Provided by: ns3-doc_3.17+dfsg-1build1_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 in src/template/doc.
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/index.php/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
[image] An example of packet animation on wireless-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
and a way to visualize the trajectory of a mobile node
[image] An example of the trajectory of a mobile node.UNINDENT
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
Qt4 (4.7 and over) is required to build NetAnim. This can be obtained using the following ways:
For Debian/Ubuntu Linux distributions:
apt-get install qt4-dev-tools
For Red Hat/Fedora based distribution:
yum install qt4
yum install qt4-devel
For Mac/OSX:
http://qt.nokia.com/downloads/
Build steps
To build NetAnim use the following commands:
cd netanim
make clean
qmake NetAnim.pro (For MAC Users: qmake -spec macx-g++ NetAnim.pro)
make
Note: qmake could be "qmake-qt4" in some systems
This should create an executable named "NetAnim" in the same directory:
john@john-VirtualBox:~/netanim$ 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:
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.
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.
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
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
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 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
Essential settings of NetAnim
Persist combobox
[image] The persist combobox.UNINDENT
When packets are transmitted and received very quickly, they can be almost invisible. The persist time
setting allows the user to control the duration for which a packet should be visible on the animation
canvas.
Update-interval slider
[image] The update-interval slider.UNINDENT
The update-interval slider controls the rate at which NetAnim refreshes the canvas screen. For
instance, for the setting above, NetAnim, updates the position of nodes and packets only once in 250
ms.
Parts of the XML
The XML trace files has the following main sections
1. Topology
• Nodes
• Links
2. packets (packets over wired-links)
3. wpackets (packets over wireless-links)
XML tags
Nodes are identified by their unique Node id. The XML begins with the "information" element describing
the rest of the elements
1. <anim> element
This is the XML root element. All other elements fall within this element.
Attributes are:
lp = Logical Processor Id (Used for distributed simulations only)
2. <topology> element
This elements contains the Node and Link elements.It describes, the co-ordinates of the canvas used for
animation.
Attributes are:
minX = minimum X coordinate of the animation canvas
minY = minimum Y coordinate of the animation canvas
maxX = maximum X coordinate of the animation canvas
maxY = maximum Y coordinate of the animation canvas
Example:
<topology minX = "-6.42025" minY = "-6.48444" maxX = "186.187" maxY = "188.049">
3. <node> element
This element describes each Node's Id and X,Y co-ordinate (position).
Attributes are:
id = Node Id
locX = X coordinate
locY = Y coordinate
Example:
<node id = "8" locX = "107.599" locY = "96.9366" />
4. <link> element
This element describes wired links between two nodes.
Attributes are:
fromId = From Node Id (first node id)
toId = To Node Id (second node id)
Example:
<link fromId="0" toId="1"/>
5. <p> element
This element describes a packet over wired links being transmitted at some node and received at another.
The reception details are described in its associated rx element
Attributes are:
fId = Node Id transmitting the packet
fbTx = First bit transmit time of the packet
lbTx = Last bit transmit time of the packet
toId = Node Id receiving the packet
fbRx = First bit Reception Time of the packet
lbRx = Last bit Reception Time of the packet
Example:
<p fId="1" fbTx="1" lbTx="1.000067199" tId="0" fbRx="1.002" lbRx="1.002067199"/>
A packet over wired-links from Node 1 was received at Node 0. The first bit of the packet was transmitted
at the 1st second, the last bit was transmitted at the 1.000067199th second of the simulation Node 0
received the first bit of the packet at the 1.002th second and the last bit of the packet at the
1.002067199th second of the simulation NOTE: A packet with fromId == toId is a dummy packet used
internally by the AnimationInterface. Please ignore this packet
7. <wp> element
This element describes a packet over wireless links being transmitted at some node and received at
another.
The reception details are described in its associated rx element.
Attributes are:
fromId = Node Id transmitting the packet
fbTx = First bit transmit time of the packet
lbTx = Last bit transmit time of the packet
range = Range of the transmission
Example:
<wp fId = "20" fbTx = "0.003" lbTx = "0.003254" range = "59.68176982" tId="32" fbRx="0.003000198" lbRx="0.003254198"/>
A packet over wireless-links from Node 20 was received at Node 32. The first bit of the packet was
transmitted at the 0.003th second, the last bit was transmitted at the 0.003254 second of the simulation
Node 0 received the first bit of the packet at the 0.003000198 second and the last bit of the packet at
the 0.003254198 second of the simulation
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/index.php/NetAnim
ANTENNA MODULE
Design documentation
Overview
The Antenna module provides:
1. a new base class (AntennaModel) that provides an interface for the modeling of the radiation
pattern of an antenna;
2. a set of classes derived from this base class that each models the radiation pattern of different
types of antennas.
AntennaModel
The AntennaModel uses the coordinate system adopted in [Balanis] and depicted in Figure
fig-antenna-coordinate-system. This system is obtained by traslating 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, heta,hi).
The antenna model neglects the radial component r, and only considers the angle components (heta, hi).
An antenna radiation pattern is then expressed as a mathematical function g(heta, hi)
grightarrow thcal{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
Provided 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:
where hi_{0}
is the azimuthal orientation of the antenna (i.e., its direction of maximum gain) and the exponential
determines the desired 3dB beamwidth hi_{3dB}.
Note that this radiation pattern is independent of the inclination angle heta.
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:
where hi_{0}
is the azimuthal orientation of the antenna (i.e., its direction of maximum gain), hi_{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 heta.
[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.
User Documentation
The antenna moduled 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 (hi,
heta) determied 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 [rfc3561].
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. Expanding ring search.
2. Local link repair.
3. 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.
References
[rfc3561]
RFC 3561 Ad hoc On-Demand Distance Vector (AODV) Routing
Usage
Examples
Helpers
Attributes
Tracing
Logging
Caveats
Validation
Unit tests
Larger-scale performance tests
APPLICATIONS
Placeholder chapter
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 documenation 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 mobility model (BuildingsMobilityModel) 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.
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 BuildingsMobilityModel class
The BuildingsMobilityModel class, which inherits from the ns3 class MobilityModel, is in charge of
managing the standard mobility functionalities plus providing information about the position of a node
with respect to building. The information managed by BuildingsMobilityModel 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 BuildingsMobilityModel 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.).
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
expressioa ns given in the following.
r {
r a
a r
where: y r ight. : power loss coefficient [dB]
N = t }sadential \ 30 & office \ 22 & commercial\nd{array}
{ y ight.
L_f = t }sidential \ 15+4(n-1) & office \ 6+3(n-1) & commercial\nd{array}
l {
n : number of floors between base station and mobile (n 1)
} l
f : frequency [MHz]
8 }
d : di&stance (where d > 1) [m]
r n
BuildingsPropag&ationLossModel
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
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 connection characteristics. In the implementation we considered three main possible
scenarios which correspond to three standard deviations (i.e., the mean is always 0), in detail:
ightarrow X_thrm{O} N(_thrm{O},
• outdoor (m_shadowingSigmaOutdoor, defaul value of 7 dB)
ma_thrm{O}^2).
ightarrow X_thrm{I} N(_thrm{I},
• indoor (m_shadowingSigmaIndoor, defaul value of 10 dB)
ma_thrm{I}^2).
ightarrow X_thrm{W}
• external walls penetration (m_shadowingSigmaExtWalls, default value 5 dB)
N(_thrm{W}, ma_thrm{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 (ma_thrm{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.
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 heigth, 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
Main configurable parameters
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 methos 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 statistics distribution
characteristics of the shadowing are the one expected. The shadowing is modeled according to a normal
distribution with mean = 0 and variable standard deviation ma, according to models commonly used in
literature. The test generates 10,000 samples of shadowing by subtracting the deterministic component
from the total loss returned by the BuildingPathlossModel. The mean and variance of the shadowing samples
are then used to verify whether the 99% confidence interval is respected by the sequence generated by the
simulator.
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 implemntation 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 determinig 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 RFC4728.
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 Dijsktra 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 mulitiple 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 nodesin 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.
References
[1] Link for the original paper:
[2] Link for RFC 4728:
[3] Link for the Broch's comparison paper:
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 Tap 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 superseded by the FdNetDevice, and will be deprecated and removed
in future revisions of ns-3.
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.
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. 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 will be 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.
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.
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 changable 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 and energy source modeling.
Model Description
The source code for the Energy Framework is currently at: src/energy.
Design
The ns-3 Energy Framework is composed of 2 parts: Energy Source and Device Energy Model. The framework
will be 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 devices on
the same node to calculate energy consumption. The Energy Source polls all devices on the same node
periodically to calculate the total current draw 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.
The Energy Source base class keeps a list of devices (Device Energy Model objects) 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
when power supply is drained.
Device Energy Model
The Device Energy Model is the energy consumption model of a device on 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.
Future Work
For Device Energy Models, we are planning to include support for other PHY layer models provided in ns-3
such as WiMAX. For Energy Sources, we are planning to included new types of Energy Sources such as energy
scavenging.
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.
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 and
the corresponding Device Energy Model for the network devices. 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 also keeps a list of Device Energy Model objects using the source as power
supply. Device Energy Model objects are installed onto the Energy Source by the Device Energy Model
Helper. User can access the Device Energy Model objects through the Energy Source object to obtain energy
consumption information of individual devices.
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.
Attributes
Attributes differ between Energy Sources and Devices Energy Models 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.
Tracing
Traced values differ between Energy Sources and Devices Energy Models 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.
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
Placeholder chapter
This feature was added as contributed code (src/contrib) in ns-3.6 and to the main distribution
(src/flow-monitor) for ns-3.7. A paper on this feature is published in the proceedings of NSTools:
http://www.nstools.org/techprog.shtml.
INTERNET MODELS
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
IPv4-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 (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");
CreateAndAggregateObjectFromTypeId (node, "ns3::UdpL4Protocol");
node->AggregateObject (m_tcpFactory.Create<Object> ());
Ptr<PacketSocketFactory> factory = CreateObject<PacketSocketFactory> ();
node->AggregateObject (factory);
// Set routing
Ptr<Ipv4> ipv4 = node->GetObject<Ipv4> ();
Ptr<Ipv4RoutingProtocol> ipv4Routing = m_routing->Create (node);
ipv4->SetRoutingProtocol (ipv4Routing);
}
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 to Ipv4::
InternetStackHelper::InternetStackHelper ()
{
SetTcp ("ns3::TcpL4Protocol");
static Ipv4StaticRoutingHelper staticRouting;
static Ipv4GlobalRoutingHelper globalRouting;
static Ipv4ListRoutingHelper listRouting;
listRouting.Add (staticRouting, 0);
listRouting.Add (globalRouting, -10);
SetRoutingHelper (listRouting);
}
By default, IPv4 and IPv6 are enabled.
Internet Node structure
An IPv4-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, and
ARP. 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. 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.
Ipv4-capable node interfaces
Many of the implementation details, or internal objects themselves, of Ipv4-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
Placeholder chapter
IPv6
Placeholder chapter
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
fig-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 analagous 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.
routing-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.
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.
Unicast routing
There are presently seven unicast routing protocols defined for IPv4 and two for IPv6:
• class Ipv4StaticRouting (covering both unicast and multicast)
• 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)
• class Ipv4ListRouting (used to store a prioritized list of routing protocols)
• 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 Ipv6ListRouting (used to store a prioritized list of routing protocols)
• class Ipv6StaticRouting
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.
Ipv4ListRouting
This section describes the current default ns-3 Ipv4RoutingProtocol. 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 IPv4 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.
Ipv4ListRouting::AddRoutingProtocol
Class Ipv4ListRouting 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);
This method is implemented by class Ipv4ListRoutingImpl in the internet-stack 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 Ipv4ListRoutingImpl
object, and add to it an Ipv4StaticRoutingImpl object at priority zero. Internally, a list of
Ipv4RoutingProtocols 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.
Optimized Link State Routing (OLSR)
This IPv4 routing protocol was originally ported from the OLSR-UM implementation for ns-2. The
implementation is found in the src/olsr directory, and an example script is in
examples/simple-point-to-point-olsr.cc.
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.
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.
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 of the first kind are used during
forwarding. All of the conditions must be explicitly provided. The second kind of routes are used to get
packets off of a local node. The difference is in the input interface. Routes for forwarding will always
have an explicit input interface specified. Routes off of a node will always set the input interface to a
wildcard specified by the index Ipv4RoutingProtocol::IF_INDEX_ANY.
For routes off of a local node wildcards may be used in the origin and multicast group addresses. The
wildcard used for Ipv4Adresses is that address returned by Ipv4Address::GetAny () -- typically "0.0.0.0".
Usage of a wildcard allows one to specify default behavior to varying degrees.
For example, making the origin address a wildcard, but leaving the multicast group specific allows one
(in the case of a node with multiple interfaces) to create different routes using different output
interfaces for each multicast group.
If the origin and multicast addresses are made wildcards, you have created essentially a default
multicast address that can forward to multiple interfaces. Compare this to the actual default multicast
address that is limited to specifying a single output interface for compatibility with existing
functionality in other systems.
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.
Generic 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 two 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.
There are presently three implementations of TCP available for ns-3.
• a natively implemented TCP for ns-3
• support for the Network Simulation Cradle (NSC)
• support for Direct Code Exectution (DCE)
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
Until ns-3.10 release, ns-3 contained a port of the TCP model from GTNetS. This implementation was
substantially rewritten by Adriam Tam for ns-3.10. The model is a full TCP, in that it is bidirectional
and attempts to model the connection setup and close logic.
