three-gpp-propagation-loss-model-test-suite.cc

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/*
 * Copyright (c) 2019 SIGNET Lab, Department of Information Engineering,
 * University of Padova
 *
 * 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
 * published by the Free Software Foundation;
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 */
 
#include "ns3/abort.h"
#include "ns3/boolean.h"
#include "ns3/channel-condition-model.h"
#include "ns3/config.h"
#include "ns3/constant-position-mobility-model.h"
#include "ns3/constant-velocity-mobility-model.h"
#include "ns3/double.h"
#include "ns3/log.h"
#include "ns3/mobility-helper.h"
#include "ns3/simulator.h"
#include "ns3/test.h"
#include "ns3/three-gpp-propagation-loss-model.h"
#include "ns3/three-gpp-v2v-propagation-loss-model.h"
 
using namespace ns3;
 
NS_LOG_COMPONENT_DEFINE("ThreeGppPropagationLossModelsTest");
 
class ThreeGppRmaPropagationLossModelTestCase : public TestCase
{
  public:
    ThreeGppRmaPropagationLossModelTestCase();
 
    ~ThreeGppRmaPropagationLossModelTestCase() override;
 
  private:
    void DoRun() override;
 
    struct TestVector
    {
        double m_distance;  
        bool m_isLos;       
        double m_frequency; 
        double m_pt;        
        double m_pr;        
    };
 
    TestVectors<TestVector> m_testVectors; 
    double m_tolerance;                    
};
 
ThreeGppRmaPropagationLossModelTestCase::ThreeGppRmaPropagationLossModelTestCase()
    : TestCase("Test for the ThreeGppRmaPropagationLossModel class"),
      m_testVectors(),
      m_tolerance(5e-2)
{
}
 
ThreeGppRmaPropagationLossModelTestCase::~ThreeGppRmaPropagationLossModelTestCase()
{
}
 
void
ThreeGppRmaPropagationLossModelTestCase::DoRun()
{
    TestVector testVector;
 
    testVector.m_distance = 10.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -77.3784;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 100.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -87.2965;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 1000.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -108.5577;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 10000.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -140.3896;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 10.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -77.3784;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 100.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -95.7718;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 1000.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -133.5223;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 5000.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -160.5169;
    m_testVectors.Add(testVector);
 
    // Create the nodes for BS and UT
    NodeContainer nodes;
    nodes.Create(2);
 
    // Create the mobility models
    Ptr<MobilityModel> a = CreateObject<ConstantPositionMobilityModel>();
    nodes.Get(0)->AggregateObject(a);
    Ptr<MobilityModel> b = CreateObject<ConstantPositionMobilityModel>();
    nodes.Get(1)->AggregateObject(b);
 
    // Use a deterministic channel condition model
    Ptr<ChannelConditionModel> losCondModel = CreateObject<AlwaysLosChannelConditionModel>();
    Ptr<ChannelConditionModel> nlosCondModel = CreateObject<NeverLosChannelConditionModel>();
 
    // Create the propagation loss model
    Ptr<ThreeGppRmaPropagationLossModel> lossModel =
        CreateObject<ThreeGppRmaPropagationLossModel>();
    lossModel->SetAttribute("ShadowingEnabled", BooleanValue(false)); // disable the shadow fading
 
    for (std::size_t i = 0; i < m_testVectors.GetN(); i++)
    {
        TestVector testVector = m_testVectors.Get(i);
 
        Vector posBs = Vector(0.0, 0.0, 35.0);
        Vector posUt = Vector(testVector.m_distance, 0.0, 1.5);
 
        // set the LOS or NLOS condition
        if (testVector.m_isLos)
        {
            lossModel->SetChannelConditionModel(losCondModel);
        }
        else
        {
            lossModel->SetChannelConditionModel(nlosCondModel);
        }
 
        a->SetPosition(posBs);
        b->SetPosition(posUt);
 
        lossModel->SetAttribute("Frequency", DoubleValue(testVector.m_frequency));
        NS_TEST_EXPECT_MSG_EQ_TOL(lossModel->CalcRxPower(testVector.m_pt, a, b),
                                  testVector.m_pr,
                                  m_tolerance,
                                  "Got unexpected rcv power");
    }
 
