WifiPhyStateHelper
Class ns3::YansWifiPhy is responsible for taking packets passed to it from the MAC (the
ns3::FrameExchangeManager object) and sending them onto the ns3::YansWifiChannel to which it is attached.
It is also responsible to receive packets from that channel, and, if reception is deemed to have been
successful, to pass them up to the MAC.
The energy of the signal intended to be received is calculated from the transmission power and adjusted
based on the Tx gain of the transmitter, Rx gain of the receiver, and any path loss propagation model in
effect.
Class ns3::WifiPhyStateHelper manages the state machine of the PHY layer, and allows other objects to
hook as listeners to monitor PHY state. The main use of listeners is for the MAC layer to know when the
PHY is busy or not (for transmission and collision avoidance).
The PHY layer can be in one of seven states:
1. TX: the PHY is currently transmitting a signal on behalf of its associated MAC
2. RX: the PHY is synchronized on a signal and is waiting until it has received its last bit to forward
it to the MAC.
3. IDLE: the PHY is not in the TX, RX, or CCA_BUSY states.
4. CCA_BUSY: the PHY is not in TX or RX state but the measured energy is higher than the energy detection
threshold.
5. SWITCHING: the PHY is switching channels.
6. SLEEP: the PHY is in a power save mode and cannot send nor receive frames.
7. OFF: the PHY is powered off and cannot send nor receive frames.
Packet reception works as follows. For YansWifiPhy, most of the logic is implemented in the WifiPhy base
class. The YansWifiChannel calls WifiPhy::StartReceivePreamble (). The latter calls
PhyEntity::StartReceivePreamble () of the appropriate PHY entity to start packet reception, but first
there is a check of the packet’s notional signal power level against a threshold value stored in the
attribute WifiPhy::RxSensitivity. Any packet with a power lower than RxSensitivity will be dropped with
no further processing. The default value is -101 dBm, which is the thermal noise floor for 20 MHz signal
at room temperature. The purpose of this attribute is two-fold: 1) very weak signals that will not
affect the outcome will otherwise consume simulation memory and event processing, so they are discarded,
and 2) this value can be adjusted upwards to function as a basic carrier sense threshold limitation for
experiments involving spatial reuse considerations. Users are cautioned about the behavior of raising
this threshold; namely, that all packets with power below this threshold will be discarded upon
reception.
In StartReceivePreamble (), the packet is immediately added to the interference helper for
signal-to-noise tracking, and then further reception steps are decided upon the state of the PHY. In the
case that the PHY is transmitting, for instance, the packet will be dropped. If the PHY is IDLE, or if
the PHY is receiving and an optional FrameCaptureModel is being used (and the packet is within the
capture window), then PhyEntity::StartPreambleDetectionPeriod () is called next.
The PhyEntity::StartPreambleDetectionPeriod () will typically schedule an event,
PhyEntity::EndPreambleDetectionPeriod (), to occur at the notional end of the first OFDM symbol, to check
whether the preamble has been detected. As of revisions to the model in ns-3.30, any state machine
transitions from IDLE state are suppressed until after the preamble detection event.
The PhyEntity::EndPreambleDetectionPeriod () method will check, with a preamble detection model, whether
the signal is strong enough to be received, and if so, an event PhyEntity::EndReceiveField () is
scheduled for the end of the preamble and the PHY is put into the CCA_BUSY state. Currently, there is
only a simple threshold-based preamble detection model in ns-3, called ThresholdPreambleDetectionModel.
If there is no preamble detection model, the preamble is assumed to have been detected. It is important
to note that, starting with the ns-3.30 release, the default in the WifiPhyHelper is to add the
ThresholdPreambleDetectionModel with a threshold RSSI of -82 dBm, and a threshold SNR of 4 dB. Both the
RSSI and SNR must be above these respective values for the preamble to be successfully detected. The
default sensitivity has been reduced in ns-3.30 compared with that of previous releases, so some packet
receptions that were previously successful will now fail on this check. More details on the modeling
behind this change are provided in [lanante2019].
The PhyEntity::EndReceiveField () method will check the correct reception of the current preamble and
header field and, if so, calls PhyEntity::StartReceiveField () for the next field, otherwise the
reception is aborted and PHY is put either in IDLE state or in CCA_BUSY state, depending on whether the
measured energy is higher than the energy detection threshold.
The next event at PhyEntity::StartReceiveField () checks, using the interference helper and error model,
whether the header was successfully decoded, and if so, a PhyRxPayloadBegin callback (equivalent to the
PHY-RXSTART primitive) is triggered. The PHY header is often transmitted at a lower modulation rate than
is the payload. The portion of the packet corresponding to the PHY header is evaluated for probability of
error based on the observed SNR. The InterferenceHelper object returns a value for “probability of error
(PER)” for this header based on the SNR that has been tracked by the InterferenceHelper. The PhyEntity
then draws a random number from a uniform distribution and compares it against the PER and decides
success or failure.
This is iteratively performed up to the beginning of the data field upon which
PhyEntity::StartReceivePayload () is called.
Even if packet objects received by the PHY are not part of the reception process, they are tracked by the
InterferenceHelper object for purposes of SINR computation and making clear channel assessment decisions.
If, in the course of reception, a packet is errored or dropped due to the PHY being in a state in which
it cannot receive a packet, the packet is added to the interference helper, and the aggregate of the
energy of all such signals is compared against an energy detection threshold to determine whether the PHY
should enter a CCA_BUSY state. The WifiPhy::CcaEdThreshold attribute corresponds to what the standard
calls the “ED threshold” for CCA Mode 1. In section 16.4.8.5 in the 802.11-2012 standard: “CCA Mode 1:
Energy above threshold. CCA shall report a busy medium upon detection of any energy above the ED
threshold.” By default, this value is set to the -62 dBm level specified in the standard for 20 MHz
channels. When using YansWifiPhy, there are no non-Wi-Fi signals, so it is unlikely that this attribute
would play much of a role in Yans wifi models if left at the default value, but if there is a strong
Wi-Fi signal that is not otherwise being received by the model, it has the possibility to raise the
CCA_BUSY while the overall energy exceeds this threshold.
The above describes the case in which the packet is a single MPDU. For more recent Wi-Fi standards using
MPDU aggregation, StartReceivePayload schedules an event for reception of each individual MPDU
(ScheduleEndOfMpdus), which then forwards each MPDU as they arrive up to FrameExchangeManager, if the
reception of the MPDU has been successful. Once the A-MPDU reception is finished, FrameExchangeManager is
also notified about the amount of successfully received MPDUs.
InterferenceHelper
The InterferenceHelper is an object that tracks all incoming packets and calculates probability of error
values for packets being received, and also evaluates whether energy on the channel rises above the CCA
threshold.
The basic operation of probability of error calculations is shown in Figure SNIR function over time.
Packets are represented as bits (not symbols) in the ns-3 model, and the InterferenceHelper breaks the
packet into one or more “chunks”, each with a different signal to noise (and interference) ratio (SNIR).
Each chunk is separately evaluated by asking for the probability of error for a given number of bits from
the error model in use. The InterferenceHelper builds an aggregate “probability of error” value based on
these chunks and their duration, and returns this back to the WifiPhy for a reception decision.
[image] SNIR function over time.UNINDENT
From the SNIR function we can derive the Bit Error Rate (BER) and Packet Error Rate (PER) for the
modulation and coding scheme being used for the transmission.
If MIMO is used and the number of spatial streams is lower than the number of active antennas at the
receiver, then a gain is applied to the calculated SNIR as follows (since STBC is not used):
gain (dB) = 10 \log(\frac{RX \ antennas}{spatial \ streams})
Having more TX antennas can be safely ignored for AWGN. The resulting gain is:
antennas NSS gain
2 x 1 1 0 dB
1 x 2 1 3 dB
2 x 2 1 3 dB
3 x 3 1 4.8 dB
3 x 3 2 1.8 dB
3 x 3 3 0 dB
4 x 4 1 6 dB
4 x 4 2 3 dB
4 x 4 3 1.2 dB
4 x 4 4 0 dB
...
ErrorRateModel
ns-3 makes a packet error or success decision based on the input received SNR of a frame and based on any
possible interfering frames that may overlap in time; i.e. based on the signal-to-noise (plus
interference) ratio, or SINR. The relationship between packet error ratio (PER) and SINR in ns-3 is
defined by the ns3::ErrorRateModel, of which there are several. The PER is a function of the frame’s
modulation and coding (MCS), its SINR, and the specific ErrorRateModel configured for the MCS.
ns-3 has updated its default ErrorRateModel over time. The current (as of ns-3.33 release) model for
recent OFDM-based standards (i.e., 802.11n/ac/ax), is the ns3::TableBasedErrorRateModel. The default for
802.11a/g is the ns3::YansErrorRateModel, and the default for 802.11b is the ns3::DsssErrorRateModel.
The error rate model for recent standards was updated during the ns-3.33 release cycle (previously, it
was the ns3::NistErrorRateModel).
The error models are described in more detail in outside references. The current OFDM model is based on
work published in [patidar2017], using link simulations results from the MATLAB WLAN Toolbox, and
validated against IEEE TGn results [erceg2004]. For publications related to other error models, please
refer to [pei80211ofdm], [pei80211b], [lacage2006yans], [Haccoun], [hepner2015] and [Frenger] for a
detailed description of the legacy PER models.
The current ns-3 error rate models are for additive white gaussian noise channels (AWGN) only; any
potential frequency-selective fading effects are not modeled.
In summary, there are four error models:
1. ns3::TableBasedErrorRateModel: for OFDM modes and reuses ns3::DsssErrorRateModel for 802.11b modes.
This is the default for 802.11n/ac/ax.
2. ns3::YansErrorRateModel: for OFDM modes and reuses ns3::DsssErrorRateModel for 802.11b modes. This is
the default for 802.11a/g.
3. ns3::DsssErrorRateModel: contains models for 802.11b modes. The 802.11b 1 Mbps and 2 Mbps error
models are based on classical modulation analysis. If GNU Scientific Library (GSL) is installed, the
5.5 Mbps and 11 Mbps from [pursley2009] are used for CCK modulation; otherwise, results from a backup
MATLAB-based CCK model are used.
4. ns3::NistErrorRateModel: for OFDM modes and reuses ns3::DsssErrorRateModel for 802.11b modes.
Users may select either NIST, YANS or Table-based models for OFDM, and DSSS will be used in either case
for 802.11b. The NIST model was a long-standing default in ns-3 (through release 3.32).
TableBasedErrorRateModel
The ns3::TableBasedErrorRateModel has been recently added and is now the ns-3 default for 802.11n/ac/ax,
while ns3::YansErrorRateModel is the ns-3 default for 802.11a/g.
Unlike analytical error models based on error bounds, ns3::TableBasedErrorRateModel contains end-to-end
link simulation tables (PER vs SNR) for AWGN channels. Since it is infeasible to generate such look-up
tables for all desired packet sizes and input SNRs, we adopt the recommendation of IEEE P802.11 TGax
[porat2016] that proposed estimating PER for any desired packet length using BCC FEC encoding by
extrapolating the results from two reference lengths: 32 (all lengths less than 400) bytes and 1458 (all
lengths greater or equal to 400) bytes respectively. In case of LDPC FEC encoding, IEEE P802.11 TGax
recommends the use of a single reference length. Hence, we provide two tables for BCC and one table for
LDPC that are generated using a reliable and publicly available commercial link simulator (MATLAB WLAN
Toolbox) for each modulation and coding scheme. Note that BCC tables are limited to MCS 9. For higher
MCSs, the models fall back to the use of the YANS analytical model.
The validation scenario is set as follows:
1. Ideal channel and perfect channel estimation.
2. Perfect packet synchronization and detection.
3. Phase tracking, phase correction, phase noise, carrier frequency offset, power amplifier
non-linearities etc. are not considered.
Several packets are simulated across the link to obtain PER, the number of packets needed to reliably
estimate a PER value is computed using the consideration that the ratio of the estimation error to the
true value should be within 10 % with probability 0.95. For each SNR value, simulations were run until a
total of 40000 packets were simulated.
The obtained results are very close to TGax curves as shown in Figure Comparison of table-based OFDM
Error Model with TGax results.
[image] Comparison of table-based OFDM Error Model with TGax results..UNINDENT
Legacy ErrorRateModels
The original error rate model was called the ns3::YansErrorRateModel and was based on analytical results.
For 802.11b modulations, the 1 Mbps mode is based on DBPSK. BER is from equation 5.2-69 from
[proakis2001]. The 2 Mbps model is based on DQPSK. Equation 8 of [ferrari2004]. More details are
provided in [lacage2006yans].
The ns3::NistErrorRateModel was later added. The model was largely aligned with the previous
ns3::YansErrorRateModel for DSSS modulations 1 Mbps and 2 Mbps, but the 5.5 Mbps and 11 Mbps models were
re-based on equations (17) and (18) from [pursley2009]. For OFDM modulations, newer results were
obtained based on work previously done at NIST [miller2003]. The results were also compared against the
CMU wireless network emulator, and details of the validation are provided in [pei80211ofdm]. Since OFDM
modes use hard-decision of punctured codes, the coded BER is calculated using Chernoff bounds
[hepner2015].
The 802.11b model was split from the OFDM model when the NIST error rate model was added, into a new
model called DsssErrorRateModel.
Furthermore, the 5.5 Mbps and 11 Mbps models for 802.11b rely on library methods implemented in the GNU
Scientific Library (GSL). The Waf build system tries to detect whether the host platform has GSL
installed; if so, it compiles in the newer models from [pursley2009] for 5.5 Mbps and 11 Mbps; if not, it
uses a backup model derived from MATLAB simulations.
The error curves for analytical models are shown to diverge from link simulation results for higher MCS
in Figure YANS and NIST error model comparison with TGn results. This prompted the move to a new error
model based on link simulations (the default TableBasedErrorRateModel, which provides curves close to
those depicted by the TGn dashed line).
[image] YANS and NIST error model comparison with TGn results.UNINDENT
SpectrumWifiPhy
This section describes the implementation of the SpectrumWifiPhy class that can be found in
src/wifi/model/spectrum-wifi-phy.{cc,h}.
The implementation also makes use of additional classes found in the same directory:
• wifi-spectrum-phy-interface.{cc,h}
• wifi-spectrum-signal-parameters.{cc,h}
and classes found in the spectrum module:
• wifi-spectrum-value-helper.{cc,h}
The current SpectrumWifiPhy class reuses the existing interference manager and error rate models
originally built for YansWifiPhy, but allows, as a first step, foreign (non Wi-Fi) signals to be treated
as additive noise.
Two main changes were needed to adapt the Spectrum framework to Wi-Fi. First, the physical layer must
send signals compatible with the Spectrum channel framework, and in particular, the
MultiModelSpectrumChannel that allows signals from different technologies to coexist. Second, the
InterferenceHelper must be extended to support the insertion of non-Wi-Fi signals and to add their
received power to the noise, in the same way that unintended Wi-Fi signals (perhaps from a different SSID
or arriving late from a hidden node) are added to the noise.