The implementation of TCP is contained in the following files::
src/internet/model/tcp-header.{cc,h}
src/internet/model/tcp-l4-protocol.{cc,h}
src/internet/model/tcp-socket-factory-impl.{cc,h}
src/internet/model/tcp-socket-base.{cc,h}
src/internet/model/tcp-tx-buffer.{cc,h}
src/internet/model/tcp-rx-buffer.{cc,h}
src/internet/model/tcp-rfc793.{cc,h}
src/internet/model/tcp-tahoe.{cc,h}
src/internet/model/tcp-reno.{cc,h}
src/internet/model/tcp-westwood.{cc,h}
src/internet/model/tcp-newreno.{cc,h}
src/internet/model/rtt-estimator.{cc,h}
src/network/model/sequence-number.{cc,h}
Different variants of TCP congestion control are supported by subclassing the common base class
TcpSocketBase. Several variants are supported, including RFC 793 (no congestion control), Tahoe, Reno,
Westwood, Westwood+, and NewReno. NewReno is used by default. See the Usage section of this document
for on how to change the default TCP variant used in simulation.
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 <http://www.nsnam.org/doxygen/classns3_1_1_tcp_socket.html> 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::TcpTahoe"));
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::TcpTahoe");
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 Tahoe, 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::TcpTahoe");
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). See Sockets-APIs for a
review of how sockets are used in ns-3.
Validation
Several TCP validation test results can be found in the wiki page describing this implementation.
Current limitations
• SACK is not supported
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 recently was added to ns-3. This section
describes the ns-3 port of NSC and how to use it.
Prerequisites
Presently, NSC has been tested and shown to work on these platforms: Linux i386 and Linux x86-64. NSC
does not support powerpc.
Building NSC requires the packages flex and bison.
Configuring and Downloading
Using the build.py script in ns-3-allinone directory, NSC will be enabled by default unless the platform
does not support it. To disable it when building ns-3, type::
./waf configure --enable-examples --enable-tests --disable-nsc
Building and validating
Building ns-3 with nsc support is the same as building it without; no additional arguments are needed for
waf. Building nsc may take some time compared to ns-3; it is interleaved in the ns-3 building process.
Try running the following ns-3 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::
~/ns-3.10> 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-comparision.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
nscTcpSocket. Data that is Send() will be handed to the nsc stack via m_nscTcpSocket->send_data(). (and
not to nsc-tcp-l4, this is the major difference compared to ns-3 TCP). The class also queues up data that
is Send() before the underlying descriptor has entered an ESTABLISHED state. This class is called from
the nsc-tcp-l4 class, when the nsc-tcp-l4 wakeup() callback is invoked by nsc. nsc-tcp-socket-impl 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
• 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.
LTE MODULE
Design Documentation
Overview
An overview of the LTE-EPC simulation model is depicted in the figure fig-epc-topology. 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 models 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 trasmitting. 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 Equipments (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 a single SGW/PGW node, as shown in Figure
fig-epc-topology.
The following design choices have been made for the EPC model:
1. The only Packet Data Network (PDN) type supported is IPv4.
2. The SGW and PGW functional entities are implemented within a single node, which is hence referred
to as the SGW/PGW node.
3. The scenarios with inter-SGW mobility are not of interests. Hence, a single SGW/PGW node will be
present in all simulations scenarios
4. 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.
5. 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/PGW should be modeled accurately.
6. 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.
7. The 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; hence, the necessary control plane
interactions can be modeled in a simplified way by leveraging on direct interaction among the
different simulation objects via the provided helper objects.
8. 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.
9. 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
fig-lte-arch-ue-data and fig-lte-arch-ue-ctrl 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 fig-lte-ue-phy.
[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
fig-lte-arch-enb-data and fig-lte-arch-enb-ctrl 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 fig-lte-enb-phy.
[image] PHY and channel model architecture for the eNB.UNINDENT
EPC Model
EPC data plane
In Figure fig-lte-epc-e2e-data-protocol-stack, we represent the end-to-end LTE-EPC data plane protocol
stack as it is modeled in the simulator. From the figure, it is evident that the biggest simplification
introduced in the data plane model is the inclusion of the SGW and PGW functionality within a single
SGW/PGW node, which removes the need for the S5 or S8 interfaces specified by 3GPP. On the other hand,
for both the S1-U protocol stack and the LTE radio protocol stack all the protocol layers 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 fig-epc-ctrl-arch.
The control interfaces that are modeled explicitly are the S1-AP, the X2-AP and the S11 interfaces.
We note that the S1-AP and the S11 interfaces are modeled in a simplified fashion, by using just one pair
of interface classes to model the interaction between entities that reside on different nodes (the eNB
and the MME for the S1-AP interface, and the MME and the SGW for the S11 interface). In practice, this
means that the primitives of these interfaces are mapped to a direct function call between the two
objects. On the other hand, the X2-AP interface is being modeled using protocol data units sent over an
X2 link (modeled as a point-to-point link); for this reason, the X2-AP interface model is more realistic.
[image] EPC control model.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 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 (/lte/model/JakesTraces/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:
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 intereference 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 fig-lte-subframe-structure.
[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).
In downlink, the CQI feedbacks are currently evaluated according to the SINR perceived by control channel
(i.e., PDCCH + PCFIC) in order to have an estimation of the interference when all the eNB are
transmitting simultaneously. 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 calld 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.
• ALL_UL_CQI for storing all the CQIs received.
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 attibutes (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 fig-lte-phy-interference 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 spectrum 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
fig-miesm-architecture, 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 guassian 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 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
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:
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 perfomance 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 SINR two instances of LtePemSinrChunkProcessor (child of
LteSinrChunkProcessor 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
respectively of PDSCH (UE side) and ULSCH (eNB side).
The model can be disabled for working with a zero-losses channel by setting the PemEnabled 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: = 13.5 and ma = 20 [dB].
• MIMO-Alamouti: = 17.7 and ma = 11.1 [dB].
• MIMO-MMSE: = 10.7 and ma = 16.6 [dB].
• MIMO-OSIC-MMSE: = 12.6 and ma = 15.5 [dB].
• MIMO-ZF: = 10.3 and ma = 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.
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:
where X is the number of original information bits, C_i are number of coded bits, M_i are the mutual
informations 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 calles 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 mma_i
be its SINR. We get the spectral efficiency \ta_i of user i using the following equations:
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
attibute, 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.
• ALL_UL_CQI: all CQIs are stored in the same internal attibute (i.e., the last CQI received is stored
independently from its nature).
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
where au is the TTI duration. At the start of each subframe t, each RBG is assigned to a certain user.
In detail, the index 144}_{k}(t) to which RBG k is assigned at time t is determined as
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:
where lpha is the time constant (in number of subframes) of the exponential moving average, and 384s the
actual throughput achieved by the user i in the subframe t. 360 is measured according to the following
procedure. First we determine the MCS 840 j:
then we determine the total number 936 j:
where |
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:
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 [TS36.213] 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
where au is the TTI duration. At the start of each subframe t, each RB is assigned to a certain user.
In detail, the index 144}_{k}(t) to which RB k is assigned at time t is determined as
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:
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:
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:
• set 2: UE whose past average throughput is larger (or equal) than TBR; TD scheduler calculates their
priority metric in Proportional Fair (PF) style:
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)
• Carrier over Interference to Average (CoIta)
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.
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
attibute, 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.
• ALL_UL_CQI: all CQIs are stored in the same internal attibute (i.e., the last CQI received is stored
independently from its nature).
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. 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 fig-mac-random-access-contention and fig-mac-random-access-noncontention 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), Unacknowledge Mode (UM) and Acknowledged Mode (AM). The simulator
includes one model for each of these entitities
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 fig-lte-rlc-implementation-model 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 nofify 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 just silently drop SDUs when the buffer is full.
• 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 fig-lte-rlc-data-txon-dl 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 fig-lte-rlc-data-retx-dl 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 fig-lte-rlc-data-txon-ul 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 fig-lte-rlc-data-retx-ul 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 expects to receive a big enough transmission opportunity. An assertion
fails if a too small transmission opportunity is received.
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 implemnetation of the Unacknowledge 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
sec-rlc-am-tx-operations, 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 sec-rlc-am-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 2)
• 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) + 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
fig-lte-arch-ue-data, fig-lte-arch-ue-ctrl, fig-lte-arch-enb-data and fig-lte-arch-enb-ctrl.
UE RRC State Machine
In Figure fig-lte-ue-rrc-states we represent the state machine as implemented in the RRC UE entity.
[image] UE RRC State Machine.UNINDENT
It is to be noted that most of the states are transient, i.e., once the UE goes into one of the
CONNECTED states it will never switch back to any of the IDLE states. This choice is done for the
following reasons:
• as discussed in the section sec-design-criteria, the focus of the LTE-EPC simulation model is on
CONNECTED mode
• radio link failure is not currently modeled, as discussed in the section sec-radio-link-failure, so
an UE cannot go IDLE because of radio link failure
• RRC connection release is currently never triggered neither by the EPC nor by the NAS
Still, we chose to model explicitly the IDLE states, because:
• a realistic UE RRC configuration is needed for handover, which is a required feature, and in order
to have a cleaner implementation it makes sense to use the same UE RRC configuration also for the
initial connection establishment
• it makes easier to implement idle mode cell selection in the future, which is a highly desirable
feature
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 fig-lte-enb-rrc-states.
[image] ENB RRC State Machine for each UE.UNINDENT
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
Since at this stage the RRC supports the CONNECTED mode only, Radio Link Failure (RLF) is not handled.
The reason is that one of the possible outcomes of RLF (when RRC re-establishment is unsuccessful) is to
leave RRC CONNECTED notifying the NAS of the RRC connection failure. In order to model RLF properly, RRC
IDLE mode should be supported, including in particular idle mode cell (re-)selection.
With the current model, an UE that experiences bad link quality will just stay associated with the same
eNB, and the scheduler will stop allocating resources to it for communications. This is also consistent
with the fact that, at this stage, only handovers explicitly triggered within the simulation program are
supported (network-driven handovers based on UE measurements are planned only at a later stage).
Handover
The RRC model support the execution of an X2-based handover procedure. The handover needs to be triggered
explicitly by the simulation program by scheduling an execution of the method
LteEnbRrc::SendHandoverRequest (). The automatic triggering of the handover based on UE measurements is
not supported at this stage.
RRC sequence diagrams
In this section we provide some sequence diagrams that explain the most important RRC procedures being
modeled.
RRC connection establishment
Figure fig-rrc-connection-establishment 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
RRC connection reconfiguration
Figure fig-rrc-connection-reconfiguration 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 fig-rrc-connection-reconf-handover 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 signalled 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 sched 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 directy with
the MME to perfom 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 fig-nas-attach 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
S1-U
The S1-U interface is 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
fig-lte-epc-e2e-data-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 layers involves the UEs, the PGW and the remote host (including eventual internet routers and hosts
in between), but does not involve the eNB. By default, UEs are assigned a public IPv4 address in the
7.0.0.0/8 network, and the PGW gets the address 7.0.0.1, which 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 and the
SGW/PGW node. This network is implemented as a set of point-to-point links which connect each eNB with
the SGW/PGW node; thus, the SGW/PGW 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 fig-epc-data-flow-dl.
Downlink Ipv4 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 SGW/PGW node
which is connected to the internet (this is the Gi interface according to 3GPP terminology). The
SGW/PGW has a VirtualNetDevice which is assigned the gateway IP address of the UE subnet; hence, static
routing rules will cause the incoming packet from the internet to be routed through this
VirtualNetDevice. Such device starts the GTP/UDP/IP tunneling procedure, by forwarding the packet to a
dedicated application in the SGW/PGW node which is called EpcSgwPgwApplication. This application does
the following operations:
1. it determines the eNB node to which the UE is attached, 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 S1-U 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 an UDP socket to the S1-U point-to-point NetDevice, addressed to
the eNB to which the UE is attached.
As a consequence, 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
outmost 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 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 Radio Bearer ID (RBID) to which the packet belongs;
3. it records the RBID in a dedicated tag called LteRadioBearerTag, 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 RBID from the LteRadioBearerTag, and based
on the RBID 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 fig-epc-data-flow-ul. 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 RBID of the packet. This
RBID is then recorded onto an LteRadioBearerTag, 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 RBID from the LteRadioBearerTag 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/PGW 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/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
EpcSgwPgwApplication via the correponding UDP socket. The EpcSgwPgwApplication 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 SGW/PGW. In the event
that the packet is addressed to another UE, the IP stack of the SGW/PGW will redirect the packet again to
the VirtualNetDevice, and the packet will go through the dowlink delivery process in order to reach its
destination UE.
Note that the EPS Bearer QoS is not enforced on the S1-U 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 an ideal fashion, with direct interaction between the eNB and the MME
objects, without actually implementing the encoding of S1AP messages and information elements specified
in [TS36413] and without actually transmitting any PDU on any 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
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 overall-architecture.
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 fig-x2-based-handover-seq-diagram shows the interaction of the entities of the X2 model in the
simulator.
[image] Sequence diagram of the X2-based handover.UNINDENT
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 fig-x2-entity-saps shows the implentation 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 fig-lte-epc-x2-interface 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.
Helpers
Two helper objects are use 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
• EpcHelper, which takes care of the configuratio of the Evolved Packet Core
It is possible to create a simple LTE-only simulations by using 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 LteHelper being the Master that interacts directly with the user program, and
EpcHelper working "under the hood" to configure the EPC upon explicit methods called by LteHelper. The
exact interactions are displayed in the Figure fig-helpers.
