313 lines
9.2 KiB
C++
313 lines
9.2 KiB
C++
/* -*- Mode: C++; c-file-style: "gnu"; indent-tabs-mode:nil; -*- */
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/*
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* Copyright (c) 2008 Timo Bingmann
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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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* Author: Timo Bingmann <timo.bingmann@student.kit.edu>
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*/
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#include "ns3/propagation-loss-model.h"
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#include "ns3/jakes-propagation-loss-model.h"
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#include "ns3/constant-position-mobility-model.h"
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#include "ns3/config.h"
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#include "ns3/string.h"
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#include "ns3/boolean.h"
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#include "ns3/double.h"
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#include "ns3/gnuplot.h"
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#include "ns3/simulator.h"
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#include <map>
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using namespace ns3;
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/// Round a double number to the given precision. e.g. dround(0.234, 0.1) = 0.2
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/// and dround(0.257, 0.1) = 0.3
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static double dround(double number, double precision)
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{
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number /= precision;
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if (number >= 0)
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{
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number = floor(number + 0.5);
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}
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else
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{
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number = ceil(number - 0.5);
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}
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number *= precision;
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return number;
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}
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static Gnuplot
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TestDeterministic (Ptr<PropagationLossModel> model)
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{
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Ptr<ConstantPositionMobilityModel> a = CreateObject<ConstantPositionMobilityModel> ();
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Ptr<ConstantPositionMobilityModel> b = CreateObject<ConstantPositionMobilityModel> ();
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Gnuplot plot;
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plot.AppendExtra("set xlabel 'Distance'");
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plot.AppendExtra("set ylabel 'rxPower (dBm)'");
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plot.AppendExtra("set key top right");
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double txPowerDbm = +20; // dBm
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Gnuplot2dDataset dataset;
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dataset.SetStyle(Gnuplot2dDataset::LINES);
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{
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a->SetPosition (Vector (0.0, 0.0, 0.0));
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for (double distance = 0.0; distance < 2500.0; distance += 10.0)
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{
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b->SetPosition (Vector (distance, 0.0, 0.0));
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// CalcRxPower() returns dBm.
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double rxPowerDbm = model->CalcRxPower (txPowerDbm, a, b);
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dataset.Add(distance, rxPowerDbm);
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Simulator::Stop (Seconds (1.0));
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Simulator::Run ();
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}
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}
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std::ostringstream os;
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os << "txPower " << txPowerDbm << "dBm";
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dataset.SetTitle(os.str());
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plot.AddDataset(dataset);
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plot.AddDataset( Gnuplot2dFunction("-94 dBm CSThreshold", "-94.0") );
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return plot;
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}
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static Gnuplot
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TestProbabilistic (Ptr<PropagationLossModel> model, unsigned int samples = 100000)
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{
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Ptr<ConstantPositionMobilityModel> a = CreateObject<ConstantPositionMobilityModel> ();
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Ptr<ConstantPositionMobilityModel> b = CreateObject<ConstantPositionMobilityModel> ();
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Gnuplot plot;
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plot.AppendExtra("set xlabel 'Distance'");
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plot.AppendExtra("set ylabel 'rxPower (dBm)'");
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plot.AppendExtra("set zlabel 'Probability' offset 0,+10");
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plot.AppendExtra("set view 50, 120, 1.0, 1.0");
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plot.AppendExtra("set key top right");
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plot.AppendExtra("set ticslevel 0");
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plot.AppendExtra("set xtics offset -0.5,0");
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plot.AppendExtra("set ytics offset 0,-0.5");
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plot.AppendExtra("set xrange [100:]");
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double txPowerDbm = +20; // dBm
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Gnuplot3dDataset dataset;
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dataset.SetStyle("with linespoints");
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dataset.SetExtra("pointtype 3 pointsize 0.5");
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typedef std::map<double, unsigned int> rxPowerMapType;
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// Take given number of samples from CalcRxPower() and show probability
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// density for discrete distances.
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{
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a->SetPosition (Vector (0.0, 0.0, 0.0));
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for (double distance = 100.0; distance < 2500.0; distance += 100.0)
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{
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b->SetPosition (Vector (distance, 0.0, 0.0));
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rxPowerMapType rxPowerMap;
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for (unsigned int samp = 0; samp < samples; ++samp)
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{
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// CalcRxPower() returns dBm.
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double rxPowerDbm = model->CalcRxPower (txPowerDbm, a, b);
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rxPowerDbm = dround(rxPowerDbm, 1.0);
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rxPowerMap[ rxPowerDbm ] ++;
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Simulator::Stop (Seconds (0.01));
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Simulator::Run ();
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}
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for (rxPowerMapType::const_iterator i = rxPowerMap.begin();
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i != rxPowerMap.end(); ++i)
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{
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dataset.Add(distance, i->first, (double)i->second / (double)samples);
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}
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dataset.AddEmptyLine();
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}
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}
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std::ostringstream os;
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os << "txPower " << txPowerDbm << "dBm";
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dataset.SetTitle(os.str());
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plot.AddDataset(dataset);
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return plot;
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}
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static Gnuplot
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TestDeterministicByTime (Ptr<PropagationLossModel> model,
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Time timeStep = Seconds(0.001),
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Time timeTotal = Seconds(1.0),
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double distance = 100.0)
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{
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Ptr<ConstantPositionMobilityModel> a = CreateObject<ConstantPositionMobilityModel> ();
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Ptr<ConstantPositionMobilityModel> b = CreateObject<ConstantPositionMobilityModel> ();
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Gnuplot plot;
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plot.AppendExtra("set xlabel 'Time (s)'");
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plot.AppendExtra("set ylabel 'rxPower (dBm)'");
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plot.AppendExtra("set key center right");
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double txPowerDbm = +20; // dBm
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Gnuplot2dDataset dataset;
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dataset.SetStyle(Gnuplot2dDataset::LINES);
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{
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a->SetPosition (Vector (0.0, 0.0, 0.0));
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b->SetPosition (Vector (distance, 0.0, 0.0));
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Time start = Simulator::Now();
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while( Simulator::Now() < start + timeTotal )
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{
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// CalcRxPower() returns dBm.
