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test propagation loss models

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This commit is contained in:
Timo Bingmann
2009-05-28 18:19:51 +02:00
parent b6c91e78f8
commit 22e869d829
1 changed files with 274 additions and 21 deletions
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295
samples/main-propagation-loss.cc
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@@ -1,6 +1,6 @@
/* -*- Mode: C++; c-file-style: "gnu"; indent-tabs-mode:nil; -*- */
/*
* Copyright (c) 2007 INRIA
* Copyright (c) 2008 Timo Bingmann
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License version 2 as
@@ -15,45 +15,298 @@
* along with this program; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*
* Author: Mathieu Lacage <mathieu.lacage@sophia.inria.fr>
* Author: Timo Bingmann <timo.bingmann@student.kit.edu>
*/
#include "ns3/propagation-loss-model.h"
#include "ns3/jakes-propagation-loss-model.h"
#include "ns3/constant-position-mobility-model.h"
#include "ns3/config.h"
#include "ns3/string.h"
#include "ns3/boolean.h"
#include "ns3/double.h"
#include "ns3/gnuplot.h"
#include "ns3/simulator.h"
#include <map>
using namespace ns3;
static void
PrintOne (double minTxpower, double maxTxpower, double stepTxpower, double min, double max, double step)
/// Round a double number to the given precision. e.g. dround(0.234, 0.1) = 0.2
/// and dround(0.257, 0.1) = 0.3
static double dround(double number, double precision)
{
number /= precision;
if (number >= 0)
{
number = floor(number + 0.5);
}
else
{
number = ceil(number - 0.5);
}
number *= precision;
return number;
}
static Gnuplot
TestDeterministic (Ptr<PropagationLossModel> model)
{
Ptr<ConstantPositionMobilityModel> a = CreateObject<ConstantPositionMobilityModel> ();
Ptr<ConstantPositionMobilityModel> b = CreateObject<ConstantPositionMobilityModel> ();
Ptr<LogDistancePropagationLossModel> log = CreateObject<LogDistancePropagationLossModel> ();
Ptr<PropagationLossModel> model = log;
Gnuplot plot;
a->SetPosition (Vector (0.0, 0.0, 0.0));
for (double x = min; x < max; x+= step)
{
b->SetPosition (Vector (x, 0.0, 0.0));
std::cout << x << " ";
for (double txpower = minTxpower; txpower < maxTxpower; txpower += stepTxpower)
{
double rxPowerDbm = model->CalcRxPower (txpower, a, b);
std::cout << rxPowerDbm << " ";
}
std::cout << std::endl;
}
plot.AppendExtra("set xlabel 'Distance'");
plot.AppendExtra("set ylabel 'rxPower (dBm)'");
plot.AppendExtra("set key top right");
double txPowerDbm = +20; // dBm
Gnuplot2dDataset dataset;
dataset.SetStyle(Gnuplot2dDataset::LINES);
{
a->SetPosition (Vector (0.0, 0.0, 0.0));
for (double distance = 0.0; distance < 2500.0; distance += 10.0)
{
b->SetPosition (Vector (distance, 0.0, 0.0));
// CalcRxPower() returns dBm.