[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 a 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;
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->ActivateEpsBearer (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::EpcHelper"
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
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::RsrpSinrFilename
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 of the SINR peceived by the RBs
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. 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
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 correcly. 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
Buildings Mobility Model
We now explain by examples how to use the buildings model (in particular, the BuildingMobilityModel 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/buildings-mobility-model.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::BuildingsMobilityModel");
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);
NetDeviceContainer ueDevs;
ueDevs = lteHelper->InstallUeDevice (ueNodes);
Ptr<BuildingsMobilityModel> mm0 = enbNodes.Get (0)->GetObject<BuildingsMobilityModel> ();
Ptr<BuildingsMobilityModel> mm1 = enbNodes.Get (1)->GetObject<BuildingsMobilityModel> ();
mm0->SetPosition (Vector (5.0, 5.0, 1.5));
mm1->SetPosition (Vector (30.0, 40.0, 1.5));
This positions the node on the scenario. Note that, in this example, node 0 will be in the building, and
node 1 will be out of the building. Note that this alone is not sufficient to setup the topology
correctly. What is left to be done is to issue the following command after we have placed all nodes in
the simulation:
BuildingsHelper::MakeMobilityModelConsistent ();
This command will go through the lists of all nodes and of all buildings, determine for each user if it
is indoor or outdoor, and if indoor it will also determine the building in which the user is located and
the corresponding floor and number inside the building.
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 m
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.
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 ("ChannelPath", StringValue ("/ChannelList/0"));
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->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 correspondly
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 networking with LTE devices. In other words, you
will be able to use the regular ns-3 applications and sockets over IPv4 over LTE, and also to connect an
LTE network to any other IPv4 network you might have in your simulation.
First of all, in your simulation program you need to create two helpers:
Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
Ptr<EpcHelper> epcHelper = CreateObject<EpcHelper> ();
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
correspondance 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, upon construction, the EpcHelper will also create and configure the PGW node. Its
configuration in particular is very complex, and hence is done automatically by the Helper. Still, it is
allowed to access the PGW node in order to connect it to other IPv4 network (e.g., the internet). Here is
a very simple example about how to connect a single remote host 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);
It's important to specify routes so that the remote host can reach LTE UEs. One way of doing this is by
exploiting the fact that the EpcHelper will by default assign to LTE UEs an IP address in the 7.0.0.0
network. With this in mind, it suffices to do:
Ipv4StaticRoutingHelper ipv4RoutingHelper;
Ptr<Ipv4StaticRouting> remoteHostStaticRouting = ipv4RoutingHelper.GetStaticRouting (remoteHost->GetObject<Ipv4> ());
remoteHostStaticRouting->AddNetworkRouteTo (Ipv4Address ("7.0.0.0"), Ipv4Mask ("255.0.0.0"), 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 = epcHelper->AssignUeIpv4Address (NetDeviceContainer (ueLteDevice));
// set the default gateway for the UE
Ptr<Ipv4StaticRouting> 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->ActivateEpsBearer (ueLteDevs, EpsBearer (EpsBearer::NGBR_VIDEO_TCP_DEFAULT), pf);
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 ();
X2-based handover
The execution of an X2-based handover between two eNBs requires the configuration of an X2 interface
between the two eNBs. This needs to be done explicitly within the simulation program like this:
lteHelper->AddX2Interface (enbNodes);
where enbNodes is a NodeContainer that contains the two eNBs between which the X2 interface is to be
configured.
Handover event needs to be scheduled explicitly within the simulation program, as the current RRC model
does not support the automatic trigger of handover based on UE measurement. The LteHelper 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, an 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.
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 << 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 << 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 << 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 << 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
Whether a target eNB will accept or not an incoming X2 HANDOVER REQUEST is controlled by the boolean
attribute LteEnbRrc::AdmitHandoverRequest (default: true). As an example, you can run the
lena-x2-handover program setting the attribute to false in this way:
NS_LOG=EpcX2:LteEnbRrc ./waf --run lena-x2-handover
--command="%s --ns3::LteEnbRrc::AdmitHandoverRequest=false"
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 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 customize by changing the corresponding global variable. To get a list of all these
parameters, you can run this command:
./waf --run lena-dual-stripe --command-template="%s --PrintGlobals"
• The system simulation scenarios mentioned in section A.2 of [TR36814]
Testing Documentation
Overview
To test and validate the ns-3 LTE module, several test suites are 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=lte --enable-examples
./test.py
The above will run not only the test suites belonging to the LTE module, but also those belonging to all
the other ns-3 modules on which the LTE 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
Unit Tests
SINR calculation in the Downlink
The test suite lte-downlink-sinr checks that the SINR calculation in downlink is performed correctly. The
SINR in the downlink is calculated for each RB assigned to data transmissions by dividing the power of
the intended signal from the considered eNB by the sum of the noise power plus all the transmissions on
the same RB coming from other eNBs (the interference signals):
In general, different signals can be active during different periods of time. We define a chunk as the
time interval between any two events of type either start or end of a waveform. In other words, a chunk
identifies a time interval during which the set of active waveforms does not change. Let i be the generic
chunk, T_i its duration and thrm{SINR_i} its SINR, calculated with the above equation. The calculation of
the average SINR riemeSINR calculation in the Uplink
The test suite lte-uplink-sinr checks that the SINR calculation in uplink is performed correctly. This
test suite is identical to lte-downlink-sinr described in the previous section, with the difference than
both the signal and the interference now refer to transmissions by the UEs, and reception is performed by
the eNB. This test suite recreates a number of random transmitted signals and interference signals to
mimic a scenario where an eNB is trying to decode the signal from several UEs simultaneously (the ones in
the cell of the eNB) while facing interference from other UEs (the ones belonging to other cells).
The test vectors are obtained by a dedicated Octave script. The test passes if the calculated values are
equal to the test vector within a tolerance of 10^{-7} which, as for the downlink SINR test, deals with
floating point arithmetic approximation issues.
E-UTRA Absolute Radio Frequency Channel Number (EARFCN)
The test suite lte-earfcn checks that the carrier frequency used by the LteSpectrumValueHelper class
(which implements the LTE spectrum model) is done in compliance with [TS36101], where the E-UTRA Absolute
Radio Frequency Channel Number (EARFCN) is defined. The test vector for this test suite comprises a set
of EARFCN values and the corresponding carrier frequency calculated by hand following the specification
of [TS36101]. The test passes if the carrier frequency returned by LteSpectrumValueHelper is the same as
the known value for each element in the test vector.
System Tests
Adaptive Modulation and Coding Tests
The test suite lte-link-adaptation provides system tests recreating a scenario with a single eNB and a
single UE. Different test cases are created corresponding to different SNR values perceived by the UE.
The aim of the test is to check that in each test case the chosen MCS corresponds to some known reference
values. These reference values are obtained by re-implementing in Octave (see
src/lte/test/reference/lte_amc.m) the model described in Section sec-lte-amc for the calculation of the
spectral efficiency, and determining the corresponding MCS index by manually looking up the tables in
[R1-081483]. The resulting test vector is represented in Figure fig-lte-mcs-index.
The MCS which is used by the simulator is measured by obtaining the tracing output produced by the
scheduler after 4ms (this is needed to account for the initial delay in CQI reporting). The SINR which is
calcualted by the simulator is also obtained using the LteSinrChunkProcessor interface. The test passes
if both the following conditions are satisfied:
1. the SINR calculated by the simulator correspond to the SNR of the test vector within an absolute
tolerance of 10^{-7};
2. the MCS index used by the simulator exactly corresponds to the one in the test vector.
[image] Test vector for Adaptive Modulation and Coding.UNINDENT
Inter-cell Interference Tests
The test suite lte-interference` provides system tests recreating an inter-cell interference scenario
with two eNBs, each having a single UE attached to it and employing Adaptive Modulation and Coding both
in the downlink and in the uplink. The topology of the scenario is depicted in Figure
fig-lte-interference-test-scenario. The d_1 parameter represents the distance of each UE to the eNB it is
attached to, whereas the d_2 parameter represent the interferer distance. We note that the scenario
topology is such that the interferer distance is the same for uplink and downlink; still, the actual
interference power perceived will be different, because of the different propagation loss in the uplink
and downlink bands. Different test cases are obtained by varying the d_1 and d_2 parameters.
[image] Topology for the inter-cell interference test.UNINDENT
The test vectors are obtained by use of a dedicated octave script (available in
src/lte/test/reference/lte_link_budget_interference.m), which does the link budget calculations
(including interference) corresponding to the topology of each test case, and outputs the resulting
SINR and spectral efficiency. The latter is then used to determine (using the same procedure adopted
for sec-lte-amc-tests. We note that the test vector contains separate values for uplink and downlink.
Round Robin scheduler performance
The test suite lte-rr-ff-mac-scheduler creates different test cases with a single eNB and several UEs,
all having the same Radio Bearer specification. In each test case, the UEs see the same SINR from the
eNB; different test cases are implemented by using different distance among UEs and the eNB (i.e.,
therefore having different SINR values) and different numbers of UEs. The test consists on checking that
the obtained throughput performance is equal among users and matches a reference throughput value
obtained according to the SINR perceived within a given tolerance.
The test vector is obtained according to the values of transport block size reported in table 7.1.7.2.1-1
of [TS36213], considering an equal distribution of the physical resource block among the users using
Resource Allocation Type 0 as defined in Section 7.1.6.1 of [TS36213]. Let au be the TTI duration, N be
the number of UEs, B the transmission bandwidth configuration in number of RBs, G the RBG size, M the
modulation and coding scheme in use at the given SINR and S(M, B) be the transport block size in bits as
defined by 3GPP TS 36.213. We first calculate the number L of RBGs allocated to each user as
The reference throughput T in bit/s achieved by each UE is then calculated as
The test passes if the measured throughput matches with the reference throughput within a relative
tolerance of 0.1. This tolerance is needed to account for the transient behavior at the beginning of the
simulation (e.g., CQI feedback is only available after a few subframes) as well as for the accuracy of
the estimator of the average throughput performance over the chosen simulation time (0.4s). This choice
of the simulation time is justified by the need to follow the ns-3 guidelines of keeping the total
execution time of the test suite low, in spite of the high number of test cases. In any case, we note
that a lower value of the tolerance can be used when longer simulations are run.
In Figure fig-lenaThrTestCase1, the curves labeled "RR" represent the test values calculated for the RR
scheduler test, as a function of the number of UEs and of the MCS index being used in each test case.
[image] Test vectors for the RR and PF Scheduler in the downlink in a scenario where all UEs use the
same MCS..UNINDENT
Proportional Fair scheduler performance
The test suite lte-pf-ff-mac-scheduler creates different test cases with a single eNB, using the
Proportional Fair (PF) scheduler, and several UEs, all having the same Radio Bearer specification. The
test cases are grouped in two categories in order to evaluate the performance both in terms of the
adaptation to the channel condition and from a fairness perspective.
In the first category of test cases, the UEs are all placed at the same distance from the eNB, and hence
all placed in order to have the same SINR. Different test cases are implemented by using a different SINR
value and a different number of UEs. The test consists on checking that the obtained throughput
performance matches with the known reference throughput up to a given tolerance. The expected behavior of
the PF scheduler when all UEs have the same SNR is that each UE should get an equal fraction of the
throughput obtainable by a single UE when using all the resources. We calculate the reference throughput
value by dividing the throughput achievable by a single UE at the given SNR by the total number of UEs.
Let au be the TTI duration, B the transmission bandwidth configuration in number of RBs, M the modulation
and coding scheme in use at the given SINR and S(M, B) be the transport block size as defined in
[TS36213]. The reference throughput T in bit/s achieved by each UE is calculated as
The curves labeled "PF" in Figure fig-lenaThrTestCase1 represent the test values calculated for the PF
scheduler tests of the first category, that we just described.
The second category of tests aims at verifying the fairness of the PF scheduler in a more realistic
simulation scenario where the UEs have a different SINR (constant for the whole simulation). In these
conditions, the PF scheduler will give to each user a share of the system bandwidth that is proportional
to the capacity achievable by a single user alone considered its SINR. In detail, let M_i be the
modulation and coding scheme being used by each UE (which is a deterministic function of the SINR of the
UE, and is hence known in this scenario). Based on the MCS, we determine the achievable rate R_i for each
hoe{R,i}sofgeach usereiuas described in Section~ref{sec:pfs}. We then define the achievable rate ratio
Let now T_i be the throughput actually achieved ho_{T,i}Eof UEwiias is obtained as part of the simulation
output. We define the obtained throughput ratio
The test consists of checking that the following condition is verified:
if so, it means that the throughput obtained by each UE over the whole simulation matches with the
steady-state throughput expected by the PF scheduler according to the theory. This test can be derived
from [Holtzman2000] as follows. From Section 3 of [Holtzman2000], we know that
ho_{T,i} given previously, we get
where c is a constant. By substituting the above into the definition of
which is exactly the expression being used in the test.
Figure fig-lenaThrTestCase2 presents the results obtained in a test case with UEs i=1,ots,5 that are
located at a distance from the base station such that they will use respectively the MCS index 28, 24,
16, 12, 6. From the figure, we note that, as expected, the obtained throughput is proportional to the
achievable rate. In other words, the PF scheduler assign more resources to the users that use a higher
MCS index.
[image] Throughput ratio evaluation for the PF scheduler in a scenario where the UEs have MCS index 28,
24, 16, 12, 6.UNINDENT
Maximum Throughput scheduler performance
Test suites lte-fdmt-ff-mac-scheduler and lte-tdmt-ff-mac-scheduler create different test cases with a
single eNB and several UEs, all having the same Radio Bearer specification, using the Frequency Domain
Maximum Throughput (FDMT) scheduler and Time Domain Maximum Throughput (TDMT) scheduler respectively. In
other words, UEs are all placed at the same distance from the eNB, and hence all placed in order to have
the same SNR. Different test cases are implemented by using a different SNR values and a different number
of UEs. The test consists on checking that the obtained throughput performance matches with the known
reference throughput up to a given tolerance.The expected behavior of both FDMT and TDMT scheduler when
all UEs have the same SNR is that scheduler allocates all RBGs to the first UE defined in script. This is
because the current FDMT and TDMT implementation always select the first UE to serve when there are
multiple UEs having the same SNR value. We calculate the reference throughput value for first UE by the
throughput achievable of a single UE at the given SNR, while reference throughput value for other UEs by
zero. Let au be the TTI duration, B the transmission bandwidth configuration in number of RBs, M the
modulation and coding scheme in use at the given SNR and S(M, B) be the transport block size as defined
in [TS36.213]. The reference throughput T in bit/s achieved by each UE is calculated as
Throughput to Average scheduler performance
Test suites lte-tta-ff-mac-scheduler create different test cases with a single eNB and several UEs, all
having the same Radio Bearer specification using TTA scheduler. Network topology and configurations in
TTA test case are as the same as the test for MT scheduler. More complex test case needs to be developed
to show the fairness feature of TTA scheduler.