Unlike YansWifiPhy, where there are no foreign signals, CCA_BUSY state will be raised for foreign signals
that are higher than CcaEdThreshold (see section 16.4.8.5 in the 802.11-2012 standard for definition of
CCA Mode 1). The attribute WifiPhy::CcaEdThreshold therefore potentially plays a larger role in this
model than in the YansWifiPhy model.
To support the Spectrum channel, the YansWifiPhy transmit and receive methods were adapted to use the
Spectrum channel API. This required developing a few SpectrumModel-related classes. The class
WifiSpectrumValueHelper is used to create Wi-Fi signals with the spectrum framework and spread their
energy across the bands. The spectrum is sub-divided into sub-bands (the width of an OFDM subcarrier,
which depends on the technology). The power allocated to a particular channel is spread across the
sub-bands roughly according to how power would be allocated to sub-carriers. Adjacent channels are models
by the use of OFDM transmit spectrum masks as defined in the standards.
To support an easier user configuration experience, the existing YansWifi helper classes (in
src/wifi/helper) were copied and adapted to provide equivalent SpectrumWifi helper classes.
Finally, for reasons related to avoiding C++ multiple inheritance issues, a small forwarding class called
WifiSpectrumPhyInterface was inserted as a shim between the SpectrumWifiPhy and the Spectrum channel.
The WifiSpectrumPhyInterface calls a different SpectrumWifiPhy::StartRx () method to start the reception
process. This method performs the check of the signal power against the WifiPhy::RxSensitivity attribute
and discards weak signals, and also checks if the signal is a Wi-Fi signal; non-Wi-Fi signals are added
to the InterferenceHelper and can raise CCA_BUSY but are not further processed in the reception chain.
After this point, valid Wi-Fi signals cause WifiPhy::StartReceivePreamble to be called, and the
processing continues as described above.
The MAC model
Infrastructure association
Association in infrastructure mode is a high-level MAC function. Either active probing or passive
scanning is used (default is passive scan). At the start of the simulation, Wi-Fi network devices
configured as STA will attempt to scan the channel. Depends on whether passive or active scanning is
selected, STA will attempt to gather beacons, or send a probe request and gather probe responses until
the respective timeout occurs. The end result will be a list of candidate AP to associate to. STA will
then try to associate to the best AP (i.e., best SNR).
If association is rejected by the AP for some reason, the STA will try to associate to the next best AP
until the candidate list is exhausted which then sends STA to ‘REFUSED’ state. If this occurs, the
simulation user will need to force reassociation retry in some way, perhaps by changing configuration
(i.e. the STA will not persistently try to associate upon a refusal).
When associated, if the configuration is changed by the simulation user, the STA will try to reassociate
with the existing AP.
If the number of missed beacons exceeds the threshold, the STA will notify the rest of the device that
the link is down (association is lost) and restart the scanning process. Note that this can also happen
when an association request fails without explicit refusal (i.e., the AP fails to respond to association
request).
Roaming
Roaming at layer-2 (i.e. a STA migrates its association from one AP to another) is not presently
supported. Because of that, the Min/Max channel dwelling time implementation as described by the IEEE
802.11 standard [ieee80211] is also omitted, since it is only meaningful on the context of channel
roaming.
Channel access
The 802.11 Distributed Coordination Function is used to calculate when to grant access to the
transmission medium. While implementing the DCF would have been particularly easy if we had used a
recurring timer that expired every slot, we chose to use the method described in [ji2004sslswn] where the
backoff timer duration is lazily calculated whenever needed since it is claimed to have much better
performance than the simpler recurring timer solution.
The DCF basic access is described in section 10.3.4.2 of [ieee80211-2016].
• “A STA may transmit an MPDU when it is operating under the DCF access method [..] when the STA
determines that the medium is idle when a frame is queued for transmission, and remains idle for a
period of a DIFS, or an EIFS (10.3.2.3.7) from the end of the immediately preceding medium-busy event,
whichever is the greater, and the backoff timer is zero. Otherwise the random backoff procedure
described in 10.3.4.3 shall be followed.”
Thus, a station is allowed not to invoke the backoff procedure if all of the following conditions are
met:
• the medium is idle when a frame is queued for transmission
• the medium remains idle until the most recent of these two events: a DIFS from the time when the frame
is queued for transmission; an EIFS from the end of the immediately preceding medium-busy event
(associated with the reception of an erroneous frame)
• the backoff timer is zero
The backoff procedure of DCF is described in section 10.3.4.3 of [ieee80211-2016].
• “A STA shall invoke the backoff procedure to transfer a frame when finding the medium busy as indicated
by either the physical or virtual CS mechanism.”
• “A backoff procedure shall be performed immediately after the end of every transmission with the More
Fragments bit set to 0 of an MPDU of type Data, Management, or Control with subtype PS-Poll, even if no
additional transmissions are currently queued.”
The EDCA backoff procedure is slightly different than the DCF backoff procedure and is described in
section 10.22.2.2 of [ieee80211-2016]. The backoff procedure shall be invoked by an EDCAF when any of the
following events occur:
• a frame is “queued for transmission such that one of the transmit queues associated with that AC has
now become non-empty and any other transmit queues associated with that AC are empty; the medium is
busy on the primary channel”
• “The transmission of the MPDU in the final PPDU transmitted by the TXOP holder during the TXOP for that
AC has completed and the TXNAV timer has expired, and the AC was a primary AC”
• “The transmission of an MPDU in the initial PPDU of a TXOP fails [..] and the AC was a primary AC”
• “The transmission attempt collides internally with another EDCAF of an AC that has higher priority”
• (optionally) “The transmission by the TXOP holder of an MPDU in a non-initial PPDU of a TXOP fails”
Additionally, section 10.22.2.4 of [ieee80211-2016] introduces the notion of slot boundary, which
basically occurs following SIFS + AIFSN * slotTime of idle medium after the last busy medium that was the
result of a reception of a frame with a correct FCS or following EIFS - DIFS + AIFSN * slotTime + SIFS of
idle medium after the last indicated busy medium that was the result of a frame reception that has
resulted in FCS error, or following a slotTime of idle medium occurring immediately after any of these
conditions.
On these specific slot boundaries, each EDCAF shall make a determination to perform one and only one of
the following functions:
• Decrement the backoff timer.
• Initiate the transmission of a frame exchange sequence.
• Invoke the backoff procedure due to an internal collision.
• Do nothing.
Thus, if an EDCAF decrements its backoff timer on a given slot boundary and, as a result, the backoff
timer has a zero value, the EDCAF cannot immediately transmit, but it has to wait for another slotTime of
idle medium before transmission can start.
The higher-level MAC functions are implemented in a set of other C++ classes and deal with:
• packet fragmentation and defragmentation,
• use of the RTS/CTS protocol,
• rate control algorithm,
• connection and disconnection to and from an Access Point,
• the MAC transmission queue,
• beacon generation,
• MSDU aggregation,
• etc.
Frame Exchange Managers
As the IEEE 802.11 standard evolves, more and more features are added and it is more and more difficult
to have a single component handling all of the allowed frame exchange sequences. A hierarchy of
FrameExchangeManager classes has been introduced to make the code clean and scalable, while avoiding code
duplication. Each FrameExchangeManager class handles the frame exchange sequences introduced by a given
amendment. The FrameExchangeManager hierarchy is depicted in Figure FrameExchangeManager hierarchy.
[image] FrameExchangeManager hierarchy.UNINDENT
The features supported by every FrameExchangeManager class are as follows:
• FrameExchangeManager is the base class. It handles the basic sequences for non-QoS stations: MPDU
followed by Normal Ack, RTS/CTS and CTS-to-self, NAV setting and resetting, MPDU fragmentation
• QosFrameExchangeManager adds TXOP support: multiple protection setting, TXOP truncation via CF-End,
TXOP recovery, ignore NAV when responding to an RTS sent by the TXOP holder
• HtFrameExchangeManager adds support for Block Ack (compressed variant), A-MSDU and A-MPDU aggregation,
Implicit Block Ack Request policy
• VhtFrameExchangeManager adds support for S-MPDUs
• HeFrameExchangeManager adds support for the transmission and reception of multi-user frames via DL
OFDMA and UL OFDMA, as detailed below.
Multi-user transmissions
Since the introduction of the IEEE 802.11ax amendment, multi-user (MU) transmissions are possible, both
in downlink (DL) and uplink (UL), by using OFDMA and/or MU-MIMO. Currently, ns-3 only supports multi-user
transmissions via OFDMA. Three acknowledgment sequences are implemented for DL OFDMA.
The first acknowledgment sequence is made of multiple BlockAckRequest/BlockAck frames sent as single-user
frames, as shown in Figure Acknowledgment of DL MU frames in single-user format.
[image] Acknowledgment of DL MU frames in single-user format.UNINDENT
For the second acknowledgment sequence, an MU-BAR Trigger Frame is sent (as a single-user frame) to
solicit BlockAck responses sent in TB PPDUs, as shown in Figure Acknowledgment of DL MU frames via
MU-BAR Trigger Frame sent as single-user frame.
[image] Acknowledgment of DL MU frames via MU-BAR Trigger Frame sent as single-user frame.UNINDENT
For the third acknowledgment sequence, an MU-BAR Trigger Frame is aggregated to every PSDU included in
the DL MU PPDU and the BlockAck responses are sent in TB PPDUs, as shown in Figure Acknowledgment of DL
MU frames via aggregated MU-BAR Trigger Frames.
[image] Acknowledgment of DL MU frames via aggregated MU-BAR Trigger Frames.UNINDENT
For UL OFDMA, both BSRP Trigger Frames and Basic Trigger Frames are supported, as shown in Figure
Acknowledgment of DL MU frames via aggregated MU-BAR Trigger Frames. A BSRP Trigger Frame is sent by an
AP to solicit stations to send QoS Null frames containing Buffer Status Reports. A Basic Trigger Frame
is sent by an AP to solicit stations to send data frames in TB PPDUs, which are acknowledged by the AP
via a Multi-STA BlockAck frame.
[image] Acknowledgment of DL MU frames via aggregated MU-BAR Trigger Frames.UNINDENT
Multi-User Scheduler
A new component, named MultiUserScheduler, is in charge of determining what frame exchange sequence the
aggregated AP has to perform when gaining a TXOP (DL OFDMA, UL OFDMA or BSRP Trigger Frame), along with
the information needed to perform the selected frame exchange sequence (e.g., the set of PSDUs to send in
case of DL OFDMA). MultiUserScheduler is an abstract base class. Currently, the only available subclass
is RrMultiUserScheduler. By default, no multi-user scheduler is aggregated to an AP (hence, OFDMA is not
enabled).
Round-robin Multi-User Scheduler
The Round-robin Multi-User Scheduler dynamically assigns a priority to each station to ensure airtime
fairness in the selection of stations for DL multi-user transmissions. The NStations attribute enables to
set the maximum number of stations that can be the recipients of a DL multi-user frame. Therefore, every
time an HE AP accesses the channel to transmit a DL multi-user frame, the scheduler determines the number
of stations the AP has frames to send to (capped at the value specified through the mentioned attribute)
and attempts to allocate equal sized RUs to as many such stations as possible without leaving RUs of the
same size unused. For instance, if the channel bandwidth is 40 MHz and the determined number of stations
is 5, the first 4 stations (in order of priority) are allocated a 106-tone RU each (if 52-tone RUs were
allocated, we would have three 52-tone RUs unused). If central 26-tone RUs can be allocated (as
determined by the UseCentral26TonesRus attribute), possible stations that have not been allocated an RU
are assigned one of such 26-tone RU. In the previous example, the fifth station would have been allocated
one of the two available central 26-tone RUs.
When UL OFDMA is enabled (via the EnableUlOfdma attribute), every DL OFDMA frame exchange is followed by
an UL OFDMA frame exchange involving the same set of stations and the same RU allocation as the preceding
DL multi-user frame. The transmission of a BSRP Trigger Frame can optionally (depending on the value of
the EnableBsrp attribute) precede the transmission of a Basic Trigger Frame in order for the AP to
collect information about the buffer status of the stations.
Ack manager
Since the introduction of the IEEE 802.11e amendment, multiple acknowledgment policies are available,
which are coded in the Ack Policy subfield in the QoS Control field of QoS Data frames (see Section
9.2.4.5.4 of the IEEE 802.11-2016 standard). For instance, an A-MPDU can be sent with the Normal Ack or
Implicit Block Ack Request policy, in which case the receiver replies with a Normal Ack or a Block Ack
depending on whether the A-MPDU contains a single MPDU or multiple MPDUs, or with the Block Ack policy,
in which case the receiver waits to receive a Block Ack Request in the future to which it replies with a
Block Ack.
WifiAckManager is the abstract base class introduced to provide an interface for multiple ack managers.
Currently, the default ack manager is the WifiDefaultAckManager.
WifiDefaultAckManager
The WifiDefaultAckManager allows to determine which acknowledgment policy to use depending on the value
of its attributes:
• UseExplicitBar: used to determine the ack policy to use when a response is needed from the recipient
and the current transmission includes multiple frames (A-MPDU) or there are frames transmitted
previously for which an acknowledgment is needed. If this attribute is true, the Block Ack policy is
used. Otherwise, the Implicit Block Ack Request policy is used.
• BaThreshold: used to determine when the originator of a Block Ack agreement needs to request a response
from the recipient. A value of zero means that a response is requested at every frame transmission.
Otherwise, a non-zero value (less than or equal to 1) means that a response is requested upon
transmission of a frame whose sequence number is distant at least BaThreshold multiplied by the
transmit window size from the starting sequence number of the transmit window.
• DlMuAckSequenceType: used to select the acknowledgment sequence for DL MU frames (acknowledgment in
single-user format, acknowledgment via MU-BAR Trigger Frame sent as single-user frame, or
acknowledgment via MU-BAR Trigger Frames aggregated to the data frames).
Protection manager
The protection manager is in charge of determining the protection mechanism to use, if any, when sending
a frame.
WifiProtectionManager is the abstract base class introduced to provide an interface for multiple
protection managers. Currently, the default protection manager is the WifiDefaultProtectionManager.
WifiDefaultProtectionManager
The WifiDefaultProtectionManager selects a protection mechanism based on the information provided by the
remote station manager.