    Simulator::Destroy();
}
 
class ThreeGppUmaPropagationLossModelTestCase : public TestCase
{
  public:
    ThreeGppUmaPropagationLossModelTestCase();
 
    ~ThreeGppUmaPropagationLossModelTestCase() override;
 
  private:
    void DoRun() override;
 
    struct TestVector
    {
        double m_distance;  
        bool m_isLos;       
        double m_frequency; 
        double m_pt;        
        double m_pr;        
    };
 
    TestVectors<TestVector> m_testVectors; 
    double m_tolerance;                    
};
 
ThreeGppUmaPropagationLossModelTestCase::ThreeGppUmaPropagationLossModelTestCase()
    : TestCase("Test for the ThreeGppUmaPropagationLossModel class"),
      m_testVectors(),
      m_tolerance(5e-2)
{
}
 
ThreeGppUmaPropagationLossModelTestCase::~ThreeGppUmaPropagationLossModelTestCase()
{
}
 
void
ThreeGppUmaPropagationLossModelTestCase::DoRun()
{
    TestVector testVector;
 
    testVector.m_distance = 10.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -72.9380;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 100.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -86.2362;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 1000.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -109.7252;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 5000.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -137.6794;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 10.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -82.5131;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 100.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -106.1356;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 1000.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -144.7641;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 5000.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -172.0753;
    m_testVectors.Add(testVector);
 
    // Create the nodes for BS and UT
    NodeContainer nodes;
    nodes.Create(2);
 
    // Create the mobility models
    Ptr<MobilityModel> a = CreateObject<ConstantPositionMobilityModel>();
    nodes.Get(0)->AggregateObject(a);
    Ptr<MobilityModel> b = CreateObject<ConstantPositionMobilityModel>();
    nodes.Get(1)->AggregateObject(b);
 
    // Use a deterministic channel condition model
    Ptr<ChannelConditionModel> losCondModel = CreateObject<AlwaysLosChannelConditionModel>();
    Ptr<ChannelConditionModel> nlosCondModel = CreateObject<NeverLosChannelConditionModel>();
 
    // Create the propagation loss model
    Ptr<ThreeGppUmaPropagationLossModel> lossModel =
        CreateObject<ThreeGppUmaPropagationLossModel>();
    lossModel->SetAttribute("ShadowingEnabled", BooleanValue(false)); // disable the shadow fading
 
    for (std::size_t i = 0; i < m_testVectors.GetN(); i++)
    {
        TestVector testVector = m_testVectors.Get(i);
 
        Vector posBs = Vector(0.0, 0.0, 25.0);
        Vector posUt = Vector(testVector.m_distance, 0.0, 1.5);
 
        // set the LOS or NLOS condition
        if (testVector.m_isLos)
        {
            lossModel->SetChannelConditionModel(losCondModel);
        }
        else
        {
            lossModel->SetChannelConditionModel(nlosCondModel);
        }
 
        a->SetPosition(posBs);
        b->SetPosition(posUt);
 
        lossModel->SetAttribute("Frequency", DoubleValue(testVector.m_frequency));
        NS_TEST_EXPECT_MSG_EQ_TOL(lossModel->CalcRxPower(testVector.m_pt, a, b),
                                  testVector.m_pr,
                                  m_tolerance,
                                  "Got unexpected rcv power");
    }
 
    Simulator::Destroy();
}
 
class ThreeGppUmiPropagationLossModelTestCase : public TestCase
{
  public:
    ThreeGppUmiPropagationLossModelTestCase();
 
    ~ThreeGppUmiPropagationLossModelTestCase() override;
 
  private:
    void DoRun() override;
 
    struct TestVector
    {
        double m_distance;  
        bool m_isLos;       
        double m_frequency; 
        double m_pt;        
        double m_pr;        
    };
 
    TestVectors<TestVector> m_testVectors; 
    double m_tolerance;                    
};
 
ThreeGppUmiPropagationLossModelTestCase::ThreeGppUmiPropagationLossModelTestCase()
    : TestCase("Test for the ThreeGppUmiPropagationLossModel class"),
      m_testVectors(),
      m_tolerance(5e-2)
{
}
 