Blind Average Throughput scheduler performance
Test suites lte-tdbet-ff-mac-scheduler and lte-fdbet-ff-mac-scheduler create different test cases with a
single eNB and several UEs, all having the same Radio Bearer specification.
In the first test case of lte-tdbet-ff-mac-scheduler and lte-fdbet-ff-mac-scheduler, the UEs are all
placed at the same distance from the eNB, and hence all placed in order to have the same SNR. Different
test cases are implemented by using a different SNR value and a different number of UEs. The test
consists on checking that the obtained throughput performance matches with the known reference throughput
up to a given tolerance. In long term, the expected behavior of both TD-BET and FD-BET when all UEs have
the same SNR is that each UE should get an equal throughput. However, the exact throughput value of
TD-BET and FD-BET in this test case is not the same.
When all UEs have the same SNR, TD-BET can be seen as a specific case of PF where achievable rate equals
to 1. Therefore, the throughput obtained by TD-BET is equal to that of PF. On the other hand, FD-BET
performs very similar to the round robin (RR) scheduler in case of that all UEs have the same SNR and the
number of UE( or RBG) is an integer multiple of the number of RBG( or UE). In this case, FD-BET always
allocate the same number of RBGs to each UE. For example, if eNB has 12 RBGs and there are 6 UEs, then
each UE will get 2 RBGs in each TTI. Or if eNB has 12 RBGs and there are 24 UEs, then each UE will get 2
RBGs per two TTIs. When the number of UE (RBG) is not an integer multiple of the number of RBG (UE),
FD-BET will not follow the RR behavior because it will assigned different number of RBGs to some UEs,
while the throughput of each UE is still the same.
The second category of tests aims at verifying the fairness of the both TD-BET and FD-BET schedulers in a
more realistic simulation scenario where the UEs have a different SNR (constant for the whole
simulation). In this case, both scheduler should give the same amount of averaged throughput to each
user.
Specifically, for TD-BET, let F_i be the fraction of time allocated to user i in total simulation time,
R^{fb}_i be the the full bandwidth achievable rate for user i and T_i be the achieved throughput of user
i. Then we have:
In TD-BET, the sum of F_i for all user equals one. In long term, all UE has the same T_i so that we
replace T_i by T. Then we have:
Token Band Fair Queue scheduler performance
Test suites lte-fdtbfq-ff-mac-scheduler and lte-tdtbfq-ff-mac-scheduler create different test cases for
testing three key features of TBFQ scheduler: traffic policing, fairness and traffic balance. Constant
Bit Rate UDP traffic is used in both downlink and uplink in all test cases. The packet interval is set
to 1ms to keep the RLC buffer non-empty. Different traffic rate is achieved by setting different packet
size. Specifically, two classes of flows are created in the testsuites:
• Homogeneous flow: flows with the same token generation rate and packet arrival rate
• Heterogeneous flow: flows with different packet arrival rate, but with the same token generation
rate
In test case 1 verifies traffic policing and fairness features for the scenario that all UEs are placed
at the same distance from the eNB. In this case, all Ues have the same SNR value. Different test cases
are implemented by using a different SNR value and a different number of UEs. Because each flow have the
same traffic rate and token generation rate, TBFQ scheduler will guarantee the same throughput among UEs
without the constraint of token generation rate. In addition, the exact value of UE throughput is
depended on the total traffic rate:
• If total traffic rate <= maximum throughput, UE throughput = traffic rate
• If total traffic rate > maximum throughput, UE throughput = maximum throughput / N
Here, N is the number of UE connected to eNodeB. The maximum throughput in this case equals to the rate
that all RBGs are assigned to one UE(e.g., when distance equals 0, maximum throughput is 2196000
byte/sec). When the traffic rate is smaller than max bandwidth, TBFQ can police the traffic by token
generation rate so that the UE throughput equals its actual traffic rate (token generation rate is set to
traffic generation rate); On the other hand, when total traffic rate is bigger than the max throughput,
eNodeB cannot forward all traffic to UEs. Therefore, in each TTI, TBFQ will allocate all RBGs to one UE
due to the large packets buffered in RLC buffer. When a UE is scheduled in current TTI, its token
counter is decreased so that it will not be scheduled in the next TTI. Because each UE has the same
traffic generation rate, TBFQ will serve each UE in turn and only serve one UE in each TTI (both in TD
TBFQ and FD TBFQ). Therefore, the UE throughput in the second condition equals to the evenly share of
maximum throughput.
Test case 2 verifies traffic policing and fairness features for the scenario that each UE is placed at
the different distance from the eNB. In this case, each UE has the different SNR value. Similar to test
case 1, UE throughput in test case 2 is also depended on the total traffic rate but with a different
maximum throughput. Suppose all UEs have a high traffic load. Then the traffic will saturate the RLC
buffer in eNodeB. In each TTI, after selecting one UE with highest metric, TBFQ will allocate all RBGs to
this UE due to the large RLC buffer size. On the other hand, once RLC buffer is saturated, the total
throughput of all UEs cannot increase any more. In addition, as we discussed in test case 1, for
homogeneous flows which have the same t_i and r_i, each UE will achieve the same throughput in long term.
Therefore, we can use the same method in TD BET to calculate the maximum throughput:
Here, T is the maximum throughput. R^{fb}_i be the the full bandwidth achievable rate for user i. N is
the number of UE.
When the totol traffic rate is bigger than T, the UE throughput equals to ac{T}{N} . Otherwise, UE
throughput equals to its traffic generation rate.
In test case 3, three flows with different traffic rate are created. Token generation rate for each flow
is the same and equals to the average traffic rate of three flows. Because TBFQ use a shared token bank,
tokens contributed by UE with lower traffic load can be utilized by UE with higher traffic load. In this
way, TBFQ can guarantee the traffic rate for each flow. Although we use heterogeneous flow here, the
calculation of maximum throughput is as same as that in test case 2. In calculation max throughput of
test case 2, we assume that all UEs suffer high traffic load so that scheduler always assign all RBGs to
one UE in each TTI. This assumes is also true in heterogeneous flow case. In other words, whether those
flows have the same traffic rate and token generation rate, if their traffic rate is bigger enough, TBFQ
performs as same as it in test case 2. Therefore, the maximum bandwidth in test case 3 is as same as it
in test case 2.
In test case 3, in some flows, token generate rate does not equal to MBR, although all flows are CBR
traffic. This is not accorded with our parameter setting rules. Actually, the traffic balance feature is
used in VBR traffic. Because different UE's peak rate may occur in different time, TBFQ use shared token
bank to balance the traffic among those VBR traffics. Test case 3 use CBR traffic to verify this feature.
But in the real simulation, it is recommended to set token generation rate to MBR.
Priority Set scheduler performance
Test suites lte-pss-ff-mac-scheduler create different test cases with a single eNB and several UEs. In
all test cases, we select PFsch in FD scheduler. Same testing results can also be obtained by using CoItA
scheduler. In addition, all test cases do not define nMux so that TD scheduler in PSS will always select
half of total UE.
In the first class test case of lte-pss-ff-mac-scheduler, the UEs are all placed at the same distance
from the eNB, and hence all placed in order to have the same SNR. Different test cases are implemented by
using a different TBR for each UEs. In each test cases, all UEs have the same Target Bit Rate configured
by GBR in EPS bear setting. The expected behavior of PSS is to guarantee that each UE's throughput at
least equals its TBR if the total flow rate is blow maximum throughput. Similar to TBFQ, the maximum
throughput in this case equals to the rate that all RBGs are assigned to one UE. When the traffic rate
is smaller than max bandwidth, the UE throughput equals its actual traffic rate; On the other hand, UE
throughput equals to the evenly share of the maximum throughput.
In the first class of test cases, each UE has the same SNR. Therefore, the priority metric in PF
scheduler will be determined by past average throughput T_{j}(t) because each UE has the same achievable
throughput R_{j}(k,t) in PFsch or same CoI[k,n] in CoItA. This means that PSS will performs like a TD-BET
which allocates all RBGs to one UE in each TTI. Then the maximum value of UE throughput equals to the
achievable rate that all RBGs are allocated to this UE.
In the second class of test case of lte-pss-ff-mac-scheduler, the UEs are all placed at the same distance
from the eNB, and hence all placed in order to have the same SNR. Different TBR values are assigned to
each UE. There also exist an maximum throughput in this case. Once total traffic rate is bigger than
this threshold, there will be some UEs that cannot achieve their TBR. Because there is no fading, subband
CQIs for each RBGs frequency are the same. Therefore, in FD scheduler,in each TTI, priority metrics of UE
for all RBGs are the same. This means that FD scheduler will always allocate all RBGs to one user.
Therefore, in the maximum throughput case, PSS performs like a TD-BET. Then we have:
Here, T is the maximum throughput. R^{fb}_i be the the full bandwidth achievable rate for user i. N is
the number of UE.
Building Propagation Loss Model
The aim of the system test is to verify the integration of the BuildingPathlossModel with the lte module.
The test exploits a set of three pre calculated losses for generating the expected SINR at the receiver
counting the transmission and the noise powers. These SINR values are compared with the results obtained
from a LTE simulation that uses the BuildingPathlossModel. The reference loss values are calculated
off-line with an Octave script (/test/reference/lte_pathloss.m). Each test case passes if the reference
loss value is equal to the value calculated by the simulator within a tolerance of 0.001 dB, which
accouns for numerical errors in the calculations.
Physical Error Model
The test suite lte-phy-error-model generates different test cases for evaluating both data and control
error models. For what concern the data, the test consists of nine test cases with single eNB and a
various number of UEs, all having the same Radio Bearer specification. Each test is designed for
evaluating the error rate perceived by a specific TB size in order to verify that it corresponds to the
expected values according to the BLER generated for CB size analog to the TB size. This means that, for
instance, the test will check that the performance of a TB of N bits is analogous to the one of a a CB
size of N bits by collecting the performance of a user which has been forced the generation of a such TB
size according to the distance to eNB. In order to significantly test the BER at MAC level, we modified
the Adaptive Modulation and Coding (AMC) module, the LteAmc class, for making it less robust to channel
conditions by adding a configurable BER parameter (called Ber in the ns3 attribute system) which enable
the selection of the desired BER at MAC level when choosing the MCS to be used. In detail, the AMC module
has been forced to select the AMC considering a BER of 0.01 (instead of the standard value equal to
0.00005). We note that, these values do not reflect actual BER since they come from an analytical bound
which do not consider all the transmission chain aspects; therefore the resulted BER might be different.
The parameters of the nine test cases are reported in the following:
1. 4 UEs placed 1800 meters far from the eNB, which implies the use of MCS 2 (SINR of -5.51 dB) and a
TB of 256 bits, that in turns produce a BER of 0.33 (see point A in figure fig-mcs-2-test).
2. 2 UEs placed 1800 meters far from the eNB, which implies the use of MCS 2 (SINR of -5.51 dB) and a
TB of 528 bits, that in turns produce a BER of 0.11 (see point B in figure fig-mcs-2-test).
3. 1 UE placed 1800 meters far from the eNB, which implies the use of MCS 2 (SINR of -5.51 dB) and a
TB of 1088 bits, that in turns produce a BER of 0.02 (see point C in figure fig-mcs-2-test).
4. 1 UE placed 600 meters far from the eNB, which implies the use of MCS 12 (SINR of 4.43 dB) and a TB
of 4800 bits, that in turns produce a BER of 0.3 (see point D in figure fig-mcs-12-test).
5. 3 UEs placed 600 meters far from the eNB, which implies the use of MCS 12 (SINR of 4.43 dB) and a
TB of 1632 bits, that in turns produce a BER of 0.55 (see point E in figure fig-mcs-12-test).
6. 1 UE placed 470 meters far from the eNB, which implies the use of MCS 16 (SINR of 8.48 dB) and a TB
of 7272 bits (segmented in 2 CBs of 3648 and 3584 bits), that in turns produce a BER of 0.14, since
each CB has CBLER equal to 0.075 (see point F in figure fig-mcs-14-test).
[image] BLER for tests 1, 2, 3..UNINDENT
[image] BLER for tests 4, 5..UNINDENT
[image] BLER for test 6..UNINDENT
The test verifies that in each case the expected number of packets received correct corresponds to a
Bernoulli distribution with a confidence interval of 95%, where the probability of success in each
trail is 1-BER and n is the total number of packet sent.
The error model of PCFICH-PDDCH channels consists of 4 test cases with a single UE and several eNBs,
where the UE is connected to only one eNB in order to have the remaining acting as interfering ones.
The errors on data are disabled in order to verify only the ones due to erroneous decodification of
PCFICH-PDCCH. The test verifies that the error on the data received respects the decodification error
probability of the PCFICH-PDCCH with a tolerance of 0.1 due to the errors that might be produced in
quantizing the MI and the error curve. As before, the system has been forced on working in a less
conservative fashion in the AMC module for appreciating the results in border situations. The
parameters of the 4 tests cases are reported in the following:
1. 2 eNBs placed 1078 meters far from the UE, which implies a SINR of -2.00 dB and a TB of 217 bits,
that in turns produce a BER of 0.007.
2. 3 eNBs placed 1078 meters far from the UE, which implies a SINR of -4.00 dB and a TB of 217 bits,
that in turns produce a BER of 0.045.
3. 4 eNBs placed 1078 meters far from the UE, which implies a SINR of -6.00 dB and a TB of 133 bits,
that in turns produce a BER of 0.206.