Rate control algorithms
Multiple rate control algorithms are available in ns-3. Some rate control algorithms are modeled after
real algorithms used in real devices; others are found in literature. The following rate control
algorithms can be used by the MAC low layer:
Algorithms found in real devices:
• ArfWifiManager (default for WifiHelper)
• OnoeWifiManager
• ConstantRateWifiManager
• MinstrelWifiManager
• MinstrelHtWifiManager
Algorithms in literature:
• IdealWifiManager
• AarfWifiManager [lacage2004aarfamrr]
• AmrrWifiManager [lacage2004aarfamrr]
• CaraWifiManager [kim2006cara]
• RraaWifiManager [wong2006rraa]
• AarfcdWifiManager [maguolo2008aarfcd]
• ParfWifiManager [akella2007parf]
• AparfWifiManager [chevillat2005aparf]
• ThompsonSamplingWifiManager [krotov2020rate]
ConstantRateWifiManager
The constant rate control algorithm always uses the same transmission mode for every packet. Users can
set a desired ‘DataMode’ for all ‘unicast’ packets and ‘ControlMode’ for all ‘request’ control packets
(e.g. RTS).
To specify different data mode for non-unicast packets, users must set the ‘NonUnicastMode’ attribute of
the WifiRemoteStationManager. Otherwise, WifiRemoteStationManager will use a mode with the lowest rate
for non-unicast packets.
The 802.11 standard is quite clear on the rules for selection of transmission parameters for control
response frames (e.g. CTS and ACK). ns-3 follows the standard and selects the rate of control response
frames from the set of basic rates or mandatory rates. This means that control response frames may be
sent using different rate even though the ConstantRateWifiManager is used. The ControlMode attribute of
the ConstantRateWifiManager is used for RTS frames only. The rate of CTS and ACK frames are selected
according to the 802.11 standard. However, users can still manually add WifiMode to the basic rate set
that will allow control response frames to be sent at other rates. Please consult the project wiki on
how to do this.
Available attributes:
• DataMode (default WifiMode::OfdmRate6Mbps): specify a mode for all non-unicast packets
• ControlMode (default WifiMode::OfdmRate6Mbps): specify a mode for all ‘request’ control packets
IdealWifiManager
The ideal rate control algorithm selects the best mode according to the SNR of the previous packet sent.
Consider node A sending a unicast packet to node B. When B successfully receives the packet sent from A,
B records the SNR of the received packet into a ns3::SnrTag and adds the tag to an ACK back to A. By
doing this, A is able to learn the SNR of the packet sent to B using an out-of-band mechanism (thus the
name ‘ideal’). A then uses the SNR to select a transmission mode based on a set of SNR thresholds, which
was built from a target BER and mode-specific SNR/BER curves.
Available attribute:
• BerThreshold (default 1e-6): The maximum Bit Error Rate that is used to calculate the SNR threshold for
each mode.
Note that the BerThreshold has to be low enough to select a robust enough MCS (or mode) for a given SNR
value, without being too restrictive on the target BER. Indeed we had noticed that the previous default
value (i.e. 1e-5) led to the selection of HE MCS-11 which resulted in high PER. With this new default
value (i.e. 1e-6), a HE STA moving away from a HE AP has smooth throughput decrease (whereas with 1e-5,
better performance was seen further away, which is not “ideal”).
ThompsonSamplingWifiManager
Thompson Sampling (TS) is a classical solution to the Multi-Armed Bandit problem.
ThompsonSamplingWifiManager implements a rate control algorithm based on TS with the goal of providing a
simple statistics-based algorithm with a low number of parameters.
The algorithm maintains the number of successful transmissions \alpha_i and the number of unsuccessful
transmissions \beta_i for each MCS i, both of which are initially set to zero.
To select MCS for a data frame, the algorithm draws a sample frame success rate q_i from the beta
distribution with shape parameters (1 + \alpha_i, 1 + \beta_i) for each MCS and then selects MCS with the
highest expected throughput calculated as the sample frame success rate multiplied by MCS rate.
To account for changing channel conditions, exponential decay is applied to \alpha_i and \beta_i. The
rate of exponential decay is controlled with the Decay attribute which is the inverse of the time
constant. Default value of 1 Hz results in using exponential window with the time constant of 1 second.
Setting this value to zero effectively disables exponential decay and can be used in static scenarios.
Control frames are always transmitted using the most robust MCS, except when the standard specifies
otherwise, such as for ACK frames.
As the main goal of this algorithm is to provide a stable baseline, it does not take into account backoff
overhead, inter-frame spaces and aggregation for MCS rate calculation. For an example of a more complex
statistics-based rate control algorithm used in real devices, consider Minstrel-HT described below.
MinstrelWifiManager
The minstrel rate control algorithm is a rate control algorithm originated from madwifi project. It is
currently the default rate control algorithm of the Linux kernel.
Minstrel keeps track of the probability of successfully sending a frame of each available rate. Minstrel
then calculates the expected throughput by multiplying the probability with the rate. This approach is
chosen to make sure that lower rates are not selected in favor of the higher rates (since lower rates are
more likely to have higher probability).
In minstrel, roughly 10 percent of transmissions are sent at the so-called lookaround rate. The goal of
the lookaround rate is to force minstrel to try higher rate than the currently used rate.
For a more detailed information about minstrel, see [linuxminstrel].
MinstrelHtWifiManager
This is the extension of minstrel for 802.11n/ac/ax.
802.11ax OBSS PD spatial reuse
802.11ax mode supports OBSS PD spatial reuse feature. OBSS PD stands for Overlapping Basic Service Set
Preamble-Detection. OBSS PD is an 802.11ax specific feature that allows a STA, under specific
conditions, to ignore an inter-BSS PPDU.
OBSS PD Algorithm
ObssPdAlgorithm is the base class of OBSS PD algorithms. It implements the common functionalities.
First, it makes sure the necessary callbacks are setup. Second, when a PHY reset is requested by the
algorithm, it performs the computation to determine the TX power restrictions and informs the PHY object.
The PHY keeps tracks of incoming requests from the MAC to get access to the channel. If a request is
received and if PHY reset(s) indicating TX power limitations occured before a packet was transmitted, the
next packet to be transmitted will be sent with a reduced power. Otherwise, no TX power restrictions will
be applied.
Constant OBSS PD Algorithm
Constant OBSS PD algorithm is a simple OBSS PD algorithm implemented in the ConstantObssPdAlgorithm
class.
Once a HE preamble and its header have been received by the PHY, ConstantObssPdAlgorithm:: ReceiveHeSig
is triggered. The algorithm then checks whether this is an OBSS frame by comparing its own BSS color
with the BSS color of the received preamble. If this is an OBSS frame, it compares the received RSSI
with its configured OBSS PD level value. The PHY then gets reset to IDLE state in case the received RSSI
is lower than that constant OBSS PD level value, and is informed about a TX power restrictions.
Note: since our model is based on a single threshold, the PHY only supports one restricted power level.
Modifying Wifi model
Modifying the default wifi model is one of the common tasks when performing research. We provide an
overview of how to make changes to the default wifi model in this section. Depending on your goal, the
common tasks are (in no particular order):
• Creating or modifying the default Wi-Fi frames/headers by making changes to wifi-mac-header.*.
• MAC low modification. For example, handling new/modified control frames (think RTS/CTS/ACK/Block ACK),
making changes to two-way transaction/four-way transaction. Users usually make changes to
frame-exchange-manager.* or its subclasses to accomplish this. Handling of control frames is performed
in FrameExchangeManager::ReceiveMpdu.
• MAC high modification. For example, handling new management frames (think beacon/probe), beacon/probe
generation. Users usually make changes to regular-wifi-mac.*,``sta-wifi-mac.*``, ap-wifi-mac.*, or
adhoc-wifi-mac.* to accomplish this.
• Wi-Fi queue management. The files txop.* and qos-txop.* are of interest for this task.
• Channel access management. Users should modify the files channel-access-manager.*, which grant access
to Txop and QosTxop.
• Fragmentation and RTS threholds are handled by Wi-Fi remote station manager. Note that Wi-Fi remote
station manager simply indicates if fragmentation and RTS are needed. Fragmentation is handled by Txop
or QosTxop while RTS/CTS transaction is handled by FrameExchangeManager.
• Modifying or creating new rate control algorithms can be done by creating a new child class of Wi-Fi
remote station manager or modifying the existing ones.
User Documentation
Using the WifiNetDevice
The modularity provided by the implementation makes low-level configuration of the WifiNetDevice powerful
but complex. For this reason, we provide some helper classes to perform common operations in a simple
matter, and leverage the ns-3 attribute system to allow users to control the parameterization of the
underlying models.
Users who use the low-level ns-3 API and who wish to add a WifiNetDevice to their node must create an
instance of a WifiNetDevice, plus a number of constituent objects, and bind them together appropriately
(the WifiNetDevice is very modular in this regard, for future extensibility). At the low-level API, this
can be done with about 20 lines of code (see ns3::WifiHelper::Install, and
ns3::YansWifiPhyHelper::Create). They also must create, at some point, a Channel, which also contains a
number of constituent objects (see ns3::YansWifiChannelHelper::Create).
However, a few helpers are available for users to add these devices and channels with only a few lines of
code, if they are willing to use defaults, and the helpers provide additional API to allow the passing of
attribute values to change default values. Commonly used attribute values are listed in the Attributes
section. The scripts in examples/wireless can be browsed to see how this is done. Next, we describe the
common steps to create a WifiNetDevice from the bottom layer (Channel) up to the device layer
(WifiNetDevice).
To create a WifiNetDevice, users need to follow these steps:
• Decide on which physical layer framework, the SpectrumWifiPhy or YansWifiPhy, to use. This will affect
which Channel and Phy type to use.
• Configure the Channel: Channel takes care of getting signal from one device to other devices on the
same Wi-Fi channel. The main configurations of WifiChannel are propagation loss model and propagation
delay model.
• Configure the WifiPhy: WifiPhy takes care of actually sending and receiving wireless signal from
Channel. Here, WifiPhy decides whether each frame will be successfully decoded or not depending on the
received signal strength and noise. Thus, the main configuration of WifiPhy is the error rate model,
which is the one that actually calculates the probability of successfully decoding the frame based on
the signal.
• Configure WifiMac: this step is more related to the architecture and device level. The users configure
the wifi architecture (i.e. ad-hoc or ap-sta) and whether QoS (802.11e), HT (802.11n) and/or VHT
(802.11ac) and/or HE (802.11ax) features are supported or not.
• Create WifiDevice: at this step, users configure the desired wifi standard (e.g. 802.11b, 802.11g,
802.11a, 802.11n, 802.11ac or 802.11ax) and rate control algorithm.
• Configure mobility: finally, a mobility model is (usually) required before WifiNetDevice can be used;
even if the devices are stationary, their relative positions are needed for propagation loss
calculations.
The following sample code illustrates a typical configuration using mostly default values in the
simulator, and infrastructure mode:
NodeContainer wifiStaNode;
wifiStaNode.Create (10); // Create 10 station node objects
NodeContainer wifiApNode;
wifiApNode.Create (1); // Create 1 access point node object
// Create a channel helper and phy helper, and then create the channel
YansWifiChannelHelper channel = YansWifiChannelHelper::Default ();
YansWifiPhyHelper phy = YansWifiPhyHelper::Default ();
phy.SetChannel (channel.Create ());
// Create a WifiMacHelper, which is reused across STA and AP configurations
WifiMacHelper mac;
// Create a WifiHelper, which will use the above helpers to create
// and install Wifi devices. Configure a Wifi standard to use, which
// will align various parameters in the Phy and Mac to standard defaults.
WifiHelper wifi;
wifi.SetStandard (WIFI_STANDARD_80211n_5GHZ);
// Declare NetDeviceContainers to hold the container returned by the helper
NetDeviceContainer wifiStaDevices;
NetDeviceContainer wifiApDevice;
// Perform the installation
mac.SetType ("ns3::StaWifiMac");
wifiStaDevices = wifi.Install (phy, mac, wifiStaNodes);
mac.SetType ("ns3::ApWifiMac");
wifiApDevice = wifi.Install (phy, mac, wifiApNode);
At this point, the 11 nodes have Wi-Fi devices configured, attached to a common channel. The rest of
this section describes how additional configuration may be performed.
YansWifiChannelHelper
The YansWifiChannelHelper has an unusual name. Readers may wonder why it is named this way. The reference
is to the yans simulator from which this model is taken. The helper can be used to create a
YansWifiChannel with a default PropagationLoss and PropagationDelay model.
Users will typically type code such as:
YansWifiChannelHelper wifiChannelHelper = YansWifiChannelHelper::Default ();
Ptr<Channel> wifiChannel = wifiChannelHelper.Create ();
to get the defaults. Specifically, the default is a channel model with a propagation delay equal to a
constant, the speed of light (ns3::ConstantSpeedPropagationDelayModel), and a propagation loss based on a
default log distance model (ns3::LogDistancePropagationLossModel), using a default exponent of 3. Please
note that the default log distance model is configured with a reference loss of 46.6777 dB at reference
distance of 1m. The reference loss of 46.6777 dB was calculated using Friis propagation loss model at
5.15 GHz. The reference loss must be changed if 802.11b, 802.11g, 802.11n (at 2.4 GHz) or 802.11ax (at
2.4 GHz) are used since they operate at 2.4 Ghz.
Note the distinction above in creating a helper object vs. an actual simulation object. In ns-3, helper
objects (used at the helper API only) are created on the stack (they could also be created with operator
new and later deleted). However, the actual ns-3 objects typically inherit from class ns3::Object and
are assigned to a smart pointer. See the chapter in the ns-3 manual for a discussion of the ns-3 object
model, if you are not familiar with it.
The following two methods are useful when configuring YansWifiChannelHelper:
• YansWifiChannelHelper::AddPropagationLoss adds a PropagationLossModel; if one or more
PropagationLossModels already exist, the new model is chained to the end
• YansWifiChannelHelper::SetPropagationDelay sets a PropagationDelayModel (not chainable)
YansWifiPhyHelper
Physical devices (base class ns3::WifiPhy) connect to ns3::YansWifiChannel models in ns-3. We need to
create WifiPhy objects appropriate for the YansWifiChannel; here the YansWifiPhyHelper will do the work.
The YansWifiPhyHelper class configures an object factory to create instances of a YansWifiPhy and adds
some other objects to it, including possibly a supplemental ErrorRateModel and a pointer to a
MobilityModel. The user code is typically:
YansWifiPhyHelper wifiPhyHelper;
wifiPhyHelper.SetChannel (wifiChannel);
The default YansWifiPhyHelper is configured with TableBasedErrorRateModel
(ns3::TableBasedErrorRateModel). You can change the error rate model by calling the
YansWifiPhyHelper::SetErrorRateModel method.
Optionally, if pcap tracing is needed, a user may use the following command to enable pcap tracing:
YansWifiPhyHelper::SetPcapDataLinkType (enum SupportedPcapDataLinkTypes dlt)
ns-3 supports RadioTap and Prism tracing extensions for 802.11.
Note that we haven’t actually created any WifiPhy objects yet; we’ve just prepared the YansWifiPhyHelper
by telling it which channel it is connected to. The Phy objects are created in the next step.