ThreeGppUmiPropagationLossModelTestCase::~ThreeGppUmiPropagationLossModelTestCase()
{
}
 
void
ThreeGppUmiPropagationLossModelTestCase::DoRun()
{
    TestVector testVector;
 
    testVector.m_distance = 10.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -69.8591;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 100.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -88.4122;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 1000.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -119.3114;
 
    testVector.m_distance = 5000.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -147.2696;
 
    testVector.m_distance = 10.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -76.7563;
 
    testVector.m_distance = 100.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -107.9432;
 
    testVector.m_distance = 1000.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -143.1886;
 
    testVector.m_distance = 5000.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -167.8617;
 
    // Create the nodes for BS and UT
    NodeContainer nodes;
    nodes.Create(2);
 
    // Create the mobility models
    Ptr<MobilityModel> a = CreateObject<ConstantPositionMobilityModel>();
    nodes.Get(0)->AggregateObject(a);
    Ptr<MobilityModel> b = CreateObject<ConstantPositionMobilityModel>();
    nodes.Get(1)->AggregateObject(b);
 
    // Use a deterministic channel condition model
    Ptr<ChannelConditionModel> losCondModel = CreateObject<AlwaysLosChannelConditionModel>();
    Ptr<ChannelConditionModel> nlosCondModel = CreateObject<NeverLosChannelConditionModel>();
 
    // Create the propagation loss model
    Ptr<ThreeGppUmiStreetCanyonPropagationLossModel> lossModel =
        CreateObject<ThreeGppUmiStreetCanyonPropagationLossModel>();
    lossModel->SetAttribute("ShadowingEnabled", BooleanValue(false)); // disable the shadow fading
 
    for (std::size_t i = 0; i < m_testVectors.GetN(); i++)
    {
        TestVector testVector = m_testVectors.Get(i);
 
        Vector posBs = Vector(0.0, 0.0, 10.0);
        Vector posUt = Vector(testVector.m_distance, 0.0, 1.5);
 
        // set the LOS or NLOS condition
        if (testVector.m_isLos)
        {
            lossModel->SetChannelConditionModel(losCondModel);
        }
        else
        {
            lossModel->SetChannelConditionModel(nlosCondModel);
        }
 
        a->SetPosition(posBs);
        b->SetPosition(posUt);
 
        lossModel->SetAttribute("Frequency", DoubleValue(testVector.m_frequency));
        NS_TEST_EXPECT_MSG_EQ_TOL(lossModel->CalcRxPower(testVector.m_pt, a, b),
                                  testVector.m_pr,
                                  m_tolerance,
                                  "Got unexpected rcv power");
    }
 
    Simulator::Destroy();
}
 
class ThreeGppIndoorOfficePropagationLossModelTestCase : public TestCase
{
  public:
    ThreeGppIndoorOfficePropagationLossModelTestCase();
 
    ~ThreeGppIndoorOfficePropagationLossModelTestCase() override;
 
  private:
    void DoRun() override;
 
    struct TestVector
    {
        double m_distance;  
        bool m_isLos;       
        double m_frequency; 
        double m_pt;        
        double m_pr;        
    };
 
    TestVectors<TestVector> m_testVectors; 
    double m_tolerance;                    
};
 
ThreeGppIndoorOfficePropagationLossModelTestCase::ThreeGppIndoorOfficePropagationLossModelTestCase()
    : TestCase("Test for the ThreeGppIndoorOfficePropagationLossModel class"),
      m_testVectors(),
      m_tolerance(5e-2)
{
}
 
ThreeGppIndoorOfficePropagationLossModelTestCase::
    ~ThreeGppIndoorOfficePropagationLossModelTestCase()
{
}
 
void
ThreeGppIndoorOfficePropagationLossModelTestCase::DoRun()
{
    TestVector testVector;
 
    testVector.m_distance = 1.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -50.8072;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 10.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -63.7630;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 50.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -75.7750;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 100.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -80.9802;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 1.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -50.8072;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 10.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -73.1894;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 50.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -99.7824;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 100.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -111.3062;
    m_testVectors.Add(testVector);
 
