/* * Copyright (c) 2021 DERONNE SOFTWARE ENGINEERING * * SPDX-License-Identifier: GPL-2.0-only * * Author: Sébastien Deronne */ // This example is used to validate NIST and YANS error rate models for EHT rates. // // It outputs plots of the Frame Success Rate versus the Signal-to-noise ratio for // Nist, Yans and Table-based error rate models and for every HT MCS value. #include "ns3/command-line.h" #include "ns3/gnuplot.h" #include "ns3/nist-error-rate-model.h" #include "ns3/table-based-error-rate-model.h" #include "ns3/wifi-tx-vector.h" #include "ns3/yans-error-rate-model.h" #include #include using namespace ns3; int main(int argc, char* argv[]) { uint32_t frameSizeBytes = 1500; std::ofstream yansfile("yans-frame-success-rate-be.plt"); std::ofstream nistfile("nist-frame-success-rate-be.plt"); std::ofstream tablefile("table-frame-success-rate-be.plt"); const std::vector modes{ "EhtMcs0", "EhtMcs1", "EhtMcs2", "EhtMcs3", "EhtMcs4", "EhtMcs5", "EhtMcs6", "EhtMcs7", "EhtMcs8", "EhtMcs9", "EhtMcs10", "EhtMcs11", "EhtMcs12", "EhtMcs13", }; CommandLine cmd(__FILE__); cmd.AddValue("FrameSize", "The frame size", frameSizeBytes); cmd.Parse(argc, argv); Gnuplot yansplot = Gnuplot("yans-frame-success-rate-be.eps"); Gnuplot nistplot = Gnuplot("nist-frame-success-rate-be.eps"); Gnuplot tableplot = Gnuplot("table-frame-success-rate-be.eps"); Ptr yans = CreateObject(); Ptr nist = CreateObject(); Ptr table = CreateObject(); WifiTxVector txVector; uint32_t frameSizeBits = frameSizeBytes * 8; for (const auto& mode : modes) { std::cout << mode << std::endl; Gnuplot2dDataset yansdataset(mode); Gnuplot2dDataset nistdataset(mode); Gnuplot2dDataset tabledataset(mode); txVector.SetMode(mode); WifiMode wifiMode(mode); for (double snrDb = -5.0; snrDb <= 55.0; snrDb += 0.1) { double snr = std::pow(10.0, snrDb / 10.0); double ps = yans->GetChunkSuccessRate(wifiMode, txVector, snr, frameSizeBits); if (ps < 0.0 || ps > 1.0) { // error exit(1); } yansdataset.Add(snrDb, ps); ps = nist->GetChunkSuccessRate(wifiMode, txVector, snr, frameSizeBits); if (ps < 0.0 || ps > 1.0) { // error exit(1); } nistdataset.Add(snrDb, ps); ps = table->GetChunkSuccessRate(wifiMode, txVector, snr, frameSizeBits); if (ps < 0.0 || ps > 1.0) { // error exit(1); } tabledataset.Add(snrDb, ps); } yansplot.AddDataset(yansdataset); nistplot.AddDataset(nistdataset); tableplot.AddDataset(tabledataset); } std::stringstream plotExtra; plotExtra << "set xrange [-5:55]\n\ set yrange [0:1]\n"; const std::vector colors{ "green", "blue", "red", "black", "orange", "purple", "yellow", "pink", "grey", "magenta", "brown", "turquoise", "olive", "beige", }; NS_ASSERT_MSG(colors.size() == modes.size(), "Colors and modes vectors have different sizes"); for (std::size_t i = 0; i < modes.size(); i++) { plotExtra << "set style line " << (i + 1) << " linewidth 5 linecolor rgb \"" << colors[i] << "\" \n"; } plotExtra << "set style increment user"; yansplot.SetTerminal("postscript eps color enh \"Times-BoldItalic\""); yansplot.SetLegend("SNR(dB)", "Frame Success Rate"); yansplot.SetExtra(plotExtra.str()); yansplot.GenerateOutput(yansfile); yansfile.close(); nistplot.SetTerminal("postscript eps color enh \"Times-BoldItalic\""); nistplot.SetLegend("SNR(dB)", "Frame Success Rate"); nistplot.SetExtra(plotExtra.str()); nistplot.GenerateOutput(nistfile); nistfile.close(); tableplot.SetTerminal("postscript eps color enh \"Times-BoldItalic\""); tableplot.SetLegend("SNR(dB)", "Frame Success Rate"); tableplot.SetExtra(plotExtra.str()); tableplot.GenerateOutput(tablefile); tablefile.close(); return 0; }