In order to enable 802.11n/ac/ax MIMO, the number of antennas as well as the number of supported spatial
streams need to be configured. For example, this code enables MIMO with 2 antennas and 2 spatial
streams:
wifiPhyHelper.Set ("Antennas", UintegerValue (2));
wifiPhyHelper.Set ("MaxSupportedTxSpatialStreams", UintegerValue (2));
wifiPhyHelper.Set ("MaxSupportedRxSpatialStreams", UintegerValue (2));
It is also possible to configure less streams than the number of antennas in order to benefit from
diversity gain, and to define different MIMO capabilities for downlink and uplink. For example, this
code configures a node with 3 antennas that supports 2 spatial streams in downstream and 1 spatial stream
in upstream:
wifiPhyHelper.Set ("Antennas", UintegerValue (3));
wifiPhyHelper.Set ("MaxSupportedTxSpatialStreams", UintegerValue (2));
wifiPhyHelper.Set ("MaxSupportedRxSpatialStreams", UintegerValue (1));
802.11n PHY layer can support both 20 (default) or 40 MHz channel width, and 802.11ac/ax PHY layer can
use either 20, 40, 80 (default) or 160 MHz channel width. See below for further documentation on setting
the frequency, channel width, and channel number.
WifiHelper wifi;
wifi.SetStandard (WIFI_STANDARD_80211ac);
wifi.SetRemoteStationManager ("ns3::ConstantRateWifiManager",
"DataMode", StringValue ("VhtMcs9"),
"ControlMode", StringValue ("VhtMcs0"));
//Install PHY and MAC
Ssid ssid = Ssid ("ns3-wifi");
WifiMacHelper mac;
mac.SetType ("ns3::StaWifiMac",
"Ssid", SsidValue (ssid),
"ActiveProbing", BooleanValue (false));
NetDeviceContainer staDevice;
staDevice = wifi.Install (phy, mac, wifiStaNode);
mac.SetType ("ns3::ApWifiMac",
"Ssid", SsidValue (ssid));
NetDeviceContainer apDevice;
apDevice = wifi.Install (phy, mac, wifiApNode);
Channel, frequency, and channel width configuration
There are a few ns3::WifiPhy parameters that are related, and cannot be set completely independently,
concerning the frequency and channel width that the device is tuned to. These are:
• WifiPhyStandard: For example, 802.11b, 802.11n, etc.
• Frequency
• ChannelWidth
• ChannelNumber
It is possible to set the above to incompatible combinations (e.g. channel number 1 with 40 MHz channel
width on frequency 4915 MHz). In addition, the latter three values above are attributes; it is possible
to set them in a number of ways:
• by setting global configuration default; e.g.
Config::SetDefault ("ns3::WifiPhy::ChannelNumber", UintegerValue (3));
• by setting an attribute value in the helper; e.g.
YansWifiPhyHelper wifiPhyHelper = YansWifiPhyHelper::Default ();
wifiPhyHelper.Set ("ChannelNumber", UintegerValue (3));
• by setting the WifiHelper::SetStandard (enum WifiStandard) method; and
• by performing post-installation configuration of the option, either via a Ptr to the WifiPhy object, or
through the Config namespace; e.g.:
Config::Set ("/NodeList/0/DeviceList/*/$ns3::WifiNetDevice/Phy/$ns3::WifiPhy/ChannelNumber",
UintegerValue (3));
This section provides guidance on how to configure these settings in a coherent manner, and what happens
if non-standard values are chosen.
WifiHelper::SetStandard()
WifiHelper::SetStandard () is a method to set various parameters in the Mac and Phy to standard values
and some reasonable defaults. For example, SetStandard (WIFI_STANDARD_80211a) will set the WifiPhy to
Channel 36 in the 5 GHz band, among other settings appropriate for 802.11a.
The following values for WifiStandard are defined in src/wifi/model/wifi-standards.h:
WIFI_STANDARD_80211a,
WIFI_STANDARD_80211b,
WIFI_STANDARD_80211g,
WIFI_STANDARD_80211p,
WIFI_STANDARD_80211n_2_4GHZ,
WIFI_STANDARD_80211n_5GHZ,
WIFI_STANDARD_80211ac,
WIFI_STANDARD_80211ax_2_4GHZ,
WIFI_STANDARD_80211ax_5GHZ,
WIFI_STANDARD_80211ax_6GHZ
By default, the WifiHelper (the typical use case for WifiPhy creation) will configure the
WIFI_STANDARD_80211a standard by default. Other values for standards should be passed explicitly to the
WifiHelper object.
If user has not already separately configured Frequency or ChannelNumber when SetStandard is called, the
user obtains default values, in addition (e.g. channel 1 for 802.11b/g, or channel 36 for a/n), in
addition to an appropriate ChannelWidth value for the standard (typically, 20 MHz, but 80 MHz for
802.11ac/ax).
WifiPhy attribute interactions
Users should keep in mind that the two attributes that matter most within the model code are
WifiPhy::Frequency and WifiPhy::ChannelWidth; these are the ones directly used to set transmission
parameters. WifiPhy::ChannelNumber and WifiHelper::SetStandard () are convenience shorthands for setting
frequency and channel width. The ns3::WifiPhy contains code to keep these values aligned and to generate
runtime errors in some cases if users set these attributes to incompatible values.
The pair (WifiPhyStandard, ChannelNumber) is an alias for a pair of (Frequency/ChannelWidth) items.
Valid combinations are stored in a map within WifiPhy that is populated with well-known values but that
can be dynamically extended at runtime.
WifiPhy::Frequency
The WifiPhy channel center frequency is set by the attribute Frequency in the class WifiPhy. It is
expressed in units of MHz. By default, this attribute is set to the value 0 to indicate that no value is
configured.
Note that this is a change in definition from ns-3.25 and earlier releases, where this attribute referred
to the start of the overall frequency band on which the channel resides, not the specific channel center
frequency.
WifiPhy::ChannelWidth
The WifiPhy channel width is set by the attribute ChannelWidth in the class WifiPhy. It is expressed in
units of MHz. By default, this attribute is set to the value 20. Allowable values are 5, 10, 20, 22,
40, 80, or 160 (MHz).
WifiPhy::ChannelNumber
Several channel numbers are defined and well-known in practice. However, valid channel numbers vary by
geographical region around the world, and there is some overlap between the different standards.
In ns-3, the class WifiPhy contains an attribute ChannelNumber that is, by default, set to the value 0.
The value 0 indicates that no channel number has been set by the user.
In ns-3, a ChannelNumber may be defined or unknown. These terms are not found in the code; they are just
used to describe behavior herein.
If a ChannelNumber is defined, it means that WifiPhy has stored a map of ChannelNumber to the center
frequency and channel width commonly known for that channel in practice. For example:
• Channel 1, when IEEE 802.11b is configured, corresponds to a channel width of 22 MHz and a center
frequency of 2412 MHz.
• Channel 36, when IEEE 802.11n is configured at 5 GHz, corresponds to a channel width of 20 MHz and a
center frequency of 5180 MHz.
The following channel numbers are well-defined for 2.4 GHz standards:
• channels 1-14 with ChannelWidth of 22 MHz for 802.11b
• channels 1-14 with ChannelWidth of 20 MHz for 802.11n-2.4GHz and 802.11g
The following channel numbers are well-defined for 5 GHz standards:
5 GHz channel numbers
┌─────────────────┬───────────────────────────────────────┐
│ChannelWidth │ ChannelNumber │
├─────────────────┼───────────────────────────────────────┤
│20 MHz │ 36, 40, 44, 48, 52, 56, 60, 64, 100, │
│ │ 104, 108, 112, 116, 120, 124, 128, │
│ │ 132, 136, 140, 144, 149, 153, 161, │
│ │ 165, 169 │
├─────────────────┼───────────────────────────────────────┤
│40 MHz │ 38, 46, 54, 62, 102, 110, 118, 126, │
│ │ 134, 142, 151, 159 │
├─────────────────┼───────────────────────────────────────┤
│80 MHz │ 42, 58, 106, 122, 138, 155 │
├─────────────────┼───────────────────────────────────────┤
│160 MHz │ 50, 114 │
├─────────────────┼───────────────────────────────────────┤
│10 MHz (802.11p) │ 172, 174, 176, 178, 180, 182, 184 │
├─────────────────┼───────────────────────────────────────┤
│5 MHz (802.11p) │ 171, 173, 175, 177, 179, 181, 183 │
└─────────────────┴───────────────────────────────────────┘
The following channel numbers are well-defined for 6 GHz standards (802.11ax only):
6 GHz channel numbers
┌─────────────┬───────────────────────────────────────┐
│ChannelWidth │ ChannelNumber │
├─────────────┼───────────────────────────────────────┤
│20 MHz │ 1, 5, 9, 13, 17, 21, 25, 29, 33, 37, │
│ │ 41, 45, 49, 53, 57, 61, 65, 69, 73, │
│ │ 77, 81, 85, 89, 93, 97, 101, 105, │
│ │ 109, 113, 117, 121, 125, 129, 133, │
│ │ 137, 141, 145, 149, 153, 157, 161, │
│ │ 165, 169, 173, 177, 181, 185, 189, │
│ │ 193, 197, 201, 205, 209, 213, 217, │
│ │ 221, 225, 229, 233 │
├─────────────┼───────────────────────────────────────┤
│40 MHz │ 3, 11, 19, 27, 35, 43, 51, 59, 67, │
│ │ 75, 83, 91, 99, 107, 115, 123, 131, │
│ │ 139, 147, 155, 163, 171, 179, 187, │
│ │ 195, 203, 211, 219, 227 │
├─────────────┼───────────────────────────────────────┤
│80 MHz │ 7, 23, 39, 55, 71, 87, 103, 119, 135, │
│ │ 151, 167, 183, 199, 215 │
├─────────────┼───────────────────────────────────────┤
│160 MHz │ 15, 47, 79, 111, 143, 175, 207 │
└─────────────┴───────────────────────────────────────┘
The channel number may be set either before or after creation of the WifiPhy object.
If an unknown channel number (other than zero) is configured, the simulator will exit with an error; for
instance, such as:
Ptr<WifiPhy> wifiPhy = ...;
wifiPhy->SetAttribute ("ChannelNumber", UintegerValue (1321));
The known channel numbers are defined in the implementation file src/wifi/model/wifi-phy.cc. Of course,
this file may be edited by users to extend to additional channel numbers. Below, we describe how new
channel numbers may be defined dynamically at run-time.
If a known channel number is configured against an incorrect value of the WifiPhyStandard, the simulator
will exit with an error; for instance, such as:
WifiHelper wifi;
wifi.SetStandard (WIFI_STANDARD_80211n_5GHZ);
...
Ptr<WifiPhy> wifiPhy = ...;
wifiPhy->SetAttribute ("ChannelNumber", UintegerValue (14));
In the above, while channel number 14 is well-defined in practice for 802.11b only, it is for 2.4 GHz
band, not 5 GHz band.
Defining a new channel number
Users may define their own channel number so that they can later refer to the channel by number.
The method is WifiPhy::DefineChannelNumber () and it takes the following arguments:
• uint16_t channelNumber
• enum WifiPhyStandard standard
• uint32_t frequency
• uint32_t channelWidth
The pair of (channelNumber, standard) are used as an index to a map that returns a Frequency and
ChannelWidth. By calling this method, one can dynamically add members to the map. For instance, let’s
suppose that you previously configured WIFI_PHY_STANDARD_80211a, and wanted to define a new channel
number ‘34’ of width 20 MHz and at center frequency 5160 MHz.
If you try to simply configure ChannelNumber to the value 34, it will fail, since 34 is undefined.
However, you can use DefineChannelNumber as follows:
Ptr<WifiPhy> wifiPhy = ...;
wifiPhy->DefineChannelNumber (34, WIFI_PHY_STANDARD_80211a, 5160, 20);
and then later you can refer to channel number 34 in your program, which will configure a center
operating frequency of 5160 MHz and a width of 20 MHz.
The steps can be repeated to explicitly configure the same channel for multiple standards:
wifiPhy->DefineChannelNumber (34, WIFI_PHY_STANDARD_80211a, 5160, 20);
wifiPhy->DefineChannelNumber (34, WIFI_PHY_STANDARD_80211n_5GHZ, 5160, 20);
or for a wildcard, unspecified standard:
wifiPhy->DefineChannelNumber (34, WIFI_PHY_STANDARD_UNSPECIFIED, 5160, 20);
Order of operation issues
Depending on the default values used and the order of operation in setting the values for the standard,
channel width, frequency, and channel number, different configurations can be obtained. Below are some
common use cases.
• (accepting the standard defaults): If a user has not already separately configured frequency or
channel number when WifiHelper::SetStandard () is called, the user gets default values (e.g. channel 1
for 802.11b/g/n, channel 36 for a/n with 20 MHz channel widths and channel 42 for ac/ax with 80 MHz
channel widths)
• (overwriting the standard channel): If the user has previously configured (e.g. via SetDefault) either
frequency or channel number when SetStandard is called, and the frequency or channel number are
appropriate for the standard being configured, they are not overwritten
• (changing the standard channel after Install): The user may also call WifiHelper::SetStandard () after
Install () and either configure the frequency to something different, or configure the channel number
to something different. Note that if the channel number is undefined for the standard that was
previously set, an error will occur.
• (changing to non-standard frequency): If the user configures a frequency outside the standardized
frequency range for the current WifiPhyStandard, this is OK. This allows users to experiment with wifi
on e.g. whitespace frequencies but still use SetStandard to set all of the other configuration details.
• (interaction between channel number and frequency): If the user sets Frequency to a different value
than the currently configured ChannelNumber (or if ChannelNumber is zero), then the ChannelNumber is
set to a new channel number if known, or to zero if unknown.
• example: ChannelNumber previously set to 36, user sets Frequency to 5200, then ChannelNumber gets
automatically set to 40
• example: ChannelNumber set to 36, user later sets Frequency to 5185, ChannelNumber gets reset to 0
In summary, ChannelNumber and Frequency follow each other. ChannelNumber sets both Frequency and
ChannelWidth if the channel number has been defined for the standard. Setting ChannelWidth has no effect
on Frequency or ChannelNumber. Setting Frequency will set ChannelNumber to either the defined value for
that Wi-Fi standard, or to the value 0 if undefined.
SpectrumWifiPhyHelper
The API for this helper closely tracks the API of the YansWifiPhyHelper, with the exception that a
channel of type ns3::SpectrumChannel instead of type ns3::YansWifiChannel must be used with it.
WifiMacHelper
The next step is to configure the MAC model. We use WifiMacHelper to accomplish this. WifiMacHelper
takes care of both the MAC low model and MAC high model, and configures an object factory to create
instances of a ns3::WifiMac. It is used to configure MAC parameters like type of MAC, and to select
whether 802.11/WMM-style QoS and/or 802.11n-style High Throughput (HT) and/or 802.11ac-style Very High
Throughput (VHT) support and/or 802.11ax-style High Efficiency (HE) support are/is required.