    // Create the nodes for BS and UT
    NodeContainer nodes;
    nodes.Create(2);
 
    // Create the mobility models
    Ptr<MobilityModel> a = CreateObject<ConstantPositionMobilityModel>();
    nodes.Get(0)->AggregateObject(a);
    Ptr<MobilityModel> b = CreateObject<ConstantPositionMobilityModel>();
    nodes.Get(1)->AggregateObject(b);
 
    // Use a deterministic channel condition model
    Ptr<ChannelConditionModel> losCondModel = CreateObject<AlwaysLosChannelConditionModel>();
    Ptr<ChannelConditionModel> nlosCondModel = CreateObject<NeverLosChannelConditionModel>();
 
    // Create the propagation loss model
    Ptr<ThreeGppIndoorOfficePropagationLossModel> lossModel =
        CreateObject<ThreeGppIndoorOfficePropagationLossModel>();
    lossModel->SetAttribute("ShadowingEnabled", BooleanValue(false)); // disable the shadow fading
 
    for (std::size_t i = 0; i < m_testVectors.GetN(); i++)
    {
        TestVector testVector = m_testVectors.Get(i);
 
        Vector posBs = Vector(0.0, 0.0, 3.0);
        Vector posUt = Vector(testVector.m_distance, 0.0, 1.5);
 
        // set the LOS or NLOS condition
        if (testVector.m_isLos)
        {
            lossModel->SetChannelConditionModel(losCondModel);
        }
        else
        {
            lossModel->SetChannelConditionModel(nlosCondModel);
        }
 
        a->SetPosition(posBs);
        b->SetPosition(posUt);
 
        lossModel->SetAttribute("Frequency", DoubleValue(testVector.m_frequency));
        NS_TEST_EXPECT_MSG_EQ_TOL(lossModel->CalcRxPower(testVector.m_pt, a, b),
                                  testVector.m_pr,
                                  m_tolerance,
                                  "Got unexpected rcv power");
    }
 
    Simulator::Destroy();
}
 
class ThreeGppV2vUrbanPropagationLossModelTestCase : public TestCase
{
  public:
    ThreeGppV2vUrbanPropagationLossModelTestCase();
 
    ~ThreeGppV2vUrbanPropagationLossModelTestCase() override;
 
  private:
    void DoRun() override;
 
    struct TestVector
    {
        double m_distance;  
        bool m_isLos;       
        double m_frequency; 
        double m_pt;        
        double m_pr;        
    };
 
    TestVectors<TestVector> m_testVectors; 
    double m_tolerance;                    
};
 
ThreeGppV2vUrbanPropagationLossModelTestCase::ThreeGppV2vUrbanPropagationLossModelTestCase()
    : TestCase("Test for the ThreeGppV2vUrbanPropagationLossModel class."),
      m_testVectors(),
      m_tolerance(5e-2)
{
}
 
ThreeGppV2vUrbanPropagationLossModelTestCase::~ThreeGppV2vUrbanPropagationLossModelTestCase()
{
}
 
void
ThreeGppV2vUrbanPropagationLossModelTestCase::DoRun()
{
    TestVector testVector;
 
    testVector.m_distance = 10.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -68.1913;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 100.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -84.8913;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 1000.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -101.5913;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 10.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -80.0605;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 100.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -110.0605;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 1000.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -140.0605;
    m_testVectors.Add(testVector);
 
    // Create the nodes for BS and UT
    NodeContainer nodes;
    nodes.Create(2);
 
    // Create the mobility models
    Ptr<MobilityModel> a = CreateObject<ConstantPositionMobilityModel>();
    nodes.Get(0)->AggregateObject(a);
    Ptr<MobilityModel> b = CreateObject<ConstantPositionMobilityModel>();
    nodes.Get(1)->AggregateObject(b);
 
    // Use a deterministic channel condition model
    Ptr<ChannelConditionModel> losCondModel = CreateObject<AlwaysLosChannelConditionModel>();
    Ptr<ChannelConditionModel> nlosCondModel = CreateObject<NeverLosChannelConditionModel>();
 