double rxPowerDbm = model->CalcRxPower (txPowerDbm, a, b);
dataset.Add(distance, rxPowerDbm);
Simulator::Stop (Seconds (1.0));
Simulator::Run ();
}
}
std::ostringstream os;
os << "txPower " << txPowerDbm << "dBm";
dataset.SetTitle(os.str());
plot.AddDataset(dataset);
plot.AddDataset( Gnuplot2dFunction("-94 dBm CSThreshold", "-94.0") );
return plot;
}
static Gnuplot
TestProbabilistic (Ptr<PropagationLossModel> model, unsigned int samples = 100000)
{
Ptr<ConstantPositionMobilityModel> a = CreateObject<ConstantPositionMobilityModel> ();
Ptr<ConstantPositionMobilityModel> b = CreateObject<ConstantPositionMobilityModel> ();
Gnuplot plot;
plot.AppendExtra("set xlabel 'Distance'");
plot.AppendExtra("set ylabel 'rxPower (dBm)'");
plot.AppendExtra("set zlabel 'Probability' offset 0,+10");
plot.AppendExtra("set view 50, 120, 1.0, 1.0");
plot.AppendExtra("set key top right");
plot.AppendExtra("set ticslevel 0");
plot.AppendExtra("set xtics offset -0.5,0");
plot.AppendExtra("set ytics offset 0,-0.5");
plot.AppendExtra("set xrange [100:]");
double txPowerDbm = +20; // dBm
Gnuplot3dDataset dataset;
dataset.SetStyle("with linespoints");
dataset.SetExtra("pointtype 3 pointsize 0.5");
typedef std::map<double, unsigned int> rxPowerMapType;
// Take given number of samples from CalcRxPower() and show probability
// density for discrete distances.
{
a->SetPosition (Vector (0.0, 0.0, 0.0));
for (double distance = 100.0; distance < 2500.0; distance += 100.0)
{
b->SetPosition (Vector (distance, 0.0, 0.0));
rxPowerMapType rxPowerMap;
for (unsigned int samp = 0; samp < samples; ++samp)
{
// CalcRxPower() returns dBm.
double rxPowerDbm = model->CalcRxPower (txPowerDbm, a, b);
rxPowerDbm = dround(rxPowerDbm, 1.0);
rxPowerMap[ rxPowerDbm ] ++;
Simulator::Stop (Seconds (0.01));
Simulator::Run ();
}
for (rxPowerMapType::const_iterator i = rxPowerMap.begin();
i != rxPowerMap.end(); ++i)
{
dataset.Add(distance, i->first, (double)i->second / (double)samples);
}
dataset.AddEmptyLine();
}
}
std::ostringstream os;
os << "txPower " << txPowerDbm << "dBm";
dataset.SetTitle(os.str());
plot.AddDataset(dataset);
return plot;
}
static Gnuplot
TestDeterministicByTime (Ptr<PropagationLossModel> model,
Time timeStep = Seconds(0.001),
Time timeTotal = Seconds(1.0),
double distance = 100.0)
{
Ptr<ConstantPositionMobilityModel> a = CreateObject<ConstantPositionMobilityModel> ();
Ptr<ConstantPositionMobilityModel> b = CreateObject<ConstantPositionMobilityModel> ();
Gnuplot plot;
plot.AppendExtra("set xlabel 'Time (s)'");
plot.AppendExtra("set ylabel 'rxPower (dBm)'");
plot.AppendExtra("set key center right");
double txPowerDbm = +20; // dBm
Gnuplot2dDataset dataset;
dataset.SetStyle(Gnuplot2dDataset::LINES);
{
a->SetPosition (Vector (0.0, 0.0, 0.0));
b->SetPosition (Vector (distance, 0.0, 0.0));
Time start = Simulator::Now();
while( Simulator::Now() < start + timeTotal )
{
// CalcRxPower() returns dBm.
