To implement network demodulation in ns3, we need to simulate a communication system in which that signals are transmitted, modulated, and demodulated. Since the ns3 doesn’t directly support to simulate the physical layer signal processing (such as modulation and demodulation) by including dedicated signal processing library we can achieve this. Still, we can simulate the effects of these processes at a high level.
Below we have provided an example for implementing this ion ns3. In this example, by using the error models we will simulate a simple wireless communication system in that the effects of modulation and demodulation indirectly evaluated this will create an impact of modulation schemes on the signal.
Step-by-step to implement network demodulation in ns3:
Step 1: Setup ns3 Environment
Ensure that ns3 is installed on the system and properly configured.
git clone https://gitlab.com/nsnam/ns-3-dev.git
cd ns-3-dev
./waf configure
./waf build
Step 2: Create the Network Demodulation Simulation Script
We will create a script that sets up a simple wireless network, applies an error model to emulate the effects of modulation and demodulation, and simulates data transmission.
#include “ns3/core-module.h”
#include “ns3/network-module.h”
#include “ns3/internet-module.h”
#include “ns3/point-to-point-module.h”
#include “ns3/mobility-module.h”
#include “ns3/applications-module.h”
#include “ns3/wifi-module.h”
#include “ns3/error-model.h”
#include “ns3/flow-monitor-module.h”
using namespace ns3;
NS_LOG_COMPONENT_DEFINE(“NetworkDemodulationExample”);
class CustomErrorModel : public ErrorModel
{
public:
static TypeId GetTypeId(void)
{
static TypeId tid = TypeId(“ns3::CustomErrorModel”)
.SetParent<ErrorModel>()
.SetGroupName(“Network”)
.AddConstructor<CustomErrorModel>()
.AddAttribute(“ErrorRate”,
“The rate of errors (0.0 to 1.0)”,
DoubleValue(0.01),
MakeDoubleAccessor(&CustomErrorModel::m_errorRate),
MakeDoubleChecker<double>());
return tid;
}
CustomErrorModel() : m_errorRate(0.01) {}
private:
virtual bool DoCorrupt(Ptr<Packet> p)
{
return (m_errorRate > 0 && (UniformVariable().GetValue() < m_errorRate));
}
virtual void DoReset(void) {}
double m_errorRate;
};
void CourseChange(std::string context, Ptr<const MobilityModel> model)
{
Vector position = model->GetPosition();
Vector velocity = model->GetVelocity();
NS_LOG_UNCOND(Simulator::Now().GetSeconds() << “s: Node at (” << position.x << “, ” << position.y << “, ” << position.z << “) with velocity (” << velocity.x << “, ” << velocity.y << “, ” << velocity.z << “)”);
}
int main(int argc, char *argv[])
{
CommandLine cmd;
cmd.Parse(argc, argv);
// Create nodes
NodeContainer nodes;
nodes.Create(10); // Ten nodes in total
// Set up mobility model
MobilityHelper mobility;
mobility.SetPositionAllocator(“ns3::GridPositionAllocator”,
“MinX”, DoubleValue(0.0),
“MinY”, DoubleValue(0.0),
“DeltaX”, DoubleValue(10.0),
“DeltaY”, DoubleValue(10.0),
“GridWidth”, UintegerValue(5),
“LayoutType”, StringValue(“RowFirst”));
mobility.SetMobilityModel(“ns3::ConstantPositionMobilityModel”);
mobility.Install(nodes);
// Set up WiFi
WifiHelper wifi;
wifi.SetRemoteStationManager(“ns3::AarfWifiManager”);
WifiMacHelper mac;
Ssid ssid = Ssid(“ns-3-ssid”);
mac.SetType(“ns3::StaWifiMac”,
“Ssid”, SsidValue(ssid),
“ActiveProbing”, BooleanValue(false));
YansWifiPhyHelper phy = YansWifiPhyHelper::Default();
YansWifiChannelHelper channel = YansWifiChannelHelper::Default();
phy.SetChannel(channel.Create());
NetDeviceContainer devices = wifi.Install(phy, mac, nodes);
mac.SetType(“ns3::ApWifiMac”,
“Ssid”, SsidValue(ssid));
NetDeviceContainer apDevices = wifi.Install(phy, mac, nodes.Get(0));
// Apply custom error model to emulate modulation/demodulation effects
Ptr<CustomErrorModel> errorModel = CreateObject<CustomErrorModel>();
errorModel->SetAttribute(“ErrorRate”, DoubleValue(0.05)); // Example error rate for demodulation
devices.Get(1)->SetAttribute(“ReceiveErrorModel”, PointerValue(errorModel));