    // Create the propagation loss model
    Ptr<ThreeGppPropagationLossModel> lossModel =
        CreateObject<ThreeGppV2vUrbanPropagationLossModel>();
    lossModel->SetAttribute("ShadowingEnabled", BooleanValue(false)); // disable the shadow fading
 
    for (std::size_t i = 0; i < m_testVectors.GetN(); i++)
    {
        TestVector testVector = m_testVectors.Get(i);
 
        Vector posUe1 = Vector(0.0, 0.0, 1.6);
        Vector posUe2 = Vector(testVector.m_distance, 0.0, 1.6);
 
        // set the LOS or NLOS condition
        if (testVector.m_isLos)
        {
            lossModel->SetChannelConditionModel(losCondModel);
        }
        else
        {
            lossModel->SetChannelConditionModel(nlosCondModel);
        }
 
        a->SetPosition(posUe1);
        b->SetPosition(posUe2);
 
        lossModel->SetAttribute("Frequency", DoubleValue(testVector.m_frequency));
        NS_TEST_EXPECT_MSG_EQ_TOL(lossModel->CalcRxPower(testVector.m_pt, a, b),
                                  testVector.m_pr,
                                  m_tolerance,
                                  "Got unexpected rcv power");
    }
 
    Simulator::Destroy();
}
 
class ThreeGppV2vHighwayPropagationLossModelTestCase : public TestCase
{
  public:
    ThreeGppV2vHighwayPropagationLossModelTestCase();
 
    ~ThreeGppV2vHighwayPropagationLossModelTestCase() override;
 
  private:
    void DoRun() override;
 
    struct TestVector
    {
        double m_distance;  
        bool m_isLos;       
        double m_frequency; 
        double m_pt;        
        double m_pr;        
    };
 
    TestVectors<TestVector> m_testVectors; 
    double m_tolerance;                    
};
 
ThreeGppV2vHighwayPropagationLossModelTestCase::ThreeGppV2vHighwayPropagationLossModelTestCase()
    : TestCase("Test for the ThreeGppV2vHighwayPropagationLossModel"),
      m_testVectors(),
      m_tolerance(5e-2)
{
}
 
ThreeGppV2vHighwayPropagationLossModelTestCase::~ThreeGppV2vHighwayPropagationLossModelTestCase()
{
}
 
void
ThreeGppV2vHighwayPropagationLossModelTestCase::DoRun()
{
    TestVector testVector;
 
    testVector.m_distance = 10.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -66.3794;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 100.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -86.3794;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 1000.0;
    testVector.m_isLos = true;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -106.3794;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 10.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -80.0605;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 100.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -110.0605;
    m_testVectors.Add(testVector);
 
    testVector.m_distance = 1000.0;
    testVector.m_isLos = false;
    testVector.m_frequency = 5.0e9;
    testVector.m_pt = 0.0;
    testVector.m_pr = -140.0605;
    m_testVectors.Add(testVector);
 
    // Create the nodes for BS and UT
    NodeContainer nodes;
    nodes.Create(2);
 
    // Create the mobility models
    Ptr<MobilityModel> a = CreateObject<ConstantPositionMobilityModel>();
    nodes.Get(0)->AggregateObject(a);
    Ptr<MobilityModel> b = CreateObject<ConstantPositionMobilityModel>();
    nodes.Get(1)->AggregateObject(b);
 
    // Use a deterministic channel condition model
    Ptr<ChannelConditionModel> losCondModel = CreateObject<AlwaysLosChannelConditionModel>();
    Ptr<ChannelConditionModel> nlosCondModel = CreateObject<NeverLosChannelConditionModel>();
 
    // Create the propagation loss model
    Ptr<ThreeGppPropagationLossModel> lossModel =
        CreateObject<ThreeGppV2vHighwayPropagationLossModel>();
    lossModel->SetAttribute("ShadowingEnabled", BooleanValue(false)); // disable the shadow fading
 
    for (std::size_t i = 0; i < m_testVectors.GetN(); i++)
    {
        TestVector testVector = m_testVectors.Get(i);
 
        Vector posUe1 = Vector(0.0, 0.0, 1.6);
        Vector posUe2 = Vector(testVector.m_distance, 0.0, 1.6);
 