By default, it creates an ad-hoc MAC instance that does not have 802.11e/WMM-style QoS nor 802.11n-style
High Throughput (HT) nor 802.11ac-style Very High Throughput (VHT) nor 802.11ax-style High Efficiency
(HE) support enabled.
For example the following user code configures a non-QoS and non-HT/non-VHT/non-HE MAC that will be a
non-AP STA in an infrastructure network where the AP has SSID ns-3-ssid:
WifiMacHelper wifiMacHelper;
Ssid ssid = Ssid ("ns-3-ssid");
wifiMacHelper.SetType ("ns3::StaWifiMac",
"Ssid", SsidValue (ssid),
"ActiveProbing", BooleanValue (false));
The following code shows how to create an AP with QoS enabled:
WifiMacHelper wifiMacHelper;
wifiMacHelper.SetType ("ns3::ApWifiMac",
"Ssid", SsidValue (ssid),
"QosSupported", BooleanValue (true),
"BeaconGeneration", BooleanValue (true),
"BeaconInterval", TimeValue (Seconds (2.5)));
To create ad-hoc MAC instances, simply use ns3::AdhocWifiMac instead of ns3::StaWifiMac or
ns3::ApWifiMac.
With QoS-enabled MAC models it is possible to work with traffic belonging to four different Access
Categories (ACs): AC_VO for voice traffic, AC_VI for video traffic, AC_BE for best-effort traffic and
AC_BK for background traffic.
When selecting 802.11n as the desired wifi standard, both 802.11e/WMM-style QoS and 802.11n-style High
Throughput (HT) support gets enabled. Similarly when selecting 802.11ac as the desired wifi standard,
802.11e/WMM-style QoS, 802.11n-style High Throughput (HT) and 802.11ac-style Very High Throughput (VHT)
support gets enabled. And when selecting 802.11ax as the desired wifi standard, 802.11e/WMM-style QoS,
802.11n-style High Throughput (HT), 802.11ac-style Very High Throughput (VHT) and 802.11ax-style High
Efficiency (HE) support gets enabled.
For MAC instances that have QoS support enabled, the ns3::WifiMacHelper can be also used to set:
• block ack threshold (number of packets for which block ack mechanism should be used);
• block ack inactivity timeout.
For example the following user code configures a MAC that will be a non-AP STA with QoS enabled and a
block ack threshold for AC_BE set to 2 packets, in an infrastructure network where the AP has SSID
ns-3-ssid:
WifiMacHelper wifiMacHelper;
Ssid ssid = Ssid ("ns-3-ssid");
wifiMacHelper.SetType ("ns3::StaWifiMac",
"Ssid", SsidValue (ssid),
"QosSupported", BooleanValue (true),
"BE_BlockAckThreshold", UintegerValue (2),
"ActiveProbing", BooleanValue (false));
For MAC instances that have 802.11n-style High Throughput (HT) and/or 802.11ac-style Very High Throughput
(VHT) and/or 802.11ax-style High Efficiency (HE) support enabled, the ns3::WifiMacHelper can be also used
to set:
• MSDU aggregation parameters for a particular Access Category (AC) in order to use 802.11n/ac A-MSDU
feature;
• MPDU aggregation parameters for a particular Access Category (AC) in order to use 802.11n/ac A-MPDU
feature.
By default, MSDU aggregation feature is disabled for all ACs and MPDU aggregation is enabled for AC_VI
and AC_BE, with a maximum aggregation size of 65535 bytes.
For example the following user code configures a MAC that will be a non-AP STA with HT and QoS enabled,
MPDU aggregation enabled for AC_VO with a maximum aggregation size of 65535 bytes, and MSDU aggregation
enabled for AC_BE with a maximum aggregation size of 7935 bytes, in an infrastructure network where the
AP has SSID ns-3-ssid:
WifiHelper wifi;
wifi.SetStandard (WIFI_STANDARD_80211n_5GHZ);
WifiMacHelper wifiMacHelper;
Ssid ssid = Ssid ("ns-3-ssid");
wifiMacHelper.SetType ("ns3::StaWifiMac",
"Ssid", SsidValue (ssid),
"VO_MaxAmpduSize", UintegerValue (65535),
"BE_MaxAmsduSize", UintegerValue (7935),
"ActiveProbing", BooleanValue (false));
802.11ax APs support sending multi-user frames via DL OFDMA and UL OFDMA if a Multi-User Scheduler is
aggregated to the wifi MAC (by default no scheduler is aggregated). WifiMacHelper enables to aggregate a
Multi-User Scheduler to an AP and set its parameters:
WifiMacHelper wifiMacHelper;
wifiMacHelper.SetMultiUserScheduler ("ns3::RrMultiUserScheduler",
"EnableUlOfdma", BooleanValue (true),
"EnableBsrp", BooleanValue (false));
The Ack Manager is in charge of selecting the acknowledgment method among the three available methods
(see section wifi-mu-ack-sequences ). The default ack manager enables to select the acknowledgment
method, e.g.:
Config::SetDefault ("ns3::WifiDefaultAckManager::DlMuAckSequenceType",
EnumValue (WifiAcknowledgment::DL_MU_AGGREGATE_TF));
Selection of the Access Category (AC)
Since ns-3.26, the QosTag is no longer used to assign a user priority to an MSDU. Instead, the selection
of the Access Category (AC) for an MSDU is based on the value of the DS field in the IP header of the
packet (ToS field in case of IPv4, Traffic Class field in case of IPv6). Details on how to set the ToS
field of IPv4 packets are given in the Type-of-service section of the documentation. In summary, users
can create an address of type ns3::InetSocketAddress with the desired type of service value and pass it
to the application helpers:
InetSocketAddress destAddress (ipv4Address, udpPort);
destAddress.SetTos (tos);
OnOffHelper onoff ("ns3::UdpSocketFactory", destAddress);
Mapping the values of the DS field onto user priorities is performed similarly to the Linux mac80211
subsystem. Basically, the ns3::WifiNetDevice::SelectQueue() method sets the user priority (UP) of an MSDU
to the three most significant bits of the DS field. The Access Category is then determined based on the
user priority according to the following table:
┌───┬─────────────────┐
│UP │ Access Category │
├───┼─────────────────┤
│7 │ AC_VO │
├───┼─────────────────┤
│6 │ AC_VO │
├───┼─────────────────┤
│5 │ AC_VI │
├───┼─────────────────┤
│4 │ AC_VI │
├───┼─────────────────┤
│3 │ AC_BE │
├───┼─────────────────┤
│0 │ AC_BE │
├───┼─────────────────┤
│2 │ AC_BK │
├───┼─────────────────┤
│1 │ AC_BK │
└───┴─────────────────┘
TOS and DSCP values map onto user priorities and access categories according to the following table.
┌─────────────┬──────────────┬────┬─────────────────┐
│DiffServ PHB │ TOS (binary) │ UP │ Access Category │
├─────────────┼──────────────┼────┼─────────────────┤
│EF │ 101110xx │ 5 │ AC_VI │
├─────────────┼──────────────┼────┼─────────────────┤
│AF11 │ 001010xx │ 1 │ AC_BK │
├─────────────┼──────────────┼────┼─────────────────┤
│AF21 │ 010010xx │ 2 │ AC_BK │
├─────────────┼──────────────┼────┼─────────────────┤
│AF31 │ 011010xx │ 3 │ AC_BE │
├─────────────┼──────────────┼────┼─────────────────┤
│AF41 │ 100010xx │ 4 │ AC_VI │
├─────────────┼──────────────┼────┼─────────────────┤
│AF12 │ 001100xx │ 1 │ AC_BK │
├─────────────┼──────────────┼────┼─────────────────┤
│AF22 │ 010100xx │ 2 │ AC_BK │
├─────────────┼──────────────┼────┼─────────────────┤
│AF32 │ 011100xx │ 3 │ AC_BE │
└─────────────┴──────────────┴────┴─────────────────┘
│AF42 │ 100100xx │ 4 │ AC_VI │
├─────────────┼──────────────┼────┼─────────────────┤
│AF13 │ 001110xx │ 1 │ AC_BK │
├─────────────┼──────────────┼────┼─────────────────┤
│AF23 │ 010110xx │ 2 │ AC_BK │
├─────────────┼──────────────┼────┼─────────────────┤
│AF33 │ 011110xx │ 3 │ AC_BE │
├─────────────┼──────────────┼────┼─────────────────┤
│AF43 │ 100110xx │ 4 │ AC_VI │
├─────────────┼──────────────┼────┼─────────────────┤
│CS0 │ 000000xx │ 0 │ AC_BE │
├─────────────┼──────────────┼────┼─────────────────┤
│CS1 │ 001000xx │ 1 │ AC_BK │
├─────────────┼──────────────┼────┼─────────────────┤
│CS2 │ 010000xx │ 2 │ AC_BK │
├─────────────┼──────────────┼────┼─────────────────┤
│CS3 │ 011000xx │ 3 │ AC_BE │
├─────────────┼──────────────┼────┼─────────────────┤
│CS4 │ 100000xx │ 4 │ AC_VI │
├─────────────┼──────────────┼────┼─────────────────┤
│CS5 │ 101000xx │ 5 │ AC_VI │
├─────────────┼──────────────┼────┼─────────────────┤
│CS6 │ 110000xx │ 6 │ AC_VO │
├─────────────┼──────────────┼────┼─────────────────┤
│CS7 │ 111000xx │ 7 │ AC_VO │
└─────────────┴──────────────┴────┴─────────────────┘
So, for example,:
destAddress.SetTos (0xc0);
will map to CS6, User Priority 6, and Access Category AC_VO. Also, the ns3-wifi-ac-mapping test suite
(defined in src/test/ns3wifi/wifi-ac-mapping-test-suite.cc) can provide additional useful information.
Note that ns3::WifiNetDevice::SelectQueue() also sets the packet priority to the user priority, thus
overwriting the value determined by the socket priority (users can read Socket-options for details on how
to set the packet priority). Also, given that the Traffic Control layer calls
ns3::WifiNetDevice::SelectQueue() before enqueuing the packet into a queue disc, it turns out that
queuing disciplines (such as PfifoFastQueueDisc) that classifies packets based on their priority will use
the user priority instead of the socket priority.
WifiHelper
We’re now ready to create WifiNetDevices. First, let’s create a WifiHelper with default settings:
WifiHelper wifiHelper;
What does this do? It sets the default wifi standard to 802.11a and sets the RemoteStationManager to
ns3::ArfWifiManager. You can change the RemoteStationManager by calling the
WifiHelper::SetRemoteStationManager method. To change the wifi standard, call the WifiHelper::SetStandard
method with the desired standard.
Now, let’s use the wifiPhyHelper and wifiMacHelper created above to install WifiNetDevices on a set of
nodes in a NodeContainer “c”:
NetDeviceContainer wifiContainer = WifiHelper::Install (wifiPhyHelper, wifiMacHelper, c);
This creates the WifiNetDevice which includes also a WifiRemoteStationManager, a WifiMac, and a WifiPhy
(connected to the matching Channel).
The WifiHelper::SetStandard method sets various default timing parameters as defined in the selected
standard version, overwriting values that may exist or have been previously configured. In order to
change parameters that are overwritten by WifiHelper::SetStandard, this should be done post-install using
Config::Set:
WifiHelper wifi;
wifi.SetStandard (WIFI_STANDARD_80211n_2_4GHZ);
wifi.SetRemoteStationManager ("ns3::ConstantRateWifiManager", "DataMode", StringValue("HtMcs7"), "ControlMode", StringValue("HtMcs0"));
//Install PHY and MAC
Ssid ssid = Ssid ("ns3-wifi");
WifiMacHelper mac;
mac.SetType ("ns3::StaWifiMac",
"Ssid", SsidValue (ssid),
"ActiveProbing", BooleanValue (false));
NetDeviceContainer staDevice;
staDevice = wifi.Install (phy, mac, wifiStaNode);
mac.SetType ("ns3::ApWifiMac",
"Ssid", SsidValue (ssid));
NetDeviceContainer apDevice;
apDevice = wifi.Install (phy, mac, wifiApNode);
//Once install is done, we overwrite the standard timing values
Config::Set ("/NodeList/*/DeviceList/*/$ns3::WifiNetDevice/Phy/Slot", TimeValue (MicroSeconds (slot)));
Config::Set ("/NodeList/*/DeviceList/*/$ns3::WifiNetDevice/Phy/Sifs", TimeValue (MicroSeconds (sifs)));
Config::Set ("/NodeList/*/DeviceList/*/$ns3::WifiNetDevice/Phy/Pifs", TimeValue (MicroSeconds (pifs)));
The WifiHelper can be used to set the attributes of the default ack policy selector
(ConstantWifiAckPolicySelector) or to select a different (user provided) ack policy selector, for each of
the available Access Categories. As an example, the following code can be used to set the BaThreshold
attribute of the default ack policy selector associated with BE AC to 0.5:
WifiHelper wifi;
wifi.SetAckPolicySelectorForAc (AC_BE, "ns3::ConstantWifiAckPolicySelector",
"BaThreshold", DoubleValue (0.5));
The WifiHelper is also used to configure OBSS PD spatial reuse for 802.11ax. The following lines
configure a WifiHelper to support OBSS PD spatial reuse using the ConstantObssPdAlgorithm with a
threshold set to -72 dBm:
WifiHelper wifi;
wifi.SetObssPdAlgorithm ("ns3::ConstantObssPdAlgorithm",
"ObssPdLevel", DoubleValue (-72.0));
There are many other ns-3 attributes that can be set on the above helpers to deviate from the default
behavior; the example scripts show how to do some of this reconfiguration.
HT configuration
HT is an acronym for High Throughput, a term synonymous with the IEEE 802.11n standard. Once the
ns3::WifiHelper::Install has been called and the user sets the standard to a variant that supports HT
capabilities (802.11n, 802.11ac, or 802.11ax), an HT configuration object will automatically be created
for the device. The configuration object is used to store and manage HT-specific attributes.
802.11n/ac PHY layer can use either long (800 ns) or short (400 ns) OFDM guard intervals. To configure
this parameter for a given device, the following lines of code could be used (in this example, it enables
the support of a short guard interval for the first station):
Ptr<NetDevice> nd = wifiStaDevices.Get (0);
Ptr<WifiNetDevice> wnd = nd->GetObject<WifiNetDevice> ();
Ptr<HtConfiguration> htConfiguration = wnd->GetHtConfiguration ();
htConfiguration->SetShortGuardIntervalSupported (true);
It is also possible to configure HT-specific attributes using Config::Set. The following line of code
enables the support of a short guard interval for all stations:
Config::Set (“/NodeList//DeviceList//$ns3::WifiNetDevice/HtConfiguration/ShortGuardIntervalSupported”,
BooleanValue (true));
VHT configuration
IEEE 802.11ac devices are also known as supporting Very High Throughput (VHT). Once the
ns3::WifiHelper::Install has been called and either the 802.11ac or 802.11ax 5 GHz standards are
configured, a VHT configuration object will be automatically created to manage VHT-specific attributes.