double rxPowerDbm = model->CalcRxPower (txPowerDbm, a, b);
Time elapsed = Simulator::Now() - start;
dataset.Add(elapsed.GetSeconds(), rxPowerDbm);
Simulator::Stop (timeStep);
Simulator::Run ();
}
}
std::ostringstream os;
os << "txPower " << txPowerDbm << "dBm";
dataset.SetTitle(os.str());
plot.AddDataset(dataset);
plot.AddDataset( Gnuplot2dFunction("-94 dBm CSThreshold", "-94.0") );
return plot;
}
int main (int argc, char *argv[])
{
GnuplotCollection gnuplots("main-propagation-loss.pdf");
Config::SetDefault ("ns3::LogDistancePropagationLossModel::ReferenceDistance", StringValue ("1.0"));
Config::SetDefault ("ns3::LogDistancePropagationLossModel::Exponent", StringValue ("4"));
{
Ptr<FriisPropagationLossModel> friis = CreateObject<FriisPropagationLossModel> ();
PrintOne (-10, 20, 5, 0, 10000, 2);
Gnuplot plot = TestDeterministic(friis);
plot.SetTitle("ns3::FriisPropagationLossModel (Default Parameters)");
gnuplots.AddPlot(plot);
}
{
Ptr<LogDistancePropagationLossModel> log = CreateObject<LogDistancePropagationLossModel> ();
log->SetAttribute("Exponent", DoubleValue (2.5));
Gnuplot plot = TestDeterministic(log);
plot.SetTitle("ns3::LogDistancePropagationLossModel (Exponent = 2.5)");
gnuplots.AddPlot(plot);
}
{
Ptr<RandomPropagationLossModel> random = CreateObject<RandomPropagationLossModel> ();
random->SetAttribute("Variable", RandomVariableValue(ExponentialVariable(50.0)));
Gnuplot plot = TestDeterministic(random);
plot.SetTitle("ns3::RandomPropagationLossModel with Exponential Distribution");
gnuplots.AddPlot(plot);
}
{
Ptr<JakesPropagationLossModel> jakes = CreateObject<JakesPropagationLossModel> ();
// doppler frequency shift for 5.15 GHz at 100 km/h
jakes->SetAttribute("DopplerFreq", DoubleValue(477.9));
Gnuplot plot = TestDeterministicByTime (jakes, Seconds(0.001), Seconds(1.0));
plot.SetTitle("ns3::JakesPropagationLossModel (with 477.9 Hz shift and 1 millisec resolution)");
gnuplots.AddPlot(plot);
}
{
Ptr<JakesPropagationLossModel> jakes = CreateObject<JakesPropagationLossModel> ();
// doppler frequency shift for 5.15 GHz at 100 km/h
jakes->SetAttribute("DopplerFreq", DoubleValue(477.9));
Gnuplot plot = TestDeterministicByTime (jakes, Seconds(0.0001), Seconds(0.1));
plot.SetTitle("ns3::JakesPropagationLossModel (with 477.9 Hz shift and 0.1 millisec resolution)");
gnuplots.AddPlot(plot);
}
{
Ptr<ThreeLogDistancePropagationLossModel> log3 = CreateObject<ThreeLogDistancePropagationLossModel> ();
Gnuplot plot = TestDeterministic(log3);
plot.SetTitle("ns3::ThreeLogDistancePropagationLossModel (Defaults)");
gnuplots.AddPlot(plot);
}
{
Ptr<ThreeLogDistancePropagationLossModel> log3 = CreateObject<ThreeLogDistancePropagationLossModel> ();
// more prominent example values:
log3->SetAttribute("Exponent0", DoubleValue(1.0));
log3->SetAttribute("Exponent1", DoubleValue(3.0));
log3->SetAttribute("Exponent2", DoubleValue(10.0));
Gnuplot plot = TestDeterministic(log3);
plot.SetTitle("ns3::ThreeLogDistancePropagationLossModel (Exponents 1.0, 3.0 and 10.0)");
gnuplots.AddPlot(plot);
}
{
Ptr<NakagamiPropagationLossModel> nak = CreateObject<NakagamiPropagationLossModel> ();
Gnuplot plot = TestProbabilistic(nak);
plot.SetTitle("ns3::NakagamiPropagationLossModel (Default Parameters)");
gnuplots.AddPlot(plot);
}
{
Ptr<ThreeLogDistancePropagationLossModel> log3 = CreateObject<ThreeLogDistancePropagationLossModel> ();
Ptr<NakagamiPropagationLossModel> nak = CreateObject<NakagamiPropagationLossModel> ();
log3->SetNext(nak);
Gnuplot plot = TestProbabilistic(log3);
plot.SetTitle("ns3::ThreeLogDistancePropagationLossModel and ns3::NakagamiPropagationLossModel (Default Parameters)");
gnuplots.AddPlot(plot);
}
gnuplots.GenerateOutput(std::cout);
return 0;
}