4. 5 eNBs placed 1078 meters far from the UE, which implies a SINR of -7.00 dB and a TB of 81 bits,
that in turns produce a BER of 0.343.
HARQ Model
The test suite lte-harq includes two tests for evaluating the HARQ model and the related extension in the
error model. The test consists on checking whether the amount of bytes received during the simulation
corresponds to the expected ones according to the values of transport block and the HARQ dynamics. In
detail, the test checks whether the throughput obtained after one HARQ retransmission is the expeted one.
For evaluating the expected throughput the expected TB delivering time has been evaluated according to
the following formula:
where P_s^i is the probability of receiving with success the HARQ block at the attempt i (i.e., the RV
with 3GPP naming). According to the scenarios, in the test we always have P_s^1 equal to 0.0, while P_s^2
varies in the two tests, in detail:
The expected throughput is calculted by counting the number of transmission slots available during the
simulation (e.g., the number of TTIs) and the size of the TB in the simulation, in detail:
where TTI_{NUM} is the total number of TTIs in 1 second. The test is performed both for Round Robin
scheduler. The test passes if the measured throughput matches with the reference throughput within a
relative tolerance of 0.1. This tolerance is needed to account for the transient behavior at the
beginning of the simulation and the on-fly blocks at the end of the simulation.
MIMO Model
The test suite lte-mimo aims at verifying both the effect of the gain considered for each Transmission
Mode on the system performance and the Transmission Mode switching through the scheduler interface. The
test consists on checking whether the amount of bytes received during a certain window of time (0.1
seconds in our case) corresponds to the expected ones according to the values of transport block size
reported in table 7.1.7.2.1-1 of [TS36213], similarly to what done for the tests of the schedulers.
The test is performed both for Round Robin and Proportional Fair schedulers. The test passes if the
measured throughput matches with the reference throughput within a relative tolerance of 0.1. This
tolerance is needed to account for the transient behavior at the beginning of the simulation and the
transition phase between the Transmission Modes.
Antenna Model integration
The test suite lte-antenna checks that the AntennaModel integrated with the LTE model works correctly.
This test suite recreates a simulation scenario with one eNB node at coordinates (0,0,0) and one UE node
at coordinates (x,y,0). The eNB node is configured with an CosineAntennaModel having given orientation
and beamwidth. The UE instead uses the default IsotropicAntennaModel. The test checks that the received
power both in uplink and downlink account for the correct value of the antenna gain, which is determined
offline; this is implemented by comparing the uplink and downlink SINR and checking that both match with
the reference value up to a tolerance of 10^{-6} which accounts for numerical errors. Different test
cases are provided by varying the x and y coordinates of the UE, and the beamwidth and the orientation
of the antenna of the eNB.
RLC
Two test suites lte-rlc-um-transmitter and lte-rlc-am-transmitter check that the UM RLC and the AM RLC
implementation work correctly. Both these suites work by testing RLC instances connected to special test
entities that play the role of the MAC and of the PDCP, implementing respectively the LteMacSapProvider
and LteRlcSapUser interfaces. Different test cases (i.e., input test vector consisting of series of
primitive calls by the MAC and the PDCP) are provided that check the behavior in the following cases:
1. one SDU, one PDU: the MAC notifies a TX opportunity causes the creation of a PDU which exactly
contains a whole SDU
2. segmentation: the MAC notifies multiple TX opportunities that are smaller than the SDU size stored
in the transmission buffer, which is then to be fragmented and hence multiple PDUs are generated;
3. concatenation: the MAC notifies a TX opportunity that is bigger than the SDU, hence multiple SDUs
are concatenated in the same PDU
4. buffer status report: a series of new SDUs notifications by the PDCP is inteleaved with a series of
TX opportunity notification in order to verify that the buffer status report procedure is correct.
In all these cases, an output test vector is determine manually from knowledge of the input test vector
and knowledge of the expected behavior. These test vector are specialized for UM RLC and AM RLC due to
their different behavior. Each test case passes if the sequence of primitives triggered by the RLC
instance being tested is exacly equal to the output test vector. In particular, for each PDU transmitted
by the RLC instance, both the size and the content of the PDU are verified to check for an exact match
with the test vector.
RRC
The test suite lte-rrc tests the correct functionality of the following aspects:
1. MAC Random Access
2. RRC System Information Acquisition
3. RRC Connection Establishment
4. RRC Reconfiguration
The test suite considers a type of scenario with a single eNB and multiple UEs that are instructed to
connect to the eNB. Each test case implement an instance of this scenario with specific values of the
following parameters:
• number of UEs
• number of Data Radio Bearers to be activated for each UE
• time t^c_0 at which the first UE is instructed to start connecting to the eNB
• time interval d^i between the start of connection of UE n and UE n+1; the time at which user n
connects is thus determined as t^c_n = t^c_0 + n d^i sdf
• a boolean flag indicating whether the ideal or the real RRC protocol model is used
Each test cases passes if a number of test conditions are positively evaluated for each UE after a delay
d^e from the time it started connecting to the eNB. The delay d^e is determined as
where:
• d^{si} is the max delay necessary for the acquisition of System Information. We set it to 90ms
accounting for 10ms for the MIB acquisition and 80ms for the subsequent SIB2 acquisition
• d^{ra} is the delay for the MAC Random Access (RA) procedure. This depends on preamble collisions as
well as on the availability of resources for the UL grant allocation. The total amount of necessary
RA attempts depends on preamble collisions and failures to allocate the UL grant because of lack of
resources. The number of collisions depends on the number of UEs that try to access simultaneously;
we estimated that for a 0.99 RA success probability, 5 attempts are sufficient for up to 20 UEs, and
10 attempts for up to 50 UEs. For the UL grant, considered the system bandwidth and the default MCS
used for the UL grant (MCS 0), at most 4 UL grants can be assigned in a TTI; so for n ceil.rThegtime
do RA simultaneously the max number of attempts due to the UL grant issue is il n/4
for a RA attempt is determined by 3ms + the value of LteEnbMac::RaResponseWindowSize, which
defaults to 3ms, plus 1ms for the scheduling of the new transmission.
• d^{ce} is the delay required for the transmission of RRC COceilTconsidering thatC2NNRRCIOpackets
COMPLETED. We consider a round trip delay of 10ms plus il 2n/4
have to be transmitted and that at most 4 such packets can be transmitted per TTI.
• d^{cr} is the delay required for eventually needed RRC CONNECTION RECONFIGURATION transactions. The
number of transactions needed is 1 for each bearer activation plus a variable number for SRS
reconfiguration that depends on:math:n:
• 0 for n __IP • 2 1 for n _____IP • 2 2 for n __________IP • 2 3 for n ____________________IP •
2 4 for n > 20
Similarlceil. wdelayoof 20ms.^{ce}, for each transaction we consider a round trip delay of 10ms plus
il 2n/4
The conditions that are evaluated for a test case to pass are, for each UE:
• the eNB has the context of the UE (identified by the RNTI value retrieved from the UE RRC)
• the RRC state of the UE at the eNB is CONNECTED_NORMALLY
• the RRC state at the UE is CONNECTED_NORMALLY
• the UE is configured with the CellId, DlBandwidth, UlBandwidth, DlEarfcn and UlEarfcn of the eNB
• the IMSI of the UE stored at the eNB is correct
• the number of active Data Radio Bearers is the expected one, both at the eNB and at the UE
• for each Data Radio Bearer, the following identifiers match between the UE and the eNB: EPS bearer
id, DRB id, LCID
GTP-U protocol
The unit test suite epc-gtpu checks that the encoding and decoding of the GTP-U header is done correctly.
The test fills in a header with a set of known values, adds the header to a packet, and then removes the
header from the packet. The test fails if, upon removing, any of the fields in the GTP-U header is not
decoded correctly. This is detected by comparing the decoded value from the known value.
S1-U interface
Two test suites (epc-s1u-uplink and epc-s1u-downlink) make sure that the S1-U interface implementation
works correctly in isolation. This is achieved by creating a set of simulation scenarios where the EPC
model alone is used, without the LTE model (i.e., without the LTE radio protocol stack, which is replaced
by simple CSMA devices). This checks that the interoperation between multiple EpcEnbApplication instances
in multiple eNBs and the EpcSgwPgwApplication instance in the SGW/PGW node works correctly in a variety
of scenarios, with varying numbers of end users (nodes with a CSMA device installed), eNBs, and different
traffic patterns (packet sizes and number of total packets). Each test case works by injecting the
chosen traffic pattern in the network (at the considered UE or at the remote host for in the uplink or
the downlink test suite respectively) and checking that at the receiver (the remote host or each
considered UE, respectively) that exactly the same traffic patterns is received. If any mismatch in the
transmitted and received traffic pattern is detected for any UE, the test fails.
TFT classifier
The test suite epc-tft-classifier checks in isolation that the behavior of the EpcTftClassifier class is
correct. This is performed by creating different classifier instances where different TFT instances are
activated, and testing for each classifier that an heterogeneous set of packets (including IP and TCP/UDP
headers) is classified correctly. Several test cases are provided that check the different matching
aspects of a TFT (e.g. local/remote IP address, local/remote port) both for uplink and downlink traffic.
Each test case corresponds to a specific packet and a specific classifier instance with a given set of
TFTs. The test case passes if the bearer identifier returned by the classifier exactly matches with the
one that is expected for the considered packet.
End-to-end LTE-EPC data plane functionality
The test suite lte-epc-e2e-data ensures the correct end-to-end functionality of the LTE-EPC data plane.
For each test case in this suite, a complete LTE-EPC simulation scenario is created with the following
characteristics:
• a given number of eNBs
• for each eNB, a given number of UEs
• for each UE, a given number of active EPS bearers
• for each active EPS bearer, a given traffic pattern (number of UDP packets to be transmitted and
packet size)
Each test is executed by transmitting the given traffic pattern both in the uplink and in the downlink,
at subsequent time intervals. The test passes if all the following conditions are satisfied:
• for each active EPS bearer, the transmitted and received traffic pattern (respectively at the UE
and the remote host for uplink, and vice versa for downlink) is exactly the same
• for each active EPS bearer and each direction (uplink or downlink), exactly the expected number of
packet flows over the corresponding RadioBearer instance
X2 handover
The test suite lte-x2-handover checks the correct functionality of the X2 handover procedure. The
scenario being tested is a topology with two eNBs connected by an X2 interface. Each test case is a
particular instance of this scenario defined by the following parameters:
• the number of UEs that are initially attached to the first eNB
• the number of EPS bearers activated for each UE
• a list of handover events to be triggered, where each event is defined by: + the start time of the
handover trigger + the index of the UE doing the handover + the index of the source eNB + the index
of the target eNB
• a boolean flag indicating whether the target eNB admits the handover or not
• a boolean flag indicating whether the ideal RRC protocol is to be used instead of the real RRC
protocol
• the type of scheduler to be used (RR or PF)
Each test cases passes if the following conditions are true:
• at time 0.06s, the test CheckConnected verifies that each UE is connected to the first eNB
• for each event in the handover list:
• at the indicated event start time, the indicated UE is connected to the indicated source eNB
• 0.1s after the start time, the indicated UE is connected to the indicated target eNB
• 0.6s after the start time, for each active EPS bearer, the uplink and downlink sink applications
of the indicated UE have achieved a number of bytes which is at least half the number of bytes
transmitted by the corresponding source applications
The condition "UE is connected to eNB" is evaluated positively if and only if all the following
conditions are met:
• the eNB has the context of the UE (identified by the RNTI value retrieved from the UE RRC)
• the RRC state of the UE at the eNB is CONNECTED_NORMALLY
• the RRC state at the UE is CONNECTED_NORMALLY
• the UE is configured with the CellId, DlBandwidth, UlBandwidth, DlEarfcn and UlEarfcn of the eNB
• the IMSI of the UE stored at the eNB is correct
• the number of active Data Radio Bearers is the expected one, both at the eNB and at the UE
• for each Data Radio Bearer, the following identifiers match between the UE and the eNB: EPS bearer
id, DRB id, LCID
Handover delays
Handover procedure consists of several message exchanges between UE, source eNodeB, and target eNodeB
over both RRC protocol and X2 interface. Test suite lte-handover-delay verifies that this procedure
consistently spends the same amount of time.
The test suite will run several handover test cases. Eact test case is an individual simulation featuring
a handover at a specified time in simulation. For example, the handover in the first test case is
invoked at time +0.100s, while in the second test case it is at +0.101s. There are 10 test cases, each
testing a different subframe in LTE. Thus the last test case has the handover at +0.109s.
The simulation scenario in the test cases is as follow:
• EPC is enabled
• 2 eNodeBs with circular (isotropic) antenna, separated by 1000 meters
• 1 static UE positioned exactly in the center between the eNodeBs
• no application installed
• no channel fading
• default path loss model (Friis)
• 0.300s simulation duration
The test case runs as follow. At the beginning of the simulation, the UE is attached to the first eNodeB.
Then at the time specified by the test case input argument, a handover request will be explicitly issued
to the second eNodeB. The test case will then record the starting time, wait until the handover is
completed, and then record the completion time. If the difference between the completion time and
starting time is less than a predefined threshold, then the test passes.
A typical handover in the current ns-3 implementation takes 4.2141 ms when using Ideal RRC protocol
model, and 19.9283 ms when using Real RRC protocol model. Accordingly, the test cases use 5 ms and 20 ms
as the maximum threshold values. The test suite runs 10 test cases with Ideal RRC protocol model and 10
test cases with Real RRC protocol model. More information regarding these models can be found in Section
sec-rrc-protocol-models.
The motivation behind using subframes as the main test parameters is the fact that subframe index is one
of the factors for calculating RA-RNTI, which is used by Random Access during the handover procedure. The
test cases verify this computation, utilizing the fact that the handover will be delayed when this
computation is broken. In the default simulation configuration, the handover delay observed because of a
broken RA-RNTI computation is typically 6 ms.