        // set the LOS or NLOS condition
        if (testVector.m_isLos)
        {
            lossModel->SetChannelConditionModel(losCondModel);
        }
        else
        {
            lossModel->SetChannelConditionModel(nlosCondModel);
        }
 
        a->SetPosition(posUe1);
        b->SetPosition(posUe2);
 
        lossModel->SetAttribute("Frequency", DoubleValue(testVector.m_frequency));
        NS_TEST_EXPECT_MSG_EQ_TOL(lossModel->CalcRxPower(testVector.m_pt, a, b),
                                  testVector.m_pr,
                                  m_tolerance,
                                  "Got unexpected rcv power");
    }
 
    Simulator::Destroy();
}
 
class ThreeGppShadowingTestCase : public TestCase
{
  public:
    ThreeGppShadowingTestCase();
    ~ThreeGppShadowingTestCase() override;
 
  private:
    void DoRun() override;
 
    void RunTest(uint16_t testNum,
                 std::string propagationLossModelType,
                 double hBs,
                 double hUt,
                 double distance,
                 bool shadowingEnabled);
 
    void EvaluateLoss(Ptr<MobilityModel> a, Ptr<MobilityModel> b, uint8_t testNum);
 
    void ChangeChannelCondition(Ptr<ChannelConditionModel> ccm);
 
    struct TestVector
    {
        std::string m_propagationLossModelType; 
        double m_hBs;                           
        double m_hUt;                           
        double m_distance;        
        double m_shadowingStdLos; 
        double m_shadowingStdNlos; 
    };
 
    TestVectors<TestVector> m_testVectors;         
    Ptr<ThreeGppPropagationLossModel> m_lossModel; 
    std::map<uint16_t /* index of experiment */, std::vector<double> /* loss in dB for each run */>
        m_results; 
};
 
ThreeGppShadowingTestCase::ThreeGppShadowingTestCase()
    : TestCase("Test to check if the shadow fading is correctly computed")
{
}
 
ThreeGppShadowingTestCase::~ThreeGppShadowingTestCase()
{
}
 
void
ThreeGppShadowingTestCase::EvaluateLoss(Ptr<MobilityModel> a, Ptr<MobilityModel> b, uint8_t testNum)
{
    double loss = m_lossModel->CalcRxPower(0, a, b);
    m_results.at(testNum).push_back(loss);
}
 
void
ThreeGppShadowingTestCase::ChangeChannelCondition(Ptr<ChannelConditionModel> ccm)
{
    m_lossModel->SetChannelConditionModel(ccm);
}
 
void
ThreeGppShadowingTestCase::RunTest(uint16_t testNum,
                                   std::string propagationLossModelType,
                                   double hBs,
                                   double hUt,
                                   double distance,
                                   bool shadowingEnabled)
{
    // Add a new entry for this test in the results map
    m_results[testNum] = std::vector<double>();
 
    // Create the nodes for BS and UT
    NodeContainer nodes;
    nodes.Create(2);
 
    // Create the mobility models
    Ptr<MobilityModel> a = CreateObject<ConstantPositionMobilityModel>();
    a->SetPosition(Vector(0.0, 0.0, hBs));
    nodes.Get(0)->AggregateObject(a);
 
    Ptr<ConstantVelocityMobilityModel> b = CreateObject<ConstantVelocityMobilityModel>();
    nodes.Get(1)->AggregateObject(b);
    b->SetPosition(Vector(0.0, distance, hUt));
    b->SetVelocity(Vector(1.0, 0.0, 0.0));
 
    // Create the propagation loss model
    ObjectFactory propagationLossModelFactory = ObjectFactory(propagationLossModelType);
    m_lossModel = propagationLossModelFactory.Create<ThreeGppPropagationLossModel>();
    m_lossModel->SetAttribute("Frequency", DoubleValue(3.5e9));
    m_lossModel->SetAttribute("ShadowingEnabled",
                              BooleanValue(shadowingEnabled)); // enable the shadow fading
 
