190 lines
6.4 KiB
C++
190 lines
6.4 KiB
C++
/*
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* Copyright (c) 2020 University of Washington
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*
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* This program is free software; you can redistribute it and/or modify
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* it under the terms of the GNU General Public License version 2 as
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* published by the Free Software Foundation;
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program; if not, write to the Free Software
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* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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*
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* Authors: Sébastien Deronne <sebastien.deronne@gmail.com>
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* Rohan Patidar <rpatidar@uw.edu>
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*/
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// This example is to show difference among Nist, Yans and Table-based error rate models.
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//
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// It outputs plots of the Frame Error Rate versus the Signal-to-noise ratio for
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// Nist, Yans and Table-based error rate models and for MCS 0, 4 and 7 value.
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#include "ns3/command-line.h"
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#include "ns3/gnuplot.h"
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#include "ns3/nist-error-rate-model.h"
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#include "ns3/table-based-error-rate-model.h"
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#include "ns3/wifi-tx-vector.h"
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#include "ns3/yans-error-rate-model.h"
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#include <cmath>
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#include <fstream>
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using namespace ns3;
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int
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main(int argc, char* argv[])
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{
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uint32_t size = 1500 * 8; // bits
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bool tableErrorModelEnabled = true;
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bool yansErrorModelEnabled = true;
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bool nistErrorModelEnabled = true;
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uint8_t beginMcs = 0;
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uint8_t endMcs = 7;
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uint8_t stepMcs = 4;
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std::string format("Ht");
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CommandLine cmd(__FILE__);
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cmd.AddValue("size", "The size in bits", size);
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cmd.AddValue("frameFormat", "The frame format to use: Ht, Vht or He", format);
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cmd.AddValue("beginMcs", "The first MCS to test", beginMcs);
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cmd.AddValue("endMcs", "The last MCS to test", endMcs);
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cmd.AddValue("stepMcs", "The step between two MCSs to test", stepMcs);
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cmd.AddValue("includeTableErrorModel",
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"Flag to include/exclude Table-based error model",
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tableErrorModelEnabled);
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cmd.AddValue("includeYansErrorModel",
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"Flag to include/exclude Yans error model",
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yansErrorModelEnabled);
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cmd.AddValue("includeNistErrorModel",
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"Flag to include/exclude Nist error model",
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nistErrorModelEnabled);
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cmd.Parse(argc, argv);
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std::ofstream errormodelfile("wifi-error-rate-models.plt");
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Gnuplot plot = Gnuplot("wifi-error-rate-models.eps");
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Ptr<YansErrorRateModel> yans = CreateObject<YansErrorRateModel>();
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Ptr<NistErrorRateModel> nist = CreateObject<NistErrorRateModel>();
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Ptr<TableBasedErrorRateModel> table = CreateObject<TableBasedErrorRateModel>();
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WifiTxVector txVector;
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std::vector<std::string> modes;
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std::stringstream mode;
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mode << format << "Mcs" << +beginMcs;
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modes.push_back(mode.str());
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for (uint8_t mcs = (beginMcs + stepMcs); mcs < endMcs; mcs += stepMcs)
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{
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mode.str("");
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mode << format << "Mcs" << +mcs;
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modes.push_back(mode.str());
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}
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mode.str("");
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mode << format << "Mcs" << +endMcs;
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modes.push_back(mode.str());
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for (uint32_t i = 0; i < modes.size(); i++)
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{
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std::cout << modes[i] << std::endl;
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Gnuplot2dDataset yansdataset(modes[i]);
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Gnuplot2dDataset nistdataset(modes[i]);
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Gnuplot2dDataset tabledataset(modes[i]);
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txVector.SetMode(modes[i]);
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for (double snr = -5.0; snr <= (endMcs * 5); snr += 0.1)
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{
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double ps = yans->GetChunkSuccessRate(WifiMode(modes[i]),
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txVector,
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std::pow(10.0, snr / 10.0),
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size);
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if (ps < 0 || ps > 1)
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{
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// error
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exit(1);
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}
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yansdataset.Add(snr, 1 - ps);
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ps = nist->GetChunkSuccessRate(WifiMode(modes[i]),
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txVector,
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std::pow(10.0, snr / 10.0),
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size);
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if (ps < 0 || ps > 1)
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{
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// error
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exit(1);
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}
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nistdataset.Add(snr, 1 - ps);
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ps = table->GetChunkSuccessRate(WifiMode(modes[i]),
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txVector,
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std::pow(10.0, snr / 10.0),
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size);
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if (ps < 0 || ps > 1)
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{
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// error
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exit(1);
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}
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tabledataset.Add(snr, 1 - ps);
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}
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if (tableErrorModelEnabled)
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{
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std::stringstream ss;
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ss << "Table-" << modes[i];
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tabledataset.SetTitle(ss.str());
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plot.AddDataset(tabledataset);
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}
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if (yansErrorModelEnabled)
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{
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std::stringstream ss;
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ss << "Yans-" << modes[i];
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yansdataset.SetTitle(ss.str());
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plot.AddDataset(yansdataset);
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}
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if (nistErrorModelEnabled)
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{
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std::stringstream ss;
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ss << "Nist-" << modes[i];
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nistdataset.SetTitle(ss.str());
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plot.AddDataset(nistdataset);
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}
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}
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plot.SetTerminal("postscript eps color enh \"Times-BoldItalic\"");
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plot.SetLegend("SNR(dB)", "Frame Error Rate");
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std::stringstream plotExtra;
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plotExtra << "set xrange [-5:" << endMcs * 5 << "]\n\
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set log y\n\
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set yrange [0.0001:1]\n";
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uint8_t lineNumber = 1;
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for (uint32_t i = 0; i < modes.size(); i++)
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{
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if (tableErrorModelEnabled)
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{
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plotExtra << "set style line " << +lineNumber++
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<< " linewidth 5 linecolor rgb \"red\" \n";
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}
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if (yansErrorModelEnabled)
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{
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plotExtra << "set style line " << +lineNumber++
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<< " linewidth 5 linecolor rgb \"green\" \n";
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}
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if (nistErrorModelEnabled)
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{
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plotExtra << "set style line " << +lineNumber++
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<< " linewidth 5 linecolor rgb \"blue\" \n";
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}
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}
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plotExtra << "set style increment user";
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plot.SetExtra(plotExtra.str());
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plot.GenerateOutput(errormodelfile);
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errormodelfile.close();
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return 0;
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}
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