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double rxPowerDbm = model->CalcRxPower (txPowerDbm, a, b);
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Time elapsed = Simulator::Now() - start;
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dataset.Add(elapsed.GetSeconds(), rxPowerDbm);
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Simulator::Stop (timeStep);
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Simulator::Run ();
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}
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}
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std::ostringstream os;
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os << "txPower " << txPowerDbm << "dBm";
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dataset.SetTitle(os.str());
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plot.AddDataset(dataset);
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plot.AddDataset( Gnuplot2dFunction("-94 dBm CSThreshold", "-94.0") );
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return plot;
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}
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int main (int argc, char *argv[])
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{
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GnuplotCollection gnuplots("main-propagation-loss.pdf");
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{
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Ptr<FriisPropagationLossModel> friis = CreateObject<FriisPropagationLossModel> ();
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Gnuplot plot = TestDeterministic(friis);
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plot.SetTitle("ns3::FriisPropagationLossModel (Default Parameters)");
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gnuplots.AddPlot(plot);
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}
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{
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Ptr<LogDistancePropagationLossModel> log = CreateObject<LogDistancePropagationLossModel> ();
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log->SetAttribute("Exponent", DoubleValue (2.5));
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Gnuplot plot = TestDeterministic(log);
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plot.SetTitle("ns3::LogDistancePropagationLossModel (Exponent = 2.5)");
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gnuplots.AddPlot(plot);
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}
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{
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Ptr<RandomPropagationLossModel> random = CreateObject<RandomPropagationLossModel> ();
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random->SetAttribute("Variable", RandomVariableValue(ExponentialVariable(50.0)));
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Gnuplot plot = TestDeterministic(random);
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plot.SetTitle("ns3::RandomPropagationLossModel with Exponential Distribution");
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gnuplots.AddPlot(plot);
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}
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{
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Ptr<JakesPropagationLossModel> jakes = CreateObject<JakesPropagationLossModel> ();
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// doppler frequency shift for 5.15 GHz at 100 km/h
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jakes->SetAttribute("DopplerFreq", DoubleValue(477.9));
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Gnuplot plot = TestDeterministicByTime (jakes, Seconds(0.001), Seconds(1.0));
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plot.SetTitle("ns3::JakesPropagationLossModel (with 477.9 Hz shift and 1 millisec resolution)");
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gnuplots.AddPlot(plot);
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}
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{
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Ptr<JakesPropagationLossModel> jakes = CreateObject<JakesPropagationLossModel> ();
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// doppler frequency shift for 5.15 GHz at 100 km/h
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jakes->SetAttribute("DopplerFreq", DoubleValue(477.9));
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Gnuplot plot = TestDeterministicByTime (jakes, Seconds(0.0001), Seconds(0.1));
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plot.SetTitle("ns3::JakesPropagationLossModel (with 477.9 Hz shift and 0.1 millisec resolution)");
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gnuplots.AddPlot(plot);
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}
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{
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Ptr<ThreeLogDistancePropagationLossModel> log3 = CreateObject<ThreeLogDistancePropagationLossModel> ();
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Gnuplot plot = TestDeterministic(log3);
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plot.SetTitle("ns3::ThreeLogDistancePropagationLossModel (Defaults)");
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gnuplots.AddPlot(plot);
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}
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{
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Ptr<ThreeLogDistancePropagationLossModel> log3 = CreateObject<ThreeLogDistancePropagationLossModel> ();
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// more prominent example values:
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log3->SetAttribute("Exponent0", DoubleValue(1.0));
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log3->SetAttribute("Exponent1", DoubleValue(3.0));
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log3->SetAttribute("Exponent2", DoubleValue(10.0));
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Gnuplot plot = TestDeterministic(log3);
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plot.SetTitle("ns3::ThreeLogDistancePropagationLossModel (Exponents 1.0, 3.0 and 10.0)");
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gnuplots.AddPlot(plot);
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}
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{
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Ptr<NakagamiPropagationLossModel> nak = CreateObject<NakagamiPropagationLossModel> ();
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Gnuplot plot = TestProbabilistic(nak);
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plot.SetTitle("ns3::NakagamiPropagationLossModel (Default Parameters)");
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gnuplots.AddPlot(plot);
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}
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{
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Ptr<ThreeLogDistancePropagationLossModel> log3 = CreateObject<ThreeLogDistancePropagationLossModel> ();
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Ptr<NakagamiPropagationLossModel> nak = CreateObject<NakagamiPropagationLossModel> ();
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log3->SetNext(nak);
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Gnuplot plot = TestProbabilistic(log3);
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plot.SetTitle("ns3::ThreeLogDistancePropagationLossModel and ns3::NakagamiPropagationLossModel (Default Parameters)");
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gnuplots.AddPlot(plot);
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}
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gnuplots.GenerateOutput(std::cout);
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return 0;
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}
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