As of ns-3.29, however, there are no VHT-specific configuration items to manage; therefore, this object
is a placeholder for future growth.
HE configuration
IEEE 802.11ax is also known as High Efficiency (HE). Once the ns3::WifiHelper::Install has been called
and IEEE 802.11ax configured as the standard, an HE configuration object will automatically be created to
manage HE-specific attributes for 802.11ax devices.
802.11ax PHY layer can use either 3200 ns, 1600 ns or 800 ns OFDM guard intervals. To configure this
parameter, the following lines of code could be used (in this example, it enables the support of 1600 ns
guard interval), such as in this example code snippet:
Ptr<NetDevice> nd = wifiStaDevices.Get (0);
Ptr<WifiNetDevice> wnd = nd->GetObject<WifiNetDevice> ();
Ptr<HeConfiguration> heConfiguration = wnd->GetHeConfiguration ();
heConfiguration->SetGuardInterval (NanoSeconds (1600));
802.11ax allows extended compressed Block ACKs containing a 256-bits bitmap, making possible transmissions of A-MPDUs containing up to 256 MPDUs,
depending on the negotiated buffer size. In order to configure the buffer size of an 802.11ax device, the following line of code could be used::
heConfiguration->SetMpduBufferSize (256);
For transmitting large MPDUs, it might also be needed to increase the maximum aggregation size (see above).
Mobility configuration
Finally, a mobility model must be configured on each node with Wi-Fi device. Mobility model is used for
calculating propagation loss and propagation delay. Two examples are provided in the next section.
Users are referred to the chapter on Mobility module for detailed information.
Example configuration
We provide two typical examples of how a user might configure a Wi-Fi network – one example with an
ad-hoc network and one example with an infrastructure network. The two examples were modified from the
two examples in the examples/wireless folder (wifi-simple-adhoc.cc and wifi-simple-infra.cc). Users are
encouraged to see examples in the examples/wireless folder.
AdHoc WifiNetDevice configuration
In this example, we create two ad-hoc nodes equipped with 802.11a Wi-Fi devices. We use the
ns3::ConstantSpeedPropagationDelayModel as the propagation delay model and
ns3::LogDistancePropagationLossModel with the exponent of 3.0 as the propagation loss model. Both
devices are configured with ConstantRateWifiManager at the fixed rate of 12Mbps. Finally, we manually
place them by using the ns3::ListPositionAllocator:
std::string phyMode ("OfdmRate12Mbps");
NodeContainer c;
c.Create (2);
WifiHelper wifi;
wifi.SetStandard (WIFI_STANDARD_80211a);
YansWifiPhyHelper wifiPhy = YansWifiPhyHelper::Default ();
// ns-3 supports RadioTap and Prism tracing extensions for 802.11
wifiPhy.SetPcapDataLinkType (WifiPhyHelper::DLT_IEEE802_11_RADIO);
YansWifiChannelHelper wifiChannel;
wifiChannel.SetPropagationDelay ("ns3::ConstantSpeedPropagationDelayModel");
wifiChannel.AddPropagationLoss ("ns3::LogDistancePropagationLossModel",
"Exponent", DoubleValue (3.0));
wifiPhy.SetChannel (wifiChannel.Create ());
// Add a non-QoS upper mac, and disable rate control (i.e. ConstantRateWifiManager)
WifiMacHelper wifiMac;
wifi.SetRemoteStationManager ("ns3::ConstantRateWifiManager",
"DataMode",StringValue (phyMode),
"ControlMode",StringValue (phyMode));
// Set it to adhoc mode
wifiMac.SetType ("ns3::AdhocWifiMac");
NetDeviceContainer devices = wifi.Install (wifiPhy, wifiMac, c);
// Configure mobility
MobilityHelper mobility;
Ptr<ListPositionAllocator> positionAlloc = CreateObject<ListPositionAllocator> ();
positionAlloc->Add (Vector (0.0, 0.0, 0.0));
positionAlloc->Add (Vector (5.0, 0.0, 0.0));
mobility.SetPositionAllocator (positionAlloc);
mobility.SetMobilityModel ("ns3::ConstantPositionMobilityModel");
mobility.Install (c);
// other set up (e.g. InternetStack, Application)
Infrastructure (access point and clients) WifiNetDevice configuration
This is a typical example of how a user might configure an access point and a set of clients. In this
example, we create one access point and two clients. Each node is equipped with 802.11b Wi-Fi device:
std::string phyMode ("DsssRate1Mbps");
NodeContainer ap;
ap.Create (1);
NodeContainer sta;
sta.Create (2);
WifiHelper wifi;
wifi.SetStandard (WIFI_STANDARD_80211b);
YansWifiPhyHelper wifiPhy = YansWifiPhyHelper::Default ();
// ns-3 supports RadioTap and Prism tracing extensions for 802.11
wifiPhy.SetPcapDataLinkType (WifiPhyHelper::DLT_IEEE802_11_RADIO);
YansWifiChannelHelper wifiChannel;
// reference loss must be changed since 802.11b is operating at 2.4GHz
wifiChannel.SetPropagationDelay ("ns3::ConstantSpeedPropagationDelayModel");
wifiChannel.AddPropagationLoss ("ns3::LogDistancePropagationLossModel",
"Exponent", DoubleValue (3.0),
"ReferenceLoss", DoubleValue (40.0459));
wifiPhy.SetChannel (wifiChannel.Create ());
// Add a non-QoS upper mac, and disable rate control
WifiMacHelper wifiMac;
wifi.SetRemoteStationManager ("ns3::ConstantRateWifiManager",
"DataMode",StringValue (phyMode),
"ControlMode",StringValue (phyMode));
// Setup the rest of the upper mac
Ssid ssid = Ssid ("wifi-default");
// setup ap.
wifiMac.SetType ("ns3::ApWifiMac",
"Ssid", SsidValue (ssid));
NetDeviceContainer apDevice = wifi.Install (wifiPhy, wifiMac, ap);
NetDeviceContainer devices = apDevice;
// setup sta.
wifiMac.SetType ("ns3::StaWifiMac",
"Ssid", SsidValue (ssid),
"ActiveProbing", BooleanValue (false));
NetDeviceContainer staDevice = wifi.Install (wifiPhy, wifiMac, sta);
devices.Add (staDevice);
// Configure mobility
MobilityHelper mobility;
Ptr<ListPositionAllocator> positionAlloc = CreateObject<ListPositionAllocator> ();
positionAlloc->Add (Vector (0.0, 0.0, 0.0));
positionAlloc->Add (Vector (5.0, 0.0, 0.0));
positionAlloc->Add (Vector (0.0, 5.0, 0.0));
mobility.SetPositionAllocator (positionAlloc);
mobility.SetMobilityModel ("ns3::ConstantPositionMobilityModel");
mobility.Install (ap);
mobility.Install (sta);
// other set up (e.g. InternetStack, Application)
Testing Documentation
At present, most of the available documentation about testing and validation exists in publications, some
of which are referenced below.
Error model
Validation results for the 802.11b error model are available in this technical report
Two clarifications on the results should be noted. First, Figure 1-4 of the above reference corresponds
to the ns-3 NIST BER model. In the program in the Appendix of the paper (80211b.c), there are two
constants used to generate the data. The first, packet size, is set to 1024 bytes. The second, “noise”,
is set to a value of 7 dB; this was empirically picked to align the curves the best with the reported
data from the CMU testbed. Although a value of 1.55 dB would correspond to the reported -99 dBm noise
floor from the CMU paper, a noise figure of 7 dB results in the best fit with the CMU experimental data.
This default of 7 dB is the RxNoiseFigure in the ns3::YansWifiPhy model. Other values for noise figure
will shift the curves leftward or rightward but not change the slope.
The curves can be reproduced by running the wifi-clear-channel-cmu.cc example program in the
examples/wireless directory, and the figure produced (when GNU Scientific Library (GSL) is enabled) is
reproduced below in Figure Clear channel (AWGN) error model for 802.11b.
[image] Clear channel (AWGN) error model for 802.11b.UNINDENT
Validation results for the 802.11a/g OFDM error model are available in this technical report. The
curves can be reproduced by running the wifi-ofdm-validation.cc example program in the
examples/wireless directory, and the figure is reproduced below in Figure Frame error rate (NIST model)
for 802.11a/g (OFDM) Wi-Fi.
[image] Frame error rate (NIST model) for 802.11a/g (OFDM) Wi-Fi.UNINDENT
Similar curves for 802.11n/ac/ax can be obtained by running the wifi-ofdm-ht-validation.cc,
wifi-ofdm-vht-validation.cc and wifi-ofdm-he-validation.cc example programs in the examples/wireless
directory, and the figures are reproduced below in Figure Frame error rate (NIST model) for 802.11n (HT
OFDM) Wi-Fi, Figure Frame error rate (NIST model) for 802.11ac (VHT OFDM) Wi-Fi and Figure Frame error
rate (NIST model) for 802.11ax (HE OFDM) Wi-Fi, respectively. There is no validation for those curves
yet.
[image] Frame error rate (NIST model) for 802.11n (HT OFDM) Wi-Fi.UNINDENT
[image] Frame error rate (NIST model) for 802.11ac (VHT OFDM) Wi-Fi.UNINDENT
[image] Frame error rate (NIST model) for 802.11ax (HE OFDM) Wi-Fi.UNINDENT
MAC validation
Validation of the 802.11 DCF MAC layer has been performed in [baldo2010].
802.11 PCF operation has been verified by running ‘wifi-pcf’ example with PCAP files generation enabled,
and observing the frame exchange using Wireshark.
SpectrumWiFiPhy
The SpectrumWifiPhy implementation has been verified to produce equivalent results to the legacy
YansWifiPhy by using the saturation and packet error rate programs (described below) and toggling the
implementation between the two physical layers.
A basic unit test is provided using injection of hand-crafted packets to a receiving Phy object,
controlling the timing and receive power of each packet arrival and checking the reception results.
However, most of the testing of this Phy implementation has been performed using example programs
described below, and during the course of a (separate) LTE/Wi-Fi coexistence study not documented herein.
Saturation performance
The program examples/wireless/wifi-spectrum-saturation-example.cc allows user to select either the
SpectrumWifiPhy or YansWifiPhy for saturation tests. The wifiType can be toggled by the argument
'--wifiType=ns3::YansWifiPhy' or --wifiType=ns3::SpectrumWifiPhy'
There isn’t any difference in the output, which is to be expected because this test is more of a test of
the DCF than the physical layer.
By default, the program will use the SpectrumWifiPhy and will run for 10 seconds of saturating UDP data,
with 802.11n features enabled. It produces this output for the main 802.11n rates (with short and long
guard intervals):
wifiType: ns3::SpectrumWifiPhy distance: 1m
index MCS width Rate (Mb/s) Tput (Mb/s) Received
0 0 20 6.5 5.81381 4937
1 1 20 13 11.8266 10043
2 2 20 19.5 17.7935 15110
3 3 20 26 23.7958 20207
4 4 20 39 35.7331 30344
5 5 20 52 47.6174 40436
6 6 20 58.5 53.6102 45525
7 7 20 65 59.5501 50569
...
63 15 40 300 254.902 216459
The above output shows the first 8 (of 32) modes, and last mode, that will be output from the program.
The first 8 modes correspond to short guard interval disabled and channel bonding disabled. The
subsequent 24 modes run by this program are variations with short guard interval enabled (cases 9-16),
and then with channel bonding enabled and short guard first disabled then enabled (cases 17-32). Cases
33-64 repeat the same configurations but for two spatial streams (MIMO abstraction).
When run with the legacy YansWifiPhy, as in ./waf --run "wifi-spectrum-saturation-example
--wifiType=ns3::YansWifiPhy", the same output is observed:
wifiType: ns3::YansWifiPhy distance: 1m
index MCS width Rate (Mb/s) Tput (Mb/s) Received
0 0 20 6.5 5.81381 4937
1 1 20 13 11.8266 10043
2 2 20 19.5 17.7935 15110
3 3 20 26 23.7958 20207
...
This is to be expected since YansWifiPhy and SpectrumWifiPhy use the same error rate model in this case.
Packet error rate performance
The program examples/wireless/wifi-spectrum-per-example.cc allows users to select either SpectrumWifiPhy
or YansWifiPhy, as above, and select the distance between the nodes, and to log the reception statistics
and received SNR (as observed by the WifiPhy::MonitorSnifferRx trace source), using a Friis propagation
loss model. The transmit power is lowered from the default of 40 mW (16 dBm) to 1 dBm to lower the
baseline SNR; the distance between the nodes can be changed to further change the SNR. By default, it
steps through the same index values as in the saturation example (0 through 31) for a 50m distance, for
10 seconds of simulation time, producing output such as:
wifiType: ns3::SpectrumWifiPhy distance: 50m; time: 10; TxPower: 1 dBm (1.3 mW)
index MCS Rate (Mb/s) Tput (Mb/s) Received Signal (dBm) Noise (dBm) SNR (dB)
0 0 6.50 5.77 7414 -79.71 -93.97 14.25
1 1 13.00 11.58 14892 -79.71 -93.97 14.25
2 2 19.50 17.39 22358 -79.71 -93.97 14.25
3 3 26.00 22.96 29521 -79.71 -93.97 14.25
4 4 39.00 0.00 0 N/A N/A N/A
5 5 52.00 0.00 0 N/A N/A N/A
6 6 58.50 0.00 0 N/A N/A N/A
7 7 65.00 0.00 0 N/A N/A N/A
As in the above saturation example, running this program with YansWifiPhy will yield identical output.
Interference performance
The program examples/wireless/wifi-spectrum-per-interference.cc is based on the previous packet error
rate example, but copies over the WaveformGenerator from the unlicensed LTE interferer test, to allow
users to inject a non-Wi-Fi signal (using the --waveformPower argument) from the command line. Another
difference with respect to the packet error rate example program is that the transmit power is set back
to the default of 40 mW (16 dBm). By default, the interference generator is off, and the program should
behave similarly to the other packet error rate example, but by adding small amounts of power (e.g.
--waveformPower=0.001), one will start to observe SNR degradation and frame loss.