    // Set the channel condition to LOS
    Ptr<ChannelConditionModel> losCondModel = CreateObject<AlwaysLosChannelConditionModel>();
    m_lossModel->SetChannelConditionModel(losCondModel);
    // Schedule a transition to NLOS
    Ptr<ChannelConditionModel> nlosCondModel = CreateObject<NeverLosChannelConditionModel>();
    Simulator::Schedule(Seconds(99.5),
                        &ThreeGppShadowingTestCase::ChangeChannelCondition,
                        this,
                        nlosCondModel);
 
    // Schedule multiple calls to EvaluateLoss. Use both EvaluateLoss (a,b) and
    // EvaluateLoss (b,a) to check if the reciprocity holds.
    for (int i = 0; i < 200; i++)
    {
        if (i % 2 == 0)
        {
            Simulator::Schedule(MilliSeconds(1000 * i),
                                &ThreeGppShadowingTestCase::EvaluateLoss,
                                this,
                                a,
                                b,
                                testNum);
        }
        else
        {
            Simulator::Schedule(MilliSeconds(1000 * i),
                                &ThreeGppShadowingTestCase::EvaluateLoss,
                                this,
                                b,
                                a,
                                testNum);
        }
    }
 
    Simulator::Run();
    Simulator::Destroy();
}
 
void
ThreeGppShadowingTestCase::DoRun()
{
    // The test scenario is composed of two nodes, one fixed
    // at position (0,0) and the other moving with constant velocity from
    // position (0,50) to position (200,50).
    // The channel condition changes from LOS to NLOS when the second node
    // reaches position (100,50).
    // Each experiment computes the propagation loss between the two nodes
    // every second, until the final position is reached, and saves the
    // results in an entry of the map m_results.
    // We run numSamples experiments and estimate the mean propagation loss in
    // each position by averaging among the samples.
    // Then, we perform the null hypothesis test with a significance level of
    // 0.05.
    // This procedure is repeated for all the 3GPP propagation scenarios, i.e.,
    // RMa, UMa, UMi and Indoor-Office.
 
    TestVector testVector;
    testVector.m_propagationLossModelType = "ns3::ThreeGppRmaPropagationLossModel";
    testVector.m_hBs = 25;
    testVector.m_hUt = 1.6;
    testVector.m_distance = 100;
    testVector.m_shadowingStdLos = 4;
    testVector.m_shadowingStdNlos = 8;
    m_testVectors.Add(testVector);
 
    testVector.m_propagationLossModelType = "ns3::ThreeGppRmaPropagationLossModel";
    testVector.m_hBs = 25;
    testVector.m_hUt = 1.6;
    testVector.m_distance = 4000; // beyond the breakpoint distance
    testVector.m_shadowingStdLos = 6;
    testVector.m_shadowingStdNlos = 8;
    m_testVectors.Add(testVector);
 
    testVector.m_propagationLossModelType = "ns3::ThreeGppUmaPropagationLossModel";
    testVector.m_hBs = 25;
    testVector.m_hUt = 1.6;
    testVector.m_distance = 100;
    testVector.m_shadowingStdLos = 4;
    testVector.m_shadowingStdNlos = 6;
    m_testVectors.Add(testVector);
 
    testVector.m_propagationLossModelType = "ns3::ThreeGppUmiStreetCanyonPropagationLossModel";
    testVector.m_hBs = 10;
    testVector.m_hUt = 1.6;
    testVector.m_distance = 100;
    testVector.m_shadowingStdLos = 4;
    testVector.m_shadowingStdNlos = 7.82;
    m_testVectors.Add(testVector);
 
    testVector.m_propagationLossModelType = "ns3::ThreeGppIndoorOfficePropagationLossModel";
    testVector.m_hBs = 3;
    testVector.m_hUt = 1;
    testVector.m_distance = 50;
    testVector.m_shadowingStdLos = 3;
    testVector.m_shadowingStdNlos = 8.03;
    m_testVectors.Add(testVector);
 
    testVector.m_propagationLossModelType = "ns3::ThreeGppV2vUrbanPropagationLossModel";
    testVector.m_hBs = 1.6;
    testVector.m_hUt = 1.6;
    testVector.m_distance = 50;
    testVector.m_shadowingStdLos = 3;
    testVector.m_shadowingStdNlos = 4;
    m_testVectors.Add(testVector);
 