Profiling Documentation
Overview and objectives
The main objective of the profiling carried out is to assess the simulator performance on a broad set of
scenarios. This evaluation provides reference values for simulation running times and memory consumption
figures. It also helps to identify potential performance improvements and to check for scalability
problems when increasing the number of eNodeB and UEs attached to those.
In the following sections, a detailed description of the general profiling framework employed to perform
the study is introduced. It also includes details on the main performed tests and its results evaluation.
Framework description
Simulation scripts
The simulation script used for all the E-UTRAN results showed in this documentation is located at
src/lte/examples/lena-profiling.cc. It uses the complete PHY and MAC UE/eNodeB implementation with a
simplified RLC implementation on top. This script generates a squared grid topology, placing a eNodeB at
the centre of each square. UEs attached to this node are scattered randomly across the square (using a
random uniform distribution along X and Y axis). If BuildingPropagationModel is used, the squares are
replaced by rooms. To generate the UL and DL traffic, the RLC implementation always report data to be
transfered.
[image] E-UTRAN.UNINDENT
For the EPC results, the script is src/lte/examples/lena-simple-epc.cc. It uses a complete E-UTRAN
implementation (PHY+MAC+RLC/UM+PDCP) and the most relevant EPC user plane entities the PGW and SGW,
including GTP-U tunneling. This script generates a given number of eNodeBs, distributed across a line
and attaches a single UE to every eNodeB. It also creates an EPC network and an external host connected
to it through the Internet. Each UE sends and receives data to and from the remote host. In addition,
each UE is also sending data to the UE camped in the adjacent eNodeB.
[image] Propagation Model.UNINDENT
RLC and MAC traces are enabled for all UEs and all eNodeBs and those traces are written to disk
directly. The MAC scheduler used is round robin.
Simulation input parameters
The lena-profiling simulation script accepts the following input parameters:
• simTime: time to simulate (in seconds)
• nUe: number of UEs attached to each eNodeB
• nEnb: number of eNodeB composing the grid per floor
• nFloors: number of floors, 0 for Friis propagation model (no walls), 1 or greater for Building
propagation model generating a nFloors-storey building.
• traceDirectory: destination directory where simulation traces will be stored
The lena-simple-epc script accepts those other parameters:
• simTime: time to simulate (in seconds)
• numberOfNodes: number of eNodeB + UE pairs created
Time measurement
Running time is measured using default Linux shell command time. This command counts how much user time
the execution of a program takes.
Perl script
To simplify the process of running the profiling script for a wide range of values and collecting its
timing data, a simple Perl script to automate the complete process is provided. It is placed in
src/lte/test/lte-test-run-time.pl for lena-profiling and in src/lte/epc-test-run-time.pl for
lena-simple-epc. It simply runs a batch of simulations with a range of parameters and stores the timing
results in a CSV file called lteTimes.csv and epcTimes.csv respectively. The range of values each
parameter sweeps can be modified editing the corresponding script.
Requirements
The following Perl modules are required to use the provided script, all of them available from CPAN:
• IO::CaptureOutput
• Statistics::Descriptive
For installing the modules, simply use the follwing command:
perl -MCPAN -e 'install moduleName'
Plotting results
To plot the results obtained from running the Perl scripts, two gnuplot scripts are provided, in
src/lte/test/lte-test-run-plot and src/lte/test/epc-test-run-plot. Most of the plots available in this
documentation can be reproduced with those, typing the commands gnuplot < src/lte/test/lte-test-run-plot
and gnuplot < src/lte/test/epc-test-run-plot.
Reference software and equipment
All timing tests had been run in a Intel Pentium IV 3.00 GHz machine with 512 Mb of RAM memory running
Fedora Core 10 with a 2.6.27.41-170.2.117 kernel, storing the traces directly to the hard disk.
Also, as a reference configuration, the build has been configured static and optimized. The exact waf
command issued is:
CXXFLAGS="-O3 -w" ./waf -d optimized configure --enable-static --enable-examples --enable-modules=lte
Results
E-UTRAN
The following results and figures had been obtained with LENA changeset 2c5b0d697717.
Running time
This scenario, evaluates the running time for a fixed simulation time (10s) and Friis propagation mode
increasing the number of UEs attached to each eNodeB and the number of planted eNodeBs in the scenario.
[image] Running time.UNINDENT
The figure shows the expected behaviour, since it increases linearly respect the number of UEs per
eNodeB and quadratically respect the total number of eNodeBs.
Propagation model
The objective of this scenario is to evaluate the impact of the propagation model complexity in the
overall run time figures. Therefore, the same scenario is simulated twice: once using the more simple
Friis model, once with the more complex Building model. The rest of the parameters (e.g. number of eNodeB
and of UE attached per eNodeB) were mantained. The timing results for both models are compared in the
following figure.
[image] Propagation Model.UNINDENT
In this situation, results are also coherent with what is expected. The more complex the model, the
higher the running time. Moreover, as the number of computed path losses increases (i.e. more UEs per
eNodeB or more eNodeBs) the extra complexity of the propagation model drives the running time figures
further apart.
Simulation time
In this scenario, for a fixed set of UEs per eNodeB, different simulation times had been run. As the
simulation time increases, running time should also increase linearly, i.e. for a given scenario,
simulate four seconds should take twice times what it takes to simulate two seconds. The slope of this
line is a function of the complexity of the scenario: the more eNodeB / UEs placed, the higher the slope
of the line.
[image] Simulation time.UNINDENT
Memory usage
Massif tool to profile memory consumption
[image] Memory profile.UNINDENT
EPC
The following results and figures had been obtained with LENA changeset e8b3ccdf6673. The rationale
behind the two scenarios profiled on this section is the same than for the E-UTRA part.
Running time
Running time evolution is quadratic since we increase at the same time the number of eNodeB and the
number of UEs.
[image] Running time.UNINDENT
To estimate the additional complexity of the upper LTE Radio Protocol Stack model and the EPC model, we
compare two scenarios using the simplified E-UTRAN version (using only PHY, MAC and the simplified
RLC/SM, with no EPC and no ns-3 applications) against the complete E-UTRAN + EPC (with UM RLC, PDCP,
end-to-end IP networking and regular ns-3 applications). Both configuration have been tested with the
same number of UEs per eNodeB, the same number of eNodeBs, and approximately the same volume of
transmitted data (an exact match was not possible due to the different ways in which packets are
generated in the two configurations).
[image] EPC E-UTRAN running time.UNINDENT
From the figure, it is evident that the additional complexity of using the upper LTE stack plus the EPC
model translates approximately into a doubling of the execution time of the simulations. We believe
that, considered all the new features that have been added, this figure is acceptable.
Simulation time
Finally, again the linearity of the running time as the simulation time increases gets validated through
a set of experiments, as the following figure shows.
[image] Simulation time.UNINDENT
References
[TS25814]
3GPP TS 25.814 "Physical layer aspect for evolved Universal Terrestrial Radio Access"
[TS29274]
3GPP TS 29.274 "Tunnelling Protocol for Control plane (GTPv2-C)"
[TS36101]
3GPP TS 36.101 "E-UTRA User Equipment (UE) radio transmission and reception"
[TS36104]
3GPP TS 36.104 "E-UTRA Base Station (BS) radio transmission and reception"
[TS36211]
3GPP TS 36.211 "E-UTRA Physical Channels and Modulation"
[TS36212]
3GPP TS 36.212 "E-UTRA Multiplexing and channel coding"
[TS36213]
3GPP TS 36.213 "E-UTRA Physical layer procedures"
[TS36300]
3GPP TS 36.300 "E-UTRA and E-UTRAN; Overall description; Stage 2"
[TS36304]
3GPP TS 36.104 "E-UTRA User Equipment (UE) procedures in idle mode"
[TS36321]
3GPP TS 36.321 "E-UTRA Medium Access Control (MAC) protocol specification"
[TS36322]
3GPP TS 36.322 "E-UTRA Radio Link Control (RLC) protocol specification"
[TS36323]
3GPP TS 36.323 "E-UTRA Packet Data Convergence Protocol (PDCP) specification"
[TS36331]
3GPP TS 36.331 "E-UTRA Radio Resource Control (RRC) protocol specification"
[TS36413]
3GPP TS 36.413 "E-UTRAN S1 application protocol (S1AP)"
[TS36420]
3GPP TS 36.420 "E-UTRAN X2 general aspects and principles"
[TS36423]
3GPP TS 36.423 "E-UTRAN X2 application protocol (X2AP)"
[R1-081483]
3GPP R1-081483 Conveying MCS and TB size via PDCCH
[R4-092042]
3GPP R4-092042 "Simulation assumptions and parameters for FDD HeNB RF requirements"
[TR36814]
3GPP TR 36.814 "E-UTRA Further advancements for E-UTRA physical layer aspects"
[FFAPI]
FemtoForum LTE MAC Scheduler Interface Specification v1.11
[ns3tutorial]
The ns-3 Tutorial
[ns3manual]
The ns-3 Manual
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S. Sesia, I. Toufik and M. Baker, "LTE - The UMTS Long Term Evolution - from theory to practice",
Wiley, 2009
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N. Baldo and M. Miozzo, "Spectrum-aware Channel and PHY layer modeling for ns3", Proceedings of ICST
NSTools 2009, Pisa, Italy.
[Piro2010]
Giuseppe Piro, Luigi Alfredo Grieco, Gennaro Boggia, and Pietro Camarda, A Two-level Scheduling
Algorithm for QoS Support in the Downlink of LTE Cellular Networks", Proc. of European Wireless,
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J.M. Holtzman, "CDMA forward link waterfilling power control", in Proc. of IEEE VTC Spring, 2000.
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G. Piro, N. Baldo. M. Miozzo, "An LTE module for the ns-3 network simulator", in Proc. of Wns3 2011
(in conjunction with SimuTOOLS 2011), March 2011, Barcelona (Spain)
[Seo2004]
H. Seo, B. G. Lee. "A proportional-fair power allocation scheme for fair and efficient multiuser
OFDM systems", in Proc. of IEEE GLOBECOM, December 2004. Dallas (USA)
[Ofcom2600MHz]
Ofcom, "Consultation on assessment of future mobile competition and proposals for the award of 800
MHz and 2.6 GHz spectrum and related issues", March 2011
[RealWireless]
RealWireless, Low-power shared access to spectrum for mobile broadband, Final Report, Ofcom Project
MC/073, 18th March 2011
[PaduaPEM]
http://mailman.isi.edu/pipermail/ns-developers/2011-November/009559.html
[ViennaLteSim]
The Vienna LTE Simulators
http://www.nt.tuwien.ac.at/about-us/staff/josep-colom-ikuno/lte-simulators/
[LozanoCost]
Joan Olmos, Silvia Ruiz, Mario García-Lozano and David Martín-Sacristán, "Link Abstraction Models
Based on Mutual Information for LTE Downlink", COST 2100 TD(10)11052 Report
[wimaxEmd]
WiMAX Forum White Paper, WiMAX System Evaluation Methodology, July 2008.
[mathworks]
Matlab R2011b Documentation Communications System Toolbox, Methodology for Simulating Multipath
Fading Channels
[CatreuxMIMO]
S. Catreux, L.J. Greenstein, V. Erceg, "Some results and insights on the performance gains of MIMO
systems," Selected Areas in Communications, IEEE Journal on , vol.21, no.5, pp. 839- 847, June 2003
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J.C. Ikuno, M. Wrulich, M. Rupp, "System Level Simulation of LTE Networks," Vehicular Technology
Conference (VTC 2010-Spring), 2010 IEEE 71st , vol., no., pp.1-5, 16-19 May 2010
[Milos2012]
J. Milos, "Performace Analysis Of PCFICH LTE Control Channel", Proceedings of the 19th Conference STUDENT
EEICT 2012, Brno, CZ, 2012.
[FujitsuWhitePaper]
"Enhancing LTE Cell-Edge Performance via PDCCH ICIC".
[Bharucha2011]
Z. Bharucha, G. Auer, T. Abe, N. Miki, "Femto-to-Macro Control Channel Interference Mitigation via Cell
ID Manipulation in LTE," Vehicular Technology Conference (VTC Fall), 2011 IEEE , vol., no., pp.1-6,
5-8 Sept. 2011
[R4-081920]
3GPP R4-081920 LTE PDCCH/PCFICH Demodulation Performance Results with Implementation Margin
[FCapo2012]
F.Capozzi, G.Piro L.A.Grieco, G.Boggia, P.Camarda, "Downlink Packet Scheduling in LTE Cellular
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G.Mongha, K.I. Pedersen, I.Z. Kovacs, P.E. Mogensen, " QoS Oriented Time and Frequency Domain Packet
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MESH NETDEVICE
Placeholder chapter
The Mesh NetDevice based on 802.11s was added in ns-3.6. An overview presentation by Kirill Andreev was
published at the wns-3 workshop in 2009: http://www.nsnam.org/wiki/index.php/Wns3-2009.
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, a conservative synchronization algorithm with lookahead is 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.
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.
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 mpirun. Here are a
few examples (from the root ns-3 directory)::
mpirun -np 2 ./waf --run simple-distributed
mpirun -np 4 -machinefile mpihosts ./waf --run 'nms-udp-nix --LAN=2 --CN=4 --nix=1'
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.
NOTE: Some users have experienced issues using mpirun and waf together. An alternative way to run
distributed examples is shown below::
./waf shell
cd build/debug
mpirun -np 2 src/mpi/examples/simple-distributed
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, special 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 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
http://trac.osgeo.org/proj/ 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.
