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GettingStarted.html
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<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"><meta name="generator" content="MATLAB R2018a"><meta http-equiv="X-UA-Compatible" content="IE=edge,IE=9,chrome=1"><title>Getting Started with MATLAB-NS3 Cosimulation</title><style type="text/css">
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</style></head><body><div class = "content"><div class = 'SectionBlock containment active'><h1 class = "S1"><span class = "S2"><span class="S0">Getting Started with MATLAB-NS3 Cosimulation</span></span></h1><h2 class = "S3"><span class = "S2"><span class="S0">I</span></span><span class = "S2"><span class="S0">ntroduction</span></span></h2><div class = "S4"><span class = "S2"><span class="S0">This document describes the underlying architecture used for realizing co-simulation, combining the powers of MATLAB and NS-3.</span></span></div><div class = "S4"><span class = "S2"><span class="S0"></span></span></div><h2 class = "S3"><span class = "S2"><span class="S0">Why co-simulate with NS3</span></span></h2><div class = "S4"><span class = "S2"><span class="S0">NS-3 is a discrete event network simulator and enjoys huge user base in research community. It has well defined implementation of major network protocol stacks for system-level simulation. The aim of this co-simulation is to leverage the protocol stacks of NS-3 from MATLAB and make it easy for users to simulate complex network scenarios.</span></span></div><div class = "S4"><span class = "S2"><span class="S0"></span></span></div><h2 class = "S3"><span class = "S2"><span class="S0">Benefits for the MATLAB user:</span></span></h2><ol class = "S5"><li class = "S6"><span class = "S0"><span class="S0">Do system level modeling of communication system with multiple nodes, without leaving MATLAB.</span></span></li><li class = "S6"><span class = "S0"><span class="S0">Create simulation scenarios and visualize results in MATLAB.</span></span></li><li class = "S6"><span class = "S0"><span class="S0">Allows MATLAB user not accustomed to C++, to utilize the power of NS-3.</span></span></li><li class = "S6"><span class = "S0"><span class="S0">Use MATLAB PHY & Channel models, where high-fidelity results are desired.</span></span></li><li class = "S6"><span class = "S0"><span class="S0">Integrate with MATLAB components that users have already developed.</span></span></li><li class = "S6"><span class = "S0"><span class="S0">Co-simulate with other MATLAB toolboxes.</span></span></li></ol><div class = "S4"><span class = "S2"><span class="S0"></span></span></div><h2 class = "S3"><span class = "S2"><span class="S0">High-level architecture</span></span></h2><div class = "S4"><span class = "S2"><span class="S0">The following figure depicts the architecture of MATLAB and NS-3 Co-Simulation. The simulation scenario is written in MATLAB while the simulation primarily runs in NS-3. Mex-bindings act as the interface between MATLAB and NS-3. The MATLAB wrapper classes provide an abstraction layer over these Mex bindings.</span></span></div><div class = "S4"><img class="imageNode" width="775" height="353" style="vertical-align: baseline" 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alt="image1.png"></div><div class = "S4"><span class = "S2"><span class="S0"></span></span></div><div class = "S4"><span class = "S2"><span class="S0">The simulation scenario in MATLAB consists of scenario configuration and a sequence of function calls. A function call could be internal to MATLAB, or a call into the NS-3 library via MATLAB wrappers and Mex-bindings. Each pair of Mex-binding file and MATLAB wrapper class correspond to an NS-3 C++ class.</span></span></div><div class = "S4"><span class = "S2"><span class="S0">Below diagram shows the way a sample function residing in the NS-3 library is invoked from MATLAB. The required type conversions are managed in Mex bindings.</span></span></div><div class = "S4"><img class="imageNode" style="vertical-align: baseline" src="data:image/png;base64,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"></div><div class = "S4"><span class = "S2"><span class="S0"></span></span></div><div class = "S4"><span class = "S2"><span class="S0">A MATLAB script calls into the NS-3 library by calling corresponding method in the MATLAB wrapper class, which in turns calls the corresponding Mex function. Finally, Mex function calls into the NS-3 library.