// Install the internet stack
InternetStackHelper stack;
stack.Install(nodes);
// Assign IP addresses
Ipv4AddressHelper address;
address.SetBase(“10.1.1.0”, “255.255.255.0”);
Ipv4InterfaceContainer interfaces = address.Assign(devices);
address.Assign(apDevices);
// Install applications to generate traffic
uint16_t port = 9;
OnOffHelperonoff(“ns3::UdpSocketFactory”, Address(InetSocketAddress(interfaces.GetAddress(1), port)));
onoff.SetConstantRate(DataRate(“1Mbps”));
ApplicationContainer apps = onoff.Install(nodes.Get(0));
apps.Start(Seconds(1.0));
apps.Stop(Seconds(10.0));
PacketSinkHelpersink(“ns3::UdpSocketFactory”, Address(InetSocketAddress(Ipv4Address::GetAny(), port)));
apps = sink.Install(nodes.Get(1));
apps.Start(Seconds(0.0));
apps.Stop(Seconds(10.0));
// Enable FlowMonitor to measure performance metrics
FlowMonitorHelper flowmon;
Ptr<FlowMonitor> monitor = flowmon.InstallAll();
// Run the simulation
Simulator::Stop(Seconds(15.0));
Simulator::Run();
// Print per-flow statistics
monitor->CheckForLostPackets();
Ptr<Ipv4FlowClassifier>classifier = DynamicCast<Ipv4FlowClassifier>(flowmon.GetClassifier());
std::map<FlowId, FlowMonitor::FlowStats> stats = monitor->GetFlowStats();
for (std::map<FlowId, FlowMonitor::FlowStats>::const_iterator i = stats.begin(); i != stats.end(); ++i)
{
Ipv4FlowClassifier::FiveTuple t = classifier->FindFlow(i->first);
NS_LOG_UNCOND(“Flow ” << i->first << ” (” << t.sourceAddress << ” -> ” << t.destinationAddress << “)”);
NS_LOG_UNCOND(” Tx Packets: ” << i->second.txPackets);
NS_LOG_UNCOND(” Tx Bytes: ” << i->second.txBytes);
NS_LOG_UNCOND(” Rx Packets: ” << i->second.rxPackets);
NS_LOG_UNCOND(” Rx Bytes: ” << i->second.rxBytes);
NS_LOG_UNCOND(” Lost Packets: ” << i->second.lostPackets);
NS_LOG_UNCOND(” Throughput: ” << i->second.rxBytes * 8.0 / (i->second.timeLastRxPacket.GetSeconds() – i->second.timeFirstTxPacket.GetSeconds()) / 1024 / 1024 << ” Mbps”);
}
// Clean up
Simulator::Destroy();
return 0;
}
Step 3: Compile and Run the Simulation
- Compile the Simulation:
./waf configure –enable-examples
./waf build
Run the Simulation:
./waf –run scratch/network-demodulation-example
Step 4: Analyze Results
The simulation script sets up a network topology with nodes and applies a custom error model to emulate the effects of modulation and demodulation. The error model introduces a certain error rate to the received packets, simulating the impact of imperfect demodulation. FlowMonitor is used to collect and print out statistics about the traffic flows, such as packet loss, throughput, and delay.
Additional Considerations
To extend the functionality of your network demodulation simulation, consider the following:
1. Modulation Schemes
Implement different error rates for different modulation schemes (e.g., BPSK, QPSK, 16-QAM) to simulate their respective performance characteristics.
2. Channel Models
Incorporate realistic channel models that account for factors such as fading, shadowing, and path loss to simulate the effects of the wireless environment on modulation and demodulation.
3. Adaptive Modulation
Implement adaptive modulation schemes that adjust the modulation level based on the channel conditions or other network parameters.
4. Performance Metrics
Collect and analyze additional metrics such as bit error rate (BER), symbol error rate (SER), and signal-to-noise ratio (SNR) to evaluate the performance of the modulation and demodulation processes.
5. Real-World Scenarios
Simulate real-world scenarios such as cellular networks, IoT deployments, or vehicular networks to test the effectiveness of the modulation and demodulation approaches in practical applications.
Over all, we all get to know how to implement Network Demodulation in ns3 by simulating a communication system where signals are transmitted, modulated, and demodulated. In this simulation a dedicated signal processing library takes place for the processing.
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