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 ns2 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 http://net.cs.uni-bonn.de/wg/cs/applications/bonnmotion/
• Some installation instructions for BonnMotion can be found here:
http://www.nsnam.org/wiki/index.php/HOWTO_use_ns-3_with_BonnMotion_mobility_generator_and_analysis_tool
• Documentation on using BonnMotion with ns-3 is posted here:
http://www.ida.liu.se/~rikno/files/mobility_generation.pdf
• SUMO http://sourceforge.net/apps/mediawiki/sumo/index.php?title=Main_Page
• TraNS http://trans.epfl.ch/
• the setdest utility in ns-2, documented here:
http://www.winlab.rutgers.edu/~zhibinwu/html/ns2_wireless_scene.htm
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
TBD
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
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::EnableMetadata() 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 packets is a high-level overview of the Packet implementation; more detail on the byte Buffer
implementation is provided later in Figure 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.
*
* \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
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.
PacketTags are limited in size to 20 bytes. This is a modifiable compile-time constant in
src/network/model/packet-tag-list.h. ByteTags have no such restriction.
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.
As of ns-3.5 and later, Tags are not serialized and deserialized to a buffer when Packet::Serialize ()
and Packet::Deserialize () are called; this is an open bug.
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 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 (mimicing 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. 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 :cpp:func:`HandleSendCallback` is called
* :cpp:func:`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.
Socket options
to be completed
Socket errno
to be completed
Example programs
to be completed
POSIX-like sockets API
Simple NetDevice
Placeholder chapter
Queues
This section documents a few queue objects, typically associated with NetDevice models, that are
maintained as part of the network module:
• DropTail
• Random Early Detection
Model Description
The source code for the new module lives in the directory src/network/utils.
ns-3 provides a couple of classic queue models and the ability to trace certain queue operations such as
enqueuing, dequeuing, and dropping. These may be added to certain NetDevice objects that take a
Ptr<Queue> pointer.
Note that not all device models use these queue models. In particular, WiFi, WiMax, and LTE use
specialized device queues. The queue models described here are more often used with simpler ns-3 device
models such as PointToPoint and Csma.
Design
An abstract base class, class Queue, is typically used and subclassed for specific scheduling and drop
policies. Common operations include:
• bool Enqueue (Ptr<Packet> p): Enqueue a packet
• Ptr<Packet> Dequeue (void): Dequeue a packet
• uint32_t GetNPackets (void): Get the queue depth, in packets
• uint32_t GetNBytes (void): Get the queue depth, in packets
as well as tracking some statistics on queue operations.
There are three trace sources that may be hooked:
• Enqueue
• Dequeue
• Drop
DropTail
This is a basic first-in-first-out (FIFO) queue that performs a tail drop when the queue is full.
Random Early Detection
Random Early Detection (RED) is a queue variant 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.
Scope and Limitations
The RED model just supports default RED. Adaptive RED is not supported.
References
The RED queue aims to be close to the results cited in: S.Floyd, K.Fall
http://icir.org/floyd/papers/redsims.ps
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 from src/network/examples/red-tests.cc:
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::RedQueue", // only backbone link has RED queue
"LinkBandwidth", StringValue (redLinkDataRate),
"LinkDelay", StringValue (redLinkDelay));
p2p.SetDeviceAttribute ("DataRate", StringValue (redLinkDataRate));
p2p.SetChannelAttribute ("Delay", StringValue (redLinkDelay));
NetDeviceContainer devn2n3 = p2p.Install (n2n3);
Attributes
The RED queue contains a number of attributes that control the RED policies:
• Mode (bytes or packets)
• MeanPktSize
• IdlePktSize
• Wait (time)
• Gentle mode
• MinTh, MaxTh
• QueueLimit
• Queue weight
• LInterm
• LinkBandwidth
• LinkDelay
Consult the ns-3 documentation for explanation of these attributes.
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. The RED queue example
is found at src/network/examples/red-tests.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
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).
Model Description
The source code for the OLSR model lives in the directory src/olsr.
Design
Scope and Limitations
The model is for IPv4 only.
• Mostly compliant with OLSR as documented in [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;
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
Usage
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.
Tracing
Logging
Caveats
Validation
Unit tests
Larger-scale performance tests
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), libdl (for address fault checking), and boost
(for assert) libraries to be installed.
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 speed of light transmission 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, for the modeling of respectively propagation loss and propagation delay.
PropagationLossModel
Each of the available propagation loss models of ns-3 is explained in one of the following subsections.
FriisPropagationLossModel
TwoRayGroundPropagationLossModel
LogDistancePropagationLossModel
ThreeLogDistancePropagationLossModel
JakesPropagationLossModel
PropagationLossModel
RandomPropagationLossModel
NakagamiPropagationLossModel
FixedRssLossModel
MatrixPropagationLossModel
RangePropagationLossModel
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:
where
and
f : frequency [MHz]
h_thrm{b} : eNB height above the ground [m]
h_thrm{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:
where for small or medium sized city
and for large cities
There extension for the standard OH in suburban is
where
L_thrm{U} : pathloss in urban areas
The extension for the standard OH in open area is
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.
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
where the breakpoint distance is given by
and the above parameters are
bda : wavelength [m]
h_thrm{b} : eNB height above the ground [m]
h_thrm{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:
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:
The free-space loss is given by:
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
where:
h_r : is the height of the rooftop [m]
h_m : is the height of the mobile [m]
phi : 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
with
Therefore, in case of l > d_s (where l is the distance over which the building extend), it can be
evaluated according to
Alternatively, in case of l < d_s, the formula is:
where
where:
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:
PropagationDelayModel
The following propagation delay models are implemented:
PropagationDelayModel
RandomPropagationDelayModel
ConstantSpeedPropagationDelayModel
[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.
STATISTICAL FRAMEWORK
This chapter outlines work on simulation data collection and the statistical framework for ns-3.
The source code for the statistical framework lives in the directory src/stats.
Goals
Primary objectives for this effort are the following:
• Provide functionality to record, calculate, and present data and statistics for analysis of network
simulations.
• Boost simulation performance by reducing the need to generate extensive trace logs in order to collect
data.
• Enable simulation control via online statistics, e.g. terminating simulations or repeating trials.
Derived sub-goals and other target features include the following:
• Integration with the existing ns-3 tracing system as the basic instrumentation framework of the
internal simulation engine, e.g. network stacks, net devices, and channels.
• Enabling users to utilize the statistics framework without requiring use of the tracing system.
• Helping users create, aggregate, and analyze data over multiple trials.
• Support for user created instrumentation, e.g. of application specific events and measures.
• Low memory and CPU overhead when the package is not in use.
• Leveraging existing analysis and output tools as much as possible. The framework may provide some
basic statistics, but the focus is on collecting data and making it accessible for manipulation in
established tools.
• Eventual support for distributing independent replications is important but not included in the first
round of features.
Overview
The statistics framework includes the following features:
• The core framework and two basic data collectors: A counter, and a min/max/avg/total observer.
• Extensions of those to easily work with times and packets.
• Plaintext output formatted for omnetpp.
• Database output using sqlite3, a standalone, lightweight, high performance SQL engine.
• Mandatory and open ended metadata for describing and working with runs.
• An example based on the notional experiment of examining the properties of NS-3's default ad hoc WiFi
performance. It incorporates the following:
• Constructs of a two node ad hoc WiFi network, with the nodes a parameterized distance apart.
• UDP traffic source and sink applications with slightly different behavior and measurement hooks than
the stock classes.
• Data collection from the NS-3 core via existing trace signals, in particular data on frames
transmitted and received by the WiFi MAC objects.
• Instrumentation of custom applications by connecting new trace signals to the stat framework, as well
as via direct updates. Information is recorded about total packets sent and received, bytes
transmitted, and end-to-end delay.
• An example of using packet tags to track end-to-end delay.
• A simple control script which runs a number of trials of the experiment at varying distances and
queries the resulting database to produce a graph using GNUPlot.
To-Do
High priority items include:
• Inclusion of online statistics code, e.g. for memory efficient confidence intervals.
• Provisions in the data collectors for terminating runs, i.e. when a threshold or confidence is met.
• Data collectors for logging samples over time, and output to the various formats.
• Demonstrate writing simple cyclic event glue to regularly poll some value.
Each of those should prove straightforward to incorporate in the current framework.
Approach
The framework is based around the following core principles:
• One experiment trial is conducted by one instance of a simulation program, whether in parallel or
serially.
• A control script executes instances of the simulation, varying parameters as necessary.
• Data is collected and stored for plotting and analysis using external scripts and existing tools.
• Measures within the ns-3 core are taken by connecting the stat framework to existing trace signals.
• Trace signals or direct manipulation of the framework may be used to instrument custom simulation code.
Those basic components of the framework and their interactions are depicted in the following figure.
[image]
Example
This section goes through the process of constructing an experiment in the framework and producing data
for analysis (graphs) from it, demonstrating the structure and API along the way.
Question
''What is the (simulated) performance of ns-3's WiFi NetDevices (using the default settings)? How far
apart can wireless nodes be in a simulation before they cannot communicate reliably?''
• Hypothesis: Based on knowledge of real life performance, the nodes should communicate reasonably well
to at least 100m apart. Communication beyond 200m shouldn't be feasible.
Although not a very common question in simulation contexts, this is an important property of which
simulation developers should have a basic understanding. It is also a common study done on live
hardware.
Simulation Program
The first thing to do in implementing this experiment is developing the simulation program. The code for
this example can be found in examples/stats/wifi-example-sim.cc. It does the following main steps.
• Declaring parameters and parsing the command line using ns3::CommandLine.
CommandLine cmd;
cmd.AddValue("distance", "Distance apart to place nodes (in meters).",
distance);
cmd.AddValue("format", "Format to use for data output.",
format);
cmd.AddValue("experiment", "Identifier for experiment.",
experiment);
cmd.AddValue("strategy", "Identifier for strategy.",
strategy);
cmd.AddValue("run", "Identifier for run.",
runID);
cmd.Parse (argc, argv);
• Creating nodes and network stacks using ns3::NodeContainer, ns3::WiFiHelper, and
ns3::InternetStackHelper.
NodeContainer nodes;
nodes.Create(2);
WifiHelper wifi;
wifi.SetMac("ns3::AdhocWifiMac");
wifi.SetPhy("ns3::WifiPhy");
NetDeviceContainer nodeDevices = wifi.Install(nodes);
InternetStackHelper internet;
internet.Install(nodes);
Ipv4AddressHelper ipAddrs;
ipAddrs.SetBase("192.168.0.0", "255.255.255.0");
ipAddrs.Assign(nodeDevices);
• Positioning the nodes using ns3::MobilityHelper. By default the nodes have static mobility and won't
move, but must be positioned the given distance apart. There are several ways to do this; it is done
here using ns3::ListPositionAllocator, which draws positions from a given list.
MobilityHelper mobility;
Ptr<ListPositionAllocator> positionAlloc =
CreateObject<ListPositionAllocator>();
positionAlloc->Add(Vector(0.0, 0.0, 0.0));
positionAlloc->Add(Vector(0.0, distance, 0.0));
mobility.SetPositionAllocator(positionAlloc);
mobility.Install(nodes);
• Installing a traffic generator and a traffic sink. The stock Applications could be used, but the
example includes custom objects in src/test/test02-apps.(cc|h). These have a simple behavior,
generating a given number of packets spaced at a given interval. As there is only one of each they are
installed manually; for a larger set the ns3::ApplicationHelper class could be used. The commented-out
Config::Set line changes the destination of the packets, set to broadcast by default in this example.
Note that in general WiFi may have different performance for broadcast and unicast frames due to
different rate control and MAC retransmission policies.
Ptr<Node> appSource = NodeList::GetNode(0);
Ptr<Sender> sender = CreateObject<Sender>();
appSource->AddApplication(sender);
sender->Start(Seconds(1));
Ptr<Node> appSink = NodeList::GetNode(1);
Ptr<Receiver> receiver = CreateObject<Receiver>();
appSink->AddApplication(receiver);
receiver->Start(Seconds(0));
// Config::Set("/NodeList/*/ApplicationList/*/$Sender/Destination",
// Ipv4AddressValue("192.168.0.2"));
• Configuring the data and statistics to be collected. The basic paradigm is that an ns3::DataCollector
object is created to hold information about this particular run, to which observers and calculators are
attached to actually generate data. Importantly, run information includes labels for the
''experiment'', ''strategy'', ''input'', and ''run''. These are used to later identify and easily
group data from multiple trials.
• The experiment is the study of which this trial is a member. Here it is on WiFi performance and
distance.
• The strategy is the code or parameters being examined in this trial. In this example it is fixed,
but an obvious extension would be to investigate different WiFi bit rates, each of which would be a
different strategy.
• The input is the particular problem given to this trial. Here it is simply the distance between the
two nodes.
• The runID is a unique identifier for this trial with which it's information is tagged for
identification in later analysis. If no run ID is given the example program makes a (weak) run ID
using the current time.
Those four pieces of metadata are required, but more may be desired. They may be added to the record
using the ns3::DataCollector::AddMetadata() method.
DataCollector data;
data.DescribeRun(experiment,
strategy,
input,
runID);
data.AddMetadata("author", "tjkopena");
Actual observation and calculating is done by ns3::DataCalculator objects, of which several different
types exist. These are created by the simulation program, attached to reporting or sampling code, and
then registered with the ns3::DataCollector so they will be queried later for their output. One easy
observation mechanism is to use existing trace sources, for example to instrument objects in the ns-3
core without changing their code. Here a counter is attached directly to a trace signal in the WiFi
MAC layer on the target node.
Ptr<PacketCounterCalculator> totalRx =
CreateObject<PacketCounterCalculator>();
totalRx->SetKey("wifi-rx-frames");
Config::Connect("/NodeList/1/DeviceList/*/$ns3::WifiNetDevice/Rx",
MakeCallback(&PacketCounterCalculator::FrameUpdate,
totalRx));
data.AddDataCalculator(totalRx);
Calculators may also be manipulated directly. In this example, a counter is created and passed to the
traffic sink application to be updated when packets are received.