</span></span></div><div class = "S4"><span class = "S2"><span class="S0"></span></span></div><h2 class = "S3"><span class = "S2"><span class="S0">MATLAB PHY and Channel model invocation from NS-3</span></span></h2><div class = "S4"><span class = "S2"><span class="S0">The Co-Simulation can optionally use MATLAB's Phy and Channel models, instead of NS-3's statistical models, for higher fidelity. To use MATLAB's models, a callback mechanism is used through which NS-3 protocol stack calls into MATLAB PHY implementation via Mex Interface. </span></span></div><div class = "S4"><img class="imageNode" style="vertical-align: baseline" 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"></div><div class = "S4"><span class = "S2"><span class="S0">The following files (along with corresponding header files) are added to the NS3 library, to invoke MATLAB PHY and Channel models for modeling PHY and Channel:</span></span></div><ol class = "S5"><li class = "S6"><span class = "S0"><span class="S0">matlab-wifi-helper.cc (class MatlabWifiPhyHelper, class MatlabWifiChannelHelper)</span></span></li><li class = "S6"><span class = "S0"><span class="S0">matlab-wifi-phy.cc (class MatlabWifiPhy)</span></span></li><li class = "S6"><span class = "S0"><span class="S0">matlab-wifi-channel.cc (class MatlabWifiChannel)</span></span></li></ol><div class = "S4"><span class = "S2"><span class="S0">User shall register a custom MATLAB callback function through the MatlabWifiChannelHelper, a MATLAB wrapper class. In the callback function, user typically defines the PHY tx-rx chain (containing modeling of PHY Tx, Channel and PHY Rx for the packet). </span></span></div><div class = "S4"><span class = "S2"><span class="S0">Whenever a packet from higher layers reaches the PHY & Channel models in NS3, the custom model added to NS3 will pass the packet to the callback in Mex. The Mex layer passes the packet to the registered MATLAB callback of user, through 'mexCallMATLAB'. The callback is passed the required parameters along with the packet, to model the PHY and Channel in MATLAB.</span></span></div><div class = "S4"><img class="imageNode" style="vertical-align: baseline" 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"></div><div class = "S4"><span class = "S2"><span class="S0"></span></span></div><div class = "S4"><span class = "S2"><span class="S0">Currently the co-simulation with NS-3 is </span></span><span class = "S2"><span class="S7">limited to Linux platform</span></span><span class = "S2"><span class="S0">. </span></span></div><div class = "S4"><span class = "S2"><span class="S0"></span></span></div><h2 class = "S3"><span class = "S2"><span class="S0">Typical steps in creating a simulation scenario in MATLAB</span></span></h2><div class = "S4"><span class = "S2"><span class="S0">Here are the steps typically performed in creating and running a simulation scenario.</span></span></div><div class = "S4"><span class = "S2"><span class="S0"> </span></span><img class="imageNode" style="vertical-align: baseline" src="data:image/png;base64,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"></div><div class = "S4"><span class = "S2"><span class="S0">The scenario description starts by defining the configurable parameters, Number of nodes, network stack layers (including PHY & Channel), application data traffic properties, mobility parameters, etc.</span></span></div><div class = "S4"><span class = "S2"><span class="S0">Setting up the simulation scenario involves: creating the nodes, binding the channel/phy/protocol stack on the nodes, configuring the position & mobility models for nodes, and installing applications on the nodes. For visualization, logging and tracing are enabled on desirable events. The traces (generated during the runtime) are visualized in MATLAB, offline.</span></span></div><div class = "S4"><span class = "S2"><span class="S0">Once simulation set-up is complete, simulation starts. The simulation runs in MATLAB process context, through Mex call. The simulation primarily runs in NS-3. During simulation, MATLAB can get triggers through callbacks registered with NS-3 (timers, application data reception, trace notifications, and node mobility triggers). When the MATLAB gets trigger from NS-3, it can perform run-time actions such as: creating application data packet, parsing received application data and acting on it, changing the nodes mobility, etc. This run-time interaction between MATLAB and NS-3 happens through Mex interface.</span></span></div><div class = "S4"><span class = "S2"><span class="S0">MATLAB can collect the statistics while the simulation is running. The statistics can be analyzed and visualized in MATLAB.</span></span></div><div class = "S4"><span class = "S2"><span class="S0"></span></span></div></div></div><br><!-- <br>##### SOURCE BEGIN #####<br>%% Getting Started with MATLAB-NS3 Cosimulation<br>%% I*ntroduction*<br>% This document describes the underlying architecture used for realizing co-simulation, <br>% combining the powers of MATLAB and NS-3.<br>% <br>% <br>%% *Why co-simulate with NS3*<br>% NS-3 is a discrete event network simulator and enjoys huge user base in research <br>% community. It has well defined implementation of major network protocol stacks <br>% for system-level simulation. The aim of this co-simulation is to leverage the <br>% protocol stacks of NS-3 from MATLAB and make it easy for users to simulate complex <br>% network scenarios.