Some sample output with default arguments (no interference) is:
./waf --run "wifi-spectrum-per-interference"
wifiType: ns3::SpectrumWifiPhy distance: 50m; time: 10; TxPower: 16 dBm (40 mW)
index MCS Rate (Mb/s) Tput (Mb/s) Received Signal (dBm)Noi+Inf(dBm) SNR (dB)
0 0 6.50 5.77 7414 -64.69 -93.97 29.27
1 1 13.00 11.58 14892 -64.69 -93.97 29.27
2 2 19.50 17.39 22358 -64.69 -93.97 29.27
3 3 26.00 23.23 29875 -64.69 -93.97 29.27
4 4 39.00 34.90 44877 -64.69 -93.97 29.27
5 5 52.00 46.51 59813 -64.69 -93.97 29.27
6 6 58.50 52.39 67374 -64.69 -93.97 29.27
7 7 65.00 58.18 74819 -64.69 -93.97 29.27
...
while a small amount of waveform power will cause frame losses to occur at higher order modulations, due
to lower SNR:
./waf --run "wifi-spectrum-per-interference --waveformPower=0.001"
wifiType: ns3::SpectrumWifiPhy distance: 50m; sent: 1000 TxPower: 16 dBm (40 mW)
index MCS Rate (Mb/s) Tput (Mb/s) Received Signal (dBm)Noi+Inf(dBm) SNR (dB)
0 0 6.50 5.77 7414 -64.69 -80.08 15.38
1 1 13.00 11.58 14892 -64.69 -80.08 15.38
2 2 19.50 17.39 22358 -64.69 -80.08 15.38
3 3 26.00 23.23 29873 -64.69 -80.08 15.38
4 4 39.00 0.41 531 -64.69 -80.08 15.38
5 5 52.00 0.00 0 N/A N/A N/A
6 6 58.50 0.00 0 N/A N/A N/A
7 7 65.00 0.00 0 N/A N/A N/A
...
If ns3::YansWifiPhy is selected as the wifiType, the waveform generator will not be enabled because only
transmitters of type YansWifiPhy may be connected to a YansWifiChannel.
The interference signal as received by the sending node is typically below the default -62 dBm CCA Mode 1
threshold in this example. If it raises above, the sending node will suppress all transmissions.
Bianchi validation
The program src/wifi/examples/wifi-bianchi.cc allows user to compare ns-3 simulation results against the
Bianchi model presented in [bianchi2000] and [bianchi2005].
The MATLAB code used to generate the Bianchi model, as well as the generated outputs, are provided in the
folder src/wifi/examples/reference. User can regenerate Bianchi results by running generate_bianchi.m in
MATLAB.
By default, the program src/wifi/examples/wifi-bianchi.cc simulates an 802.11a adhoc ring scenario, with
a PHY rate set to 54 Mbit/s, and loop from 5 stations to 50 stations, by a step of 5 stations. It
generates a plt file, which allows user to quickly generate an eps file using gnuplot and vizualize the
graph.
./waf --run "wifi-bianchi"
[image] Bianchi throughput validation results for 802.11a 54 Mbps in adhoc configuration.UNINDENT
The user has the possibility to select the standard (only 11a, 11b or 11g currently supported), to
select the PHY rate (in Mbit/s), as well as to choose between an adhoc or an infrastructure
configuration.
When run for 802.11g 6 Mbit/s in infrastucture mode, the output is:
./waf --run "wifi-bianchi --standard=11g --phyRate=6 --duration=500 --infra"
[image] Bianchi throughput validation results for 802.11g 6 Mbps in infrastructure
configuration.UNINDENT
Multi-user transmissions validation
The implementation of the OFDMA support has been validated against a theoretical model [magrin2021mu] .
A preliminary evaluation of the usage of OFDMA in 802.11ax, in terms of latency in non-saturated
conditions, throughput in saturated conditions and transmission range with UL OFDMA, is provided in
[avallone2021wcm] .
References
[ieee80211]
IEEE Std 802.11-2012, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY)
Specifications
[ieee80211-2016]
IEEE Std 802.11-2016, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY)
Specifications
[pei80211b]
G. Pei and Tom Henderson, Validation of ns-3 802.11b PHY model
[pei80211ofdm]
G. Pei and Tom Henderson, Validation of OFDM error rate model in ns-3
[lacage2006yans]
M. Lacage and T. Henderson, Yet another Network Simulator
[Haccoun]
D. Haccoun and G. Begin, High-Rate Punctured Convolutional Codes for Viterbi Sequential Decoding,
IEEE Transactions on Communications, Vol. 32, Issue 3, pp.315-319.
[Frenger]
Pâl Frenger et al., “Multi-rate Convolutional Codes”.
[ji2004sslswn]
Z. Ji, J. Zhou, M. Takai and R. Bagrodia, Scalable simulation of large-scale wireless networks with
bounded inaccuracies, in Proc. of the Seventh ACM Symposium on Modeling, Analysis and Simulation of
Wireless and Mobile Systems, October 2004.
[linuxminstrel]
minstrel linux wireless
[lacage2004aarfamrr]
M. Lacage, H. Manshaei, and T. Turletti, IEEE 802.11 rate adaptation: a practical approach, in Proc.
7th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems,
2004.
[kim2006cara]
J. Kim, S. Kim, S. Choi, and D. Qiao, CARA: Collision-Aware Rate Adaptation for IEEE 802.11 WLANs,
in Proc. 25th IEEE International Conference on Computer Communications, 2006
[wong2006rraa]
S. Wong, H. Yang, S. Lu, and V. Bharghavan, Robust Rate Adaptation for 802.11 Wireless Networks, in
Proc. 12th Annual International Conference on Mobile Computing and Networking, 2006
[maguolo2008aarfcd]
F. Maguolo, M. Lacage, and T. Turletti, Efficient collision detection for auto rate fallback
algorithm, in IEEE Symposium on Computers and Communications, 2008
[proakis2001]
J. Proakis, Digital Communications, Wiley, 2001.
[miller2003]
L. E. Miller, “Validation of 802.11a/UWB Coexistence Simulation.” Technical Report, October 2003.
Available online
[ferrari2004]
G. Ferrari and G. Corazza, “Tight bounds and accurate approximations for DQPSK transmission bit
error rate”, Electronics Letters, 40(20):1284-85, September 2004.
[pursley2009]
M. Pursley and T. Royster, “Properties and performance of the IEEE 802.11b complementary code key
signal sets,” IEEE Transactions on Communications, 57(2);440-449, February 2009.
[akella2007parf]
A. Akella, G. Judd, S. Seshan, and P. Steenkiste, ‘Self-management in chaotic wireless deployments’,
in Wireless Networks, Kluwer Academic Publishers, 2007, 13, 737-755.
http://www.cs.odu.edu/~nadeem/classes/cs795-WNS-S13/papers/enter-006.pdf
[chevillat2005aparf]
Chevillat, P.; Jelitto, J., and Truong, H. L., ‘Dynamic data rate and transmit power adjustment in
IEEE 802.11 wireless LANs’, in International Journal of Wireless Information Networks, Springer,
2005, 12, 123-145.
http://www.cs.mun.ca/~yzchen/papers/papers/rate_adaptation/80211_dynamic_rate_power_adjustment_chevillat_j2005.pdf
[hepner2015]
C. Hepner, A. Witt, and R. Muenzner, “In depth analysis of the ns-3 physical layer abstraction for
WLAN systems and evaluation of its influences on network simulation results”, BW-CAR Symposium on
Information and Communication Systems (SInCom) 2015.
https://core.ac.uk/download/pdf/75487102.pdf#page=50
[baldo2010]
N. Baldo et al., “Validation of the ns-3 IEEE 802.11 model using the EXTREME testbed”, Proceedings
of SIMUTools Conference, March 2010.
[lanante2019]
L. Lanante Jr. et al., “Improved Abstraction for Clear Channel Assessment in ns-3 802.11 WLAN
Model”, Proceedings of the 2019 Workshop on ns-3, June 2019.
[bianchi2000]
G. Bianchi, “Performance analysis of the IEEE 802.11 distributed coordination function”, IEEE
Communications Letters, 18(3):535–547, 2000.
[bianchi2005]
G. Bianchi and I. Tinnirello. “Remarks on IEEE 802.11 DCF performance analysis”, IEEE Communications
Letters, 9(8):765–767, 2005.
[patidar2017]
R. Patidar et al., “Link-to-System Mapping for ns-3 Wi-Fi OFDM Error Models”, Proceedings of the
Workshop on ns-3, June 2017. https://dl.acm.org/doi/10.1145/3067665.3067671
[erceg2004]
V. Erceg and L. Schumacher and P. Kyritsi, “Tgn channel models”, IEEE 802.11-03/940r4, 2004.
[porat2016]
R. Porat et al., “11ax Evaluation Methodology”, IEE P802.11 Wireless LANs, 11-14-0571r3, 2016.
[krotov2020rate]
A. Krotov, A. Kiryanov, E. Khorov., Rate Control With Spatial Reuse for Wi-Fi 6 Dense Deployments,
IEEE Access, September 2020
[magrin2021mu]
D. Magrin, S. Avallone, S. Roy, and M. Zorzi, ‘Validation of the ns-3 802.11ax OFDMA
implementation’, in Proceedings of WNS3 2021.
[avallone2021wcm]
S. Avallone, P. Imputato, G. Redieteab, C. Ghosh and S. Roy, “Will OFDMA Improve the Performance of
802.11 WiFi Networks?”, in IEEE Wireless Communications Magazine, DOI: 10.1109/MWC.001.2000332, to
appear.
WIMAX NETDEVICE
This chapter describes the ns-3 WimaxNetDevice and related models. By adding WimaxNetDevice objects to
ns-3 nodes, one can create models of 802.16-based networks. Below, we list some more details about what
the ns-3 WiMAX models cover but, in summary, the most important features of the ns-3 model are:
• a scalable and realistic physical layer and channel model
• a packet classifier for the IP convergence sublayer
• efficient uplink and downlink schedulers
• support for Multicast and Broadcast Service (MBS), and
• packet tracing functionality
The source code for the WiMAX models lives in the directory src/wimax.
There have been two academic papers published on this model:
• M.A. Ismail, G. Piro, L.A. Grieco, and T. Turletti, “An Improved IEEE 802.16 WiMAX Module for the NS-3
Simulator”, SIMUTools 2010 Conference, March 2010.
• J. Farooq and T. Turletti, “An IEEE 802.16 WiMAX module for the NS-3 Simulator,” SIMUTools 2009
Conference, March 2009.
Scope of the model
From a MAC perspective, there are two basic modes of operation, that of a Subscriber Station (SS) or a
Base Station (BS). These are implemented as two subclasses of the base class ns3::NetDevice, class
SubscriberStationNetDevice and class BaseStationNetDevice. As is typical in ns-3, there is also a
physical layer class WimaxPhy and a channel class WimaxChannel which serves to hold the references to all
of the attached Phy devices. The main physical layer class is the SimpleOfdmWimaxChannel class.
Another important aspect of WiMAX is the uplink and downlink scheduler, and there are three primary
scheduler types implemented:
• SIMPLE: a simple priority based FCFS scheduler
• RTPS: a real-time polling service (rtPS) scheduler
• MBQOS: a migration-based uplink scheduler
The following additional aspects of the 802.16 specifications, as well as physical layer and channel
models, are modelled:
• leverages existing ns-3 wireless propagation loss and delay models, as well as ns-3 mobility models
• Point-to-Multipoint (PMP) mode and the WirelessMAN-OFDM PHY layer
• Initial Ranging
• Service Flow Initialization
• Management Connection
• Transport Initialization
• UGS, rtPS, nrtPS, and BE connections
The following aspects are not presently modelled but would be good topics for future extensions:
• OFDMA PHY layer
• Link adaptation
• Mesh topologies
• ARQ
• ertPS connection
• packet header suppression
Using the Wimax models
The main way that users who write simulation scripts will typically interact with the Wimax models is
through the helper API and through the publicly visible attributes of the model.
The helper API is defined in src/wimax/helper/wimax-helper.{cc,h}.
The example src/wimax/examples/wimax-simple.cc contains some basic code that shows how to set up the
model:
switch (schedType)
{
case 0:
scheduler = WimaxHelper::SCHED_TYPE_SIMPLE;
break;
case 1:
scheduler = WimaxHelper::SCHED_TYPE_MBQOS;
break;
case 2:
scheduler = WimaxHelper::SCHED_TYPE_RTPS;
break;
default:
scheduler = WimaxHelper::SCHED_TYPE_SIMPLE;
}
NodeContainer ssNodes;
NodeContainer bsNodes;
ssNodes.Create (2);
bsNodes.Create (1);
WimaxHelper wimax;
NetDeviceContainer ssDevs, bsDevs;
ssDevs = wimax.Install (ssNodes,
WimaxHelper::DEVICE_TYPE_SUBSCRIBER_STATION,
WimaxHelper::SIMPLE_PHY_TYPE_OFDM,
scheduler);
bsDevs = wimax.Install (bsNodes, WimaxHelper::DEVICE_TYPE_BASE_STATION, WimaxHelper::SIMPLE_PHY_TYPE_OFDM, scheduler);
This example shows that there are two subscriber stations and one base station created. The helper method
Install allows the user to specify the scheduler type, the physical layer type, and the device type.
Different variants of Install are available; for instance, the example
src/wimax/examples/wimax-multicast.cc shows how to specify a non-default channel or propagation model:
channel = CreateObject<SimpleOfdmWimaxChannel> ();
channel->SetPropagationModel (SimpleOfdmWimaxChannel::COST231_PROPAGATION);
ssDevs = wimax.Install (ssNodes,
WimaxHelper::DEVICE_TYPE_SUBSCRIBER_STATION,
WimaxHelper::SIMPLE_PHY_TYPE_OFDM,
channel,
scheduler);
Ptr<WimaxNetDevice> dev = wimax.Install (bsNodes.Get (0),
WimaxHelper::DEVICE_TYPE_BASE_STATION,
WimaxHelper::SIMPLE_PHY_TYPE_OFDM,
channel,
scheduler);
Mobility is also supported in the same way as in Wifi models; see the
src/wimax/examples/wimax-multicast.cc.
Another important concept in WiMAX is that of a service flow. This is a unidirectional flow of packets
with a set of QoS parameters such as traffic priority, rate, scheduling type, etc. The base station is
responsible for issuing service flow identifiers and mapping them to WiMAX connections. The following
code from src/wimax/examples/wimax-multicast.cc shows how this is configured from a helper level:
ServiceFlow MulticastServiceFlow = wimax.CreateServiceFlow (ServiceFlow::SF_DIRECTION_DOWN,
ServiceFlow::SF_TYPE_UGS,
MulticastClassifier);
bs->GetServiceFlowManager ()->AddMulticastServiceFlow (MulticastServiceFlow, WimaxPhy::MODULATION_TYPE_QPSK_12);
Wimax Attributes
The WimaxNetDevice makes heavy use of the ns-3 attributes subsystem for configuration and default value
management. Presently, approximately 60 values are stored in this system.
For instance, class ns-3::SimpleOfdmWimaxPhy exports these attributes:
• NoiseFigure: Loss (dB) in the Signal-to-Noise-Ratio due to non-idealities in the receiver.
• TxPower: Transmission power (dB)
• G: The ratio of CP time to useful time
• txGain: Transmission gain (dB)
• RxGain: Reception gain (dB)
• Nfft: FFT size
• TraceFilePath: Path to the directory containing SNR to block error rate files
For a full list of attributes in these models, consult the Doxygen page that lists all attributes for
ns-3.
Wimax Tracing
ns-3 has a sophisticated tracing infrastructure that allows users to hook into existing trace sources, or
to define and export new ones.