Ptr<CounterCalculator<> > appRx =
CreateObject<CounterCalculator<> >();
appRx->SetKey("receiver-rx-packets");
receiver->SetCounter(appRx);
data.AddDataCalculator(appRx);
To increment the count, the sink's packet processing code then calls one of the calculator's update
methods.
m_calc->Update();
The program includes several other examples as well, using both the primitive calculators such as
ns3::CounterCalculator and those adapted for observing packets and times. In
src/test/test02-apps.(cc|h) it also creates a simple custom tag which it uses to track end-to-end delay
for generated packets, reporting results to a ns3::TimeMinMaxAvgTotalCalculator data calculator.
• Running the simulation, which is very straightforward once constructed.
Simulator::Run();
• Generating either omnetpp or sqlite output, depending on the command line arguments. To do this a
ns3::DataOutputInterface object is created and configured. The specific type of this will determine
the output format. This object is then given the ns3::DataCollector object which it interrogates to
produce the output.
Ptr<DataOutputInterface> output;
if (format == "omnet") {
NS_LOG_INFO("Creating omnet formatted data output.");
output = CreateObject<OmnetDataOutput>();
} else {
#ifdef STAT_USE_DB
NS_LOG_INFO("Creating sqlite formatted data output.");
output = CreateObject<SqliteDataOutput>();
#endif
}
output->Output(data);
• Freeing any memory used by the simulation. This should come at the end of the main function for the
example.
Simulator::Destroy();
Logging
To see what the example program, applications, and stat framework are doing in detail, set the NS_LOG
variable appropriately. The following will provide copious output from all three.
export NS_LOG=StatFramework:WiFiDistanceExperiment:WiFiDistanceApps
Note that this slows down the simulation extraordinarily.
Sample Output
Compiling and simply running the test program will append omnet++ formatted output such as the following
to data.sca.
run run-1212239121
attr experiment "wifi-distance-test"
attr strategy "wifi-default"
attr input "50"
attr description ""
attr "author" "tjkopena"
scalar wifi-tx-frames count 30
scalar wifi-rx-frames count 30
scalar sender-tx-packets count 30
scalar receiver-rx-packets count 30
scalar tx-pkt-size count 30
scalar tx-pkt-size total 1920
scalar tx-pkt-size average 64
scalar tx-pkt-size max 64
scalar tx-pkt-size min 64
scalar delay count 30
scalar delay total 5884980ns
scalar delay average 196166ns
scalar delay max 196166ns
scalar delay min 196166ns
Control Script
In order to automate data collection at a variety of inputs (distances), a simple Bash script is used to
execute a series of simulations. It can be found at examples/stats/wifi-example-db.sh. The script runs
through a set of distances, collecting the results into an sqlite3 database. At each distance five
trials are conducted to give a better picture of expected performance. The entire experiment takes only
a few dozen seconds to run on a low end machine as there is no output during the simulation and little
traffic is generated.
#!/bin/sh
DISTANCES="25 50 75 100 125 145 147 150 152 155 157 160 162 165 167 170 172 175 177 180"
TRIALS="1 2 3 4 5"
echo WiFi Experiment Example
if [ -e data.db ]
then
echo Kill data.db?
read ANS
if [ "$ANS" = "yes" -o "$ANS" = "y" ]
then
echo Deleting database
rm data.db
fi
fi
for trial in $TRIALS
do
for distance in $DISTANCES
do
echo Trial $trial, distance $distance
./bin/test02 --format=db --distance=$distance --run=run-$distance-$trial
done
done
Analysis and Conclusion
Once all trials have been conducted, the script executes a simple SQL query over the database using the
sqlite3 command line program. The query computes average packet loss in each set of trials associated
with each distance. It does not take into account different strategies, but the information is present
in the database to make some simple extensions and do so. The collected data is then passed to GNUPlot
for graphing.
CMD="select exp.input,avg(100-((rx.value*100)/tx.value)) \
from Singletons rx, Singletons tx, Experiments exp \
where rx.run = tx.run AND \
rx.run = exp.run AND \
rx.name='receiver-rx-packets' AND \
tx.name='sender-tx-packets' \
group by exp.input \
order by abs(exp.input) ASC;"
sqlite3 -noheader data.db "$CMD" > wifi-default.data
sed -i "s/|/ /" wifi-default.data
gnuplot wifi-example.gnuplot
The GNUPlot script found at examples/stats/wifi-example.gnuplot simply defines the output format and some
basic formatting for the graph.
set terminal postscript portrait enhanced lw 2 "Helvetica" 14
set size 1.0, 0.66
#-------------------------------------------------------
set out "wifi-default.eps"
#set title "Packet Loss Over Distance"
set xlabel "Distance (m) --- average of 5 trials per point"
set xrange [0:200]
set ylabel "% Packet Loss"
set yrange [0:110]
plot "wifi-default.data" with lines title "WiFi Defaults"
End Result
The resulting graph provides no evidence that the default WiFi model's performance is necessarily
unreasonable and lends some confidence to an at least token faithfulness to reality. More importantly,
this simple investigation has been carried all the way through using the statistical framework. Success!
[image]
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/
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 [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 auvmobilitymodel.
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 consumprion 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 μmodem. The class follows pretty
closely the RadioEnergyModel class as the transducer behaviour is pretty close to that of a wifi 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 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 consuption 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].
WIFI
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 and 802.11g physical layers
• QoS-based EDCA and queueing extensions of 802.11e
• various propagation loss models including Nakagami, Rayleigh, Friis, LogDistance, FixedRss, Random
• two propagation delay models, a distance-based and random model
• various rate control algorithms including Aarf, Arf, Cara, Onoe, Rraa, ConstantRate, and Minstrel
• 802.11s (mesh), 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 not-so-slow PHY-level model of the 802.11a specification.
The implementation is modular and provides roughly four levels of models:
• the PHY layer models
• the so-called MAC low models: they implement DCF and EDCAF
• the so-called MAC high models: they implement the MAC-level beacon generation, probing, and association
state machines, and
• a set of Rate control algorithms used by the MAC low 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) (implemented in class 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. 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. In order for the MAC to determine the appropriate AC for an MSDU, packets forwarded
down to these MAC layers should be marked using ns3::QosTag in order to set a TID (traffic id) for that
packet otherwise it will be considered belonging to AC_BE.
The MAC low layer is split into three components:
1. ns3::MacLow which takes care of RTS/CTS/DATA/ACK transactions.
2. ns3::DcfManager and ns3::DcfState which implements the DCF and EDCAF functions.
3. ns3::DcaTxop and ns3::EdcaTxopN which handle the packet queue, packet fragmentation, and packet
retransmissions if they are needed. The ns3::DcaTxop object is used 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::EdcaTxopN is is used by QoS-enabled high MACs and also performs
QoS operations like 802.11n-style MSDU aggregation.
There are also several rate control algorithms that can be used by the Mac low layer:
• OnoeWifiManager
• IdealWifiManager
• AarfcdWifiManager
• AarfWifiManager
• ArfWifiManager
• AmrrWifiManager
• ConstantRateWifiManager
• MinstrelWifiManager
• CaraWifiManager
• RraaWifiManager
The PHY layer implements a single model in the ns3::WifiPhy class: the physical layer model implemented
there is described fully in a paper entitled Yet Another Network Simulator Validation results for 802.11b
are available in this technical report
In ns-3, nodes can have multiple WifiNetDevices on separate channels, and the WifiNetDevice can coexist
with other device types; this removes an architectural limitation found in ns-2. Presently, however,
there is no model for cross-channel interference or coupling.
The source code for the Wifi NetDevice lives in the directory src/wifi.
[image] Wifi NetDevice architecture..UNINDENT
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 parametrization 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 WifiChannel, 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. The scripts in src/examples can be browsed to see how this is
done.
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 WifiChannel
with a default PropagationLoss and PropagationDelay model. Specifically, the default is a channel model
with a propagation delay equal to a constant, the speed of light, and a propagation loss based on a log
distance model with a reference loss of 46.6777 dB at reference distance of 1m.
Users will typically type code such as::
YansWifiChannelHelper wifiChannelHelper = YansWifiChannelHelper::Default ();
Ptr<WifiChannel> wifiChannel = wifiChannelHelper.Create ();
to get the defaults. 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.
Todo: Add notes about how to configure attributes with this helper API
YansWifiPhyHelper
Physical devices (base class ns3::Phy) connect to ns3::Channel models in ns-3. We need to create Phy
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 = YansWifiPhyHelper::Default ();
wifiPhyHelper.SetChannel (wifiChannel);
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.
NqosWifiMacHelper and QosWifiMacHelper
The ns3::NqosWifiMacHelper and ns3::QosWifiMacHelper configure an object factory to create instances of a
ns3::WifiMac. They are used to configure MAC parameters like type of MAC.
The former, ns3::NqosWifiMacHelper, supports creation of MAC instances that do not have 802.11e/WMM-style
QoS support enabled.
For example the following user code configures a non-QoS MAC that will be a non-AP STA in an
infrastructure network where the AP has SSID ns-3-ssid::
NqosWifiMacHelper wifiMacHelper = NqosWifiMacHelper::Default ();
Ssid ssid = Ssid ("ns-3-ssid");
wifiMacHelper.SetType ("ns3::StaWifiMac",
"Ssid", SsidValue (ssid),
"ActiveProbing", BooleanValue (false));
To create MAC instances with QoS support enabled, ns3::QosWifiMacHelper is used in place of
ns3::NqosWifiMacHelper. This object can be also used to set:
• a MSDU aggregator for a particular Access Category (AC) in order to use 802.11n MSDU aggregation
feature;
• block ack parameters like threshold (number of packets for which block ack mechanism should be used)
and inactivity timeout.
The following code shows an example use of ns3::QosWifiMacHelper to create an AP with QoS enabled,
aggregation on AC_VO, and Block Ack on AC_BE::
QosWifiMacHelper wifiMacHelper = QosWifiMacHelper::Default ();
wifiMacHelper.SetType ("ns3::ApWifiMac",
"Ssid", SsidValue (ssid),
"BeaconGeneration", BooleanValue (true),
"BeaconInterval", TimeValue (Seconds (2.5)));
wifiMacHelper.SetMsduAggregatorForAc (AC_VO, "ns3::MsduStandardAggregator",
"MaxAmsduSize", UintegerValue (3839));
wifiMacHelper.SetBlockAckThresholdForAc (AC_BE, 10);
wifiMacHelper.SetBlockAckInactivityTimeoutForAc (AC_BE, 5);
WifiHelper
We're now ready to create WifiNetDevices. First, let's create a WifiHelper with default settings::
WifiHelper wifiHelper = WifiHelper::Default ();
What does this do? It sets the RemoteStationManager to ns3::ArfWifiManager. 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 WifiChannel).
There are many 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.
AdHoc WifiNetDevice configuration
This is a typical example of how a user might configure an adhoc network.
To be completed
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.
To be completed
The WifiChannel and WifiPhy models
The WifiChannel subclass can be used to connect together a set of ns3::WifiNetDevice network interfaces.
The class ns3::WifiPhy is the object within the WifiNetDevice that receives bits from the channel. For
the channel propagation modeling, the propagation module is used; see section Propagation for details.
This section summarizes the description of the BER calculations found in the yans paper taking into
account the Forward Error Correction present in 802.11a and describes the algorithm we implemented to
decide whether or not a packet can be successfully received. See "Yet Another Network Simulator" for more
details.
The PHY layer can be in one of three 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 or RX states.
When the first bit of a new packet is received while the PHY is not IDLE (that is, it is already
synchronized on the reception of another earlier packet or it is sending data itself), the received
packet is dropped. Otherwise, if the PHY is IDLE, we calculate the received energy of the first bit of
this new signal and compare it against our Energy Detection threshold (as defined by the Clear Channel
Assessment function mode 1). If the energy of the packet k is higher, then the PHY moves to RX state and
schedules an event when the last bit of the packet is expected to be received. Otherwise, the PHY stays
in IDLE state and drops the packet.
The energy of the received signal is assumed to be zero outside of the reception interval of packet k and
is calculated from the transmission power with a path-loss propagation model in the reception interval.
where the path loss exponent, n, is chosen equal to 3, the reference distance, d_0 is choosen equal to
1.0m and the reference energy is based based on a Friis propagation model.
When the last bit of the packet upon which the PHY is synchronized is received, we need to calculate the
probability that the packet is received with any error to decide whether or not the packet on which we
were synchronized could be successfully received or not: a random number is drawn from a uniform
distribution and is compared against the probability of error.
To evaluate the probability of error, we start from the piecewise linear functions shown in Figure snir
and calculate the SNIR function.
[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. Please refer to [pei80211ofdm],
[pei80211b] and [lacage2006yans] for a detailed description of the available BER/PER models.
WifiChannel configuration
The WifiChannel implementation uses the propagation loss and delay models provided within the ns-3
propagation module.
The MAC model
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 backoff procedure of DCF is described in section 9.2.5.2 of [ieee80211].
• “The backoff procedure shall be invoked for a STA 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.”
Thus, if the queue is empty, a newly arrived packet should be transmitted immediately after channel is
sensed idle for DIFS. If queue is not empty and after a successful MPDU that has no more fragments, a
node should also start the backoff timer.
Some users have observed that the 802.11 MAC with an empty queue on an idle channel will transmit the
first frame arriving to the model immediately without waiting for DIFS or backoff, and wonder whether
this is compliant. According to the standard, “The backoff procedure shall be invoked for a STA to
transfer a frame when finding the medium busy as indicated by either the physical or virtual CS
mechanism.” So in this case, the medium is not found to be busy in recent past and the station can
transmit immediately.
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.
Wifi Attributes
Should link to the list of attributes exported by Doxygen
Wifi Tracing
Should link to the list of traces exported by Doxygen
References
[ieee80211]
IEEE Std 802.11-2007 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
[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.
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 splitted 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
2011, ns-3 project
ns-3.17 December 15, 2013 NS-3-MODEL-LIBRARY(1)