<br>% <br>% <br>%% *Benefits for the MATLAB user:*<br>% # Do system level modeling of communication system with multiple nodes, without <br>% leaving MATLAB.<br>% # Create simulation scenarios and visualize results in MATLAB.<br>% # Allows MATLAB user not accustomed to C++, to utilize the power of NS-3.<br>% # Use MATLAB PHY & Channel models, where high-fidelity results are desired.<br>% # Integrate with MATLAB components that users have already developed.<br>% # Co-simulate with other MATLAB toolboxes.<br>% <br>% <br>%% *High-level architecture*<br>% The following figure depicts the architecture of MATLAB and NS-3 Co-Simulation. <br>% The simulation scenario is written in MATLAB while the simulation primarily <br>% runs in NS-3. Mex-bindings act as the interface between MATLAB and NS-3. The <br>% MATLAB wrapper classes provide an abstraction layer over these Mex bindings.<br>% <br>% <br>% <br>% <br>% <br>% The simulation scenario in MATLAB consists of scenario configuration and <br>% a sequence of function calls. A function call could be internal to MATLAB, or <br>% a call into the NS-3 library via MATLAB wrappers and Mex-bindings. Each pair <br>% of Mex-binding file and MATLAB wrapper class correspond to an NS-3 C++ class.<br>% <br>% Below diagram shows the way a sample function residing in the NS-3 library <br>% is invoked from MATLAB. The required type conversions are managed in Mex bindings.<br>% <br>% <br>% <br>% <br>% <br>% A MATLAB script calls into the NS-3 library by calling corresponding method <br>% in the MATLAB wrapper class, which in turns calls the corresponding Mex function. <br>% Finally, Mex function calls into the NS-3 library.<br>% <br>% <br>%% *MATLAB PHY and Channel model invocation from NS-3*<br>% The Co-Simulation can optionally use MATLAB's Phy and Channel models, instead <br>% of NS-3's statistical models, for higher fidelity. To use MATLAB's models, a <br>% callback mechanism is used through which NS-3 protocol stack calls into MATLAB <br>% PHY implementation via Mex Interface. <br>% <br>% <br>% <br>% The following files (along with corresponding header files) are added to <br>% the NS3 library, to invoke MATLAB PHY and Channel models for modeling PHY and <br>% Channel:<br>% <br>% # matlab-wifi-helper.cc (class MatlabWifiPhyHelper, class MatlabWifiChannelHelper)<br>% # matlab-wifi-phy.cc (class MatlabWifiPhy)<br>% # matlab-wifi-channel.cc (class MatlabWifiChannel)<br>% <br>% User shall register a custom MATLAB callback function through the MatlabWifiChannelHelper, <br>% a MATLAB wrapper class. In the callback function, user typically defines the <br>% PHY tx-rx chain (containing modeling of PHY Tx, Channel and PHY Rx for the packet). <br>% <br>% Whenever a packet from higher layers reaches the PHY & Channel models in <br>% NS3, the custom model added to NS3 will pass the packet to the callback in Mex. <br>% The Mex layer passes the packet to the registered MATLAB callback of user, through <br>% 'mexCallMATLAB'. The callback is passed the required parameters along with the <br>% packet, to model the PHY and Channel in MATLAB.<br>% <br>% <br>% <br>% <br>% <br>% Currently the co-simulation with NS-3 is limited to Linux platform. <br>% <br>% <br>%% *Typical steps in creating a simulation scenario in MATLAB*<br>% Here are the steps typically performed in creating and running a simulation <br>% scenario.<br>% <br>% <br>% <br>% The scenario description starts by defining the configurable parameters, <br>% Number of nodes, network stack layers (including PHY & Channel), application <br>% data traffic properties, mobility parameters, etc.<br>% <br>% Setting up the simulation scenario involves: creating the nodes, binding <br>% the channel/phy/protocol stack on the nodes, configuring the position & mobility <br>% models for nodes, and installing applications on the nodes. For visualization, <br>% logging and tracing are enabled on desirable events. The traces (generated during <br>% the runtime) are visualized in MATLAB, offline.<br>% <br>% Once simulation set-up is complete, simulation starts. The simulation runs <br>% in MATLAB process context, through Mex call. The simulation primarily runs in <br>% NS-3. During simulation, MATLAB can get triggers through callbacks registered <br>% with NS-3 (timers, application data reception, trace notifications, and node <br>% mobility triggers). When the MATLAB gets trigger from NS-3, it can perform run-time <br>% actions such as: creating application data packet, parsing received application <br>% data and acting on it, changing the nodes mobility, etc. This run-time interaction <br>% between MATLAB and NS-3 happens through Mex interface.<br>% <br>% MATLAB can collect the statistics while the simulation is running. The <br>% statistics can be analyzed and visualized in MATLAB.<br>##### SOURCE END #####<br>--></body></html>