    testVector.m_propagationLossModelType = "ns3::ThreeGppV2vHighwayPropagationLossModel";
    testVector.m_hBs = 1.6;
    testVector.m_hUt = 1.6;
    testVector.m_distance = 50;
    testVector.m_shadowingStdLos = 3;
    testVector.m_shadowingStdNlos = 4;
    m_testVectors.Add(testVector);
 
    uint16_t numSamples = 250;
 
    for (std::size_t tvIndex = 0; tvIndex < m_testVectors.GetN(); tvIndex++)
    {
        TestVector tv = m_testVectors.Get(tvIndex);
 
        // run the experiments.
        for (uint16_t sampleIndex = 0; sampleIndex < numSamples; sampleIndex++)
        {
            RunTest(sampleIndex,
                    tv.m_propagationLossModelType,
                    tv.m_hBs,
                    tv.m_hUt,
                    tv.m_distance,
                    true);
        }
 
        // analyze the results
        std::vector<double> mean_vector; // the vector containing the mean propagation loss for each
                                         // position (sample mean)
 
        uint16_t numPositions = m_results.at(0).size();
        for (uint16_t k = 0; k < numPositions; k++)
        {
            // compute the mean propagation loss in position k
            double mean = 0.0;
            for (auto resIt : m_results)
            {
                mean += resIt.second.at(k);
            }
            mean /= m_results.size();
            mean_vector.push_back(mean);
        }
 
        // compute the true mean - just the pathloss, without the shadowing component
        RunTest(numSamples,
                tv.m_propagationLossModelType,
                tv.m_hBs,
                tv.m_hUt,
                tv.m_distance,
                false);
        std::vector<double> true_mean =
            m_results.at(numSamples); // the result of the last test is the true mean
 
        // perform the null hypothesis test for the LOS case
        // positions from (0, 50) to (99, 50) are LOS
        for (std::size_t i = 0; i < mean_vector.size() / 2; i++)
        {
            double z = (mean_vector.at(i) - true_mean.at(i)) /
                       (tv.m_shadowingStdLos / std::sqrt(mean_vector.size() / 2));
            NS_TEST_EXPECT_MSG_EQ_TOL(
                z,
                0.0,
                1.96,
                "Null hypothesis test (LOS case) for the shadowing component rejected");
        }
 
        // perform the null hypothesis test for the NLOS case
        // positions from (100, 50) to (199, 50) are NLOS
        for (std::size_t i = mean_vector.size() / 2; i < mean_vector.size(); i++)
        {
            double z = (mean_vector.at(i) - true_mean.at(i)) /
                       (tv.m_shadowingStdNlos / std::sqrt(mean_vector.size() / 2));
            NS_TEST_EXPECT_MSG_EQ_TOL(
                z,
                0.0,
                1.96,
                "Null hypothesis test (NLOS case) for the shadowing component rejected");
        }
    }
}
 
class ThreeGppPropagationLossModelsTestSuite : public TestSuite
{
  public:
    ThreeGppPropagationLossModelsTestSuite();
};
 
ThreeGppPropagationLossModelsTestSuite::ThreeGppPropagationLossModelsTestSuite()
    : TestSuite("three-gpp-propagation-loss-model", UNIT)
{
    AddTestCase(new ThreeGppRmaPropagationLossModelTestCase, TestCase::QUICK);
    AddTestCase(new ThreeGppUmaPropagationLossModelTestCase, TestCase::QUICK);
    AddTestCase(new ThreeGppUmiPropagationLossModelTestCase, TestCase::QUICK);
    AddTestCase(new ThreeGppIndoorOfficePropagationLossModelTestCase, TestCase::QUICK);
    AddTestCase(new ThreeGppV2vUrbanPropagationLossModelTestCase, TestCase::QUICK);
    AddTestCase(new ThreeGppV2vHighwayPropagationLossModelTestCase, TestCase::QUICK);
    AddTestCase(new ThreeGppShadowingTestCase, TestCase::QUICK);
}
 
static ThreeGppPropagationLossModelsTestSuite g_propagationLossModelsTestSuite;