Many ns-3 users use the built-in Pcap or Ascii tracing, and the WimaxHelper has similar APIs:
AsciiTraceHelper ascii;
WimaxHelper wimax;
wimax.EnablePcap ("wimax-program", false);
wimax.EnableAsciiAll (ascii.CreateFileStream ("wimax-program.tr");
Unlike other helpers, there is also a special EnableAsciiForConnection() method that limits the ascii
tracing to a specific device and connection.
These helpers access the low level trace sources that exist in the WiMAX physical layer, net device, and
queue models. Like other ns-3 trace sources, users may hook their own functions to these trace sources if
they want to do customized things based on the packet events. See the Doxygen List of trace sources for a
complete list of these sources.
Wimax MAC model
The 802.16 model provided in ns-3 attempts to provide an accurate MAC and PHY level implementation of the
802.16 specification with the Point-to-Multipoint (PMP) mode and the WirelessMAN-OFDM PHY layer. The
model is mainly composed of three layers:
• The convergence sublayer (CS)
• The MAC CP Common Part Sublayer (MAC-CPS)
• Physical (PHY) layer
The following figure WiMAX architecture shows the relationships of these models.
[image] WiMAX architecture.UNINDENT
Convergence Sublayer
The Convergence sublayer (CS) provided with this module implements the Packet CS, designed to work with
the packet-based protocols at higher layers. The CS is responsible of receiving packet from the higher
layer and from peer stations, classifying packets to appropriate connections (or service flows) and
processing packets. It keeps a mapping of transport connections to service flows. This enables the MAC
CPS identifying the Quality of Service (QoS) parameters associated to a transport connection and ensuring
the QoS requirements. The CS currently employs an IP classifier.
IP Packet Classifier
An IP packet classifier is used to map incoming packets to appropriate connections based on a set of
criteria. The classifier maintains a list of mapping rules which associate an IP flow (src IP address and
mask, dst IP address and mask, src port range, dst port range and protocol) to one of the service flows.
By analyzing the IP and the TCP/UDP headers the classifier will append the incoming packet (from the
upper layer) to the queue of the appropriate WiMAX connection. Class IpcsClassifier and class
IpcsClassifierRecord implement the classifier module for both SS and BS
MAC Common Part Sublayer
The MAC Common Part Sublayer (CPS) is the main sublayer of the IEEE 802.16 MAC and performs the
fundamental functions of the MAC. The module implements the Point-Multi-Point (PMP) mode. In PMP mode BS
is responsible of managing communication among multiple SSs. The key functionalities of the MAC CPS
include framing and addressing, generation of MAC management messages, SS initialization and
registration, service flow management, bandwidth management and scheduling services. Class
WimaxNetDevice represents the MAC layer of a WiMAX network device. This class extends the class NetDevice
of the ns-3 API that provides abstraction of a network device. Class WimaxNetDevice is further extended
by class BaseStationNetDevice and class SubscriberStationNetDevice, defining MAC layers of BS and SS,
respectively. Besides these main classes, the key functions of MAC are distributed to several other
classes.
Framing and Management Messages
The module implements a frame as a fixed duration of time, i.e., frame boundaries are defined with
respect to time. Each frame is further subdivided into downlink (DL) and uplink (UL) subframes. The
module implements the Time Division Duplex (TDD) mode where DL and UL operate on same frequency but are
separated in time. A number of DL and UL bursts are then allocated in DL and UL subframes, respectively.
Since the standard allows sending and receiving bursts of packets in a given DL or UL burst, the unit of
transmission at the MAC layer is a packet burst. The module implements a special PacketBurst data
structure for this purpose. A packet burst is essentially a list of packets. The BS downlink and uplink
schedulers, implemented by class BSScheduler and class UplinkScheduler, are responsible of generating DL
and UL subframes, respectively. In the case of DL, the subframe is simulated by transmitting consecutive
bursts (instances PacketBurst). In case of UL, the subframe is divided, with respect to time, into a
number of slots. The bursts transmitted by the SSs in these slots are then aligned to slot boundaries.
The frame is divided into integer number of symbols and Physical Slots (PS) which helps in managing
bandwidth more effectively. The number of symbols per frame depends on the underlying implementation of
the PHY layer. The size of a DL or UL burst is specified in units of symbols.
Network Entry and Initialization
The network entry and initialization phase is basically divided into two sub-phases, (1) scanning and
synchronization and (2) initial ranging. The entire phase is performed by the LinkManager component of SS
and BS. Once an SS wants to join the network, it first scans the downlink frequencies to search for a
suitable channel. The search is complete as soon as it detects a PHY frame. The next step is to establish
synchronization with the BS. Once SS receives a Downlink-MAP (DL-MAP) message the synchronization phase
is complete and it remains synchronized as long as it keeps receiving DL-MAP and Downlink Channel
Descriptor (DCD) messages. After the synchronization is established, SS waits for a Uplink Channel
Descriptor (UCD) message to acquire uplink channel parameters. Once acquired, the first sub-phase of the
network entry and initialization is complete. Once synchronization is achieved, the SS waits for a UL-MAP
message to locate a special grant, called initial ranging interval, in the UL subframe. This grant is
allocated by the BS Uplink Scheduler at regular intervals. Currently this interval is set to 0.5 ms,
however the user is enabled to modify its value from the simulation script.
Connections and Addressing
All communication at the MAC layer is carried in terms of connections. The standard defines a connection
as a unidirectional mapping between the SS and BS’s MAC entities for the transmission of traffic. The
standard defines two types of connections: management connections for transmitting control messages and
transport connections for data transmission. A connection is identified by a 16-bit Connection Identifier
(CID). Class WimaxConnection and class Cid implement the connection and CID, respectively. Note that
each connection maintains its own transmission queue where packets to transmit on that connection are
queued. The ConnectionManager component of BS is responsible of creating and managing connections for all
SSs.
The two key management connections defined by the standard, namely the Basic and Primary management
connections, are created and allocated to the SS during the ranging process. Basic connection plays an
important role throughout the operation of SS also because all (unicast) DL and UL grants are directed
towards SS’s Basic CID. In addition to management connections, an SS may have one or more transport
connections to send data packets. The Connection Manager component of SS manages the connections
associated to SS. As defined by the standard, a management connection is bidirectional, i.e., a pair of
downlink and uplink connections is represented by the same CID. This feature is implemented in a way that
one connection (in DL direction) is created by the BS and upon receiving the CID the SS then creates an
identical connection (in UL direction) with the same CID.
Scheduling Services
The module supports the four scheduling services defined by the 802.16-2004 standard:
• Unsolicited Grant Service (UGS)
• Real-Time Polling Services (rtPS)
• Non Real-Time Polling Services (nrtPS)
• Best Effort (BE)
These scheduling services behave differently with respect to how they request bandwidth as well as how
the it is granted. Each service flow is associated to exactly one scheduling service, and the QoS
parameter set associated to a service flow actually defines the scheduling service it belongs to. When a
service flow is created the UplinkScheduler calculates necessary parameters such as grant size and grant
interval based on QoS parameters associated to it.
WiMAX Uplink Scheduler Model
Uplink Scheduler at the BS decides which of the SSs will be assigned uplink allocations based on the QoS
parameters associated to a service flow (or scheduling service) and bandwidth requests from the SSs.
Uplink scheduler together with Bandwidth Manager implements the complete scheduling service
functionality. The standard defines up to four scheduling services (BE, UGS, rtPS, nrtPS) for
applications with different types of QoS requirements. The service flows of these scheduling services
behave differently with respect to how they request for bandwidth as well as how the bandwidth is
granted. The module supports all four scheduling services. Each service flow is associated to exactly one
transport connection and one scheduling service. The QoS parameters associated to a service flow actually
define the scheduling service it belongs to. Standard QoS parameters for UGS, rtPS, nrtPS and BE
services, as specified in Tables 111a to 111d of the 802.16e amendment, are supported. When a service
flow is created the uplink scheduler calculates necessary parameters such as grant size and allocation
interval based on QoS parameters associated to it. The current WiMAX module provides three different
versions of schedulers.
• The first one is a simple priority-based First Come First Serve (FCFS). For the real-time services
(UGS and rtPS) the BS then allocates grants/polls on regular basis based on the calculated interval.
For the non real-time services (nrtPS and BE) only minimum reserved bandwidth is guaranteed if
available after servicing real-time flows. Note that not all of these parameters are utilized by the
uplink scheduler. Also note that currently only service flow with fixed-size packet size are supported,
as currently set up in simulation scenario with OnOff application of fixed packet size. This scheduler
is implemented by class BSSchedulerSimple and class UplinkSchedulerSimple.
• The second one is similar to first scheduler except by rtPS service flow. All rtPS Connections are able
to transmit all packet in the queue according to the available bandwidth. The bandwidth saturation
control has been implemented to redistribute the effective available bandwidth to all rtPS that have at
least one packet to transmit. The remaining bandwidth is allocated to nrtPS and BE Connections. This
scheduler is implemented by class BSSchedulerRtps and class UplinkSchedulerRtps.
• The third one is a Migration-based Quality of Service uplink scheduler This uplink scheduler uses three
queues, the low priority queue, the intermediate queue and the high priority queue. The scheduler
serves the requests in strict priority order from the high priority queue to the low priority queue.
The low priority queue stores the bandwidth requests of the BE service flow. The intermediate queue
holds bandwidth requests sent by rtPS and by nrtPS connections. rtPS and nrtPS requests can migrate to
the high priority queue to guarantee that their QoS requirements are met. Besides the requests migrated
from the intermediate queue, the high priority queue stores periodic grants and unicast request
opportunities that must be scheduled in the following frame. To guarantee the maximum delay
requirement, the BS assigns a deadline to each rtPS bandwidth request in the intermediate queue. The
minimum bandwidth requirement of both rtPS and nrtPS connections is guaranteed over a window of
duration T. This scheduler is implemented by class UplinkSchedulerMBQoS.
WiMAX Outbound Schedulers Model
Besides the uplink scheduler these are the outbound schedulers at BS and SS side (BSScheduler and
SSScheduler). The outbound schedulers decide which of the packets from the outbound queues will be
transmitted in a given allocation. The outbound scheduler at the BS schedules the downlink traffic, i.e.,
packets to be transmitted to the SSs in the downlink subframe. Similarly the outbound scheduler at a SS
schedules the packet to be transmitted in the uplink allocation assigned to that SS in the uplink
subframe. All three schedulers have been implemented to work as FCFS scheduler, as they allocate grants
starting from highest priority scheduling service to the lower priority one (UGS> rtPS> nrtPS> BE). The
standard does not suggest any scheduling algorithm and instead leaves this decision up to the
manufacturers. Of course more sophisticated algorithms can be added later if required.
WimaxChannel and WimaxPhy models
The module implements the Wireless MAN OFDM PHY specifications as the more relevant for implementation as
it is the schema chosen by the WiMAX Forum. This specification is designed for non-light-of-sight (NLOS)
including fixed and mobile broadband wireless access. The proposed model uses a 256 FFT processor, with
192 data subcarriers. It supports all the seven modulation and coding schemes specified by Wireless
MAN-OFDM. It is composed of two parts: the channel model and the physical model.
Channel model
The channel model we propose is implemented by the class SimpleOFDMWimaxChannel which extends the class
wimaxchannel. The channel entity has a private structure named m_phyList which handles all the physical
devices connected to it. When a physical device sends a packet (FEC Block) to the channel, the channel
handles the packet, and then for each physical device connected to it, it calculates the propagation
delay, the path loss according to a given propagation model and eventually forwards the packet to the
receiver device. The channel class uses the method GetDistanceFrom() to calculate the distance between
two physical entities according to their 3D coordinates. The delay is computed as delay = distance/C,
where C is the speed of the light.
Physical model
The physical layer performs two main operations: (i) It receives a burst from a channel and forwards it
to the MAC layer, (ii) it receives a burst from the MAC layer and transmits it on the channel. In order
to reduce the simulation complexity of the WiMAX physical layer, we have chosen to model offline part of
the physical layer. More specifically we have developed an OFDM simulator to generate trace files used by
the reception process to evaluate if a FEC block can be correctly decoded or not.
Transmission Process: A burst is a set of WiMAX MAC PDUs. At the sending process, a burst is converted
into bit-streams and then split into smaller FEC blocks which are then sent to the channel with a power
equal P_tx.
Reception Process: The reception process includes the following operations:
1. Receive a FEC block from the channel.
2. Calculate the noise level.
3. Estimate the signal to noise ratio (SNR) with the following formula.
4. Determine if a FEC block can be correctly decoded.
5. Concatenate received FEC blocks to reconstruct the original burst.
6. Forward the burst to the upper layer.
The developed process to evaluate if a FEC block can be correctly received or not uses pre-generated
traces. The trace files are generated by an external OFDM simulator (described later). A class named
SNRToBlockErrorRateManager handles a repository containing seven trace files (one for each modulation and
coding scheme). A repository is specific for a particular channel model.
A trace file is made of 6 columns. The first column provides the SNR value (1), whereas the other columns
give respectively the bit error rate BER (2), the block error rate BlcER(3), the standard deviation on
BlcER, and the confidence interval (4 and 5). These trace files are loaded into memory by the
SNRToBlockErrorRateManager entity at the beginning of the simulation.
Currently, The first process uses the first and third columns to determine if a FEC block is correctly
received. When the physical layer receives a packet with an SNR equal to SNR_rx, it asks the
SNRToBlockErrorRateManager to return the corresponding block error rate BlcER. A random number RAND
between 0 and 1 is then generated. If RAND is greater than BlcER, then the block is correctly received,
otherwise the block is considered erroneous and is ignored.
The module provides defaults SNR to block error rate traces in default-traces.h. The traces have been
generated by an External WiMAX OFDM simulator. The simulator is based on an external mathematics and
signal processing library IT++ and includes : a random block generator, a Reed Solomon (RS) coder, a
convolutional coder, an interleaver, a 256 FFT-based OFDM modulator, a multi-path channel simulator and
an equalizer. The multipath channel is simulated using the TDL_channel class of the IT++ library.
Users can configure the module to use their own traces generated by another OFDM simulator or ideally by
performing experiments in real environment. For this purpose, a path to a repository containing trace
files should be provided. If no repository is provided the traces form default-traces.h will be loaded.
A valid repository should contain 7 files, one for each modulation and coding scheme.
The names of the files should respect the following format: modulation0.txt for modulation 0,
modulation1.txt for modulation 1 and so on… The file format should be as follows:
SNR_value1 BER Blc_ER STANDARD_DEVIATION CONFIDENCE_INTERVAL1 CONFIDENCE_INTERVAL2
SNR_value2 BER Blc_ER STANDARD_DEVIATION CONFIDENCE_INTERVAL1 CONFIDENCE_INTERVAL2
... ... ... ... ... ...
... ... ... ... ... ...
AUTHOR
ns-3 project
COPYRIGHT
2006-2019