How to Implement Network Channel Equalization in OMNeT++

To implement the network channel equalization in OMNeT++ has encompasses mimicking the process of correcting the distortions familiarised by a communication channel. It is vital in wireless communication systems to mitigate the effects of interference, multipath fading, and other channel impairments that can degrade signal quality.

Given below is a step-by-step process to executing a basic channel equalization mechanism in OMNeT++ using the INET framework:

Step-by-Step Implementations:

1. Set Up OMNeT++ and INET Framework:

• Install OMNeT++: Make sure OMNeT++ is installed and configured on the system.
• Install INET Framework: Download and install the INET framework, which offers models for wireless communication, containing channel models and transmission/reception components.

2. Define the Network Topology:

Make a network topology where wireless nodes communicate with each other. The channel model will familiarise distortions that the equalizer will try to correct.

Example NED File (ChannelEqualizationNetwork.ned):

package mynetwork;

import inet.node.inet.StandardHost;

import inet.node.inet.AccessPoint;

network ChannelEqualizationNetwork

{

submodules:

hostA: StandardHost {

@display(“p=100,200”);

}

hostB: StandardHost {

@display(“p=300,200”);

}

ap: AccessPoint {

@display(“p=200,200”);

}

connections allowunconnected:

hostA.wlan[0] <–> wlan[0] <–> ap.wlan[0];

ap.wlan[0] <–> wlan[0] <–> hostB.wlan[0];

}

In this example:

• hostA and hostB: Denote the transmitting and receiving nodes.
• ap: Performs as an access point over which the communication occurs.

3. Configure the Channel Model:

We want to configure a channel model that presents distortions like multipath fading or noise. INET offers several channel models that can mimic these effects.

Example Channel Configuration in omnetpp.ini:

[General]

network = ChannelEqualizationNetwork

**.**.wlan[0].radio.transmitter.power = 20mW

**.**.wlan[0].radio.receiver.sensitivity = -85dBm

**.**.wlan[0].radio.analogModel.typename = “FlatFadingAnalogModel”

**.**.wlan[0].radio.analogModel.pathLossModel.typename = “FreeSpacePathLoss”

**.**.wlan[0].radio.analogModel.fadingModel.typename = “RayleighFading”

In this configuration:

• FlatFadingAnalogModel: Mimics flat fading, where the whole signal bandwidth is affected uniformly.
• FreeSpacePathLoss: Models the loss of signal strength as it propagates over free space.
• RayleighFading: Mimics the effects of multipath fading.

4. Implement Channel Equalization:

Channel equalization is normally performed at the receiver to right the distorted signal. For simplicity, we will execute a basic equalizer that modifies for flat fading using a linear equalization technique.

Example: Basic Channel Equalizer Implementation in C++

void Receiver::handleMessage(cMessage *msg) {

auto packet = check_and_cast<Packet *>(msg);

// Simulate the effect of the channel (e.g., fading)

simtime_t arrivalTime = packet->getTag<CreationTimeTag>()->getCreationTime();

double channelEffect = computeChannelEffect(arrivalTime);

// Apply equalization to correct the channel effect

double correctedSignal = equalizeSignal(packet->peekData<ByteCountChunk>(), channelEffect);

 

EV << “Received packet with corrected signal: ” << correctedSignal << “\n”;

// Process the corrected signal (e.g., demodulate, decode)

processSignal(correctedSignal);

delete packet;

}

double Receiver::computeChannelEffect(simtime_t arrivalTime) {

// Placeholder for computing the effect of the channel (e.g., based on fading model)

return 0.8;  // Example: signal is attenuated to 80% of its original value

}

Double Receiver::equalizeSignal(const Ptr<const ByteCountChunk>& signal, double channelEffect) {

// Apply a simple linear equalization

return signal->getLength().get() / channelEffect;  // Normalize by the channel effect

}

void Receiver::processSignal(double correctedSignal) {

// Further processing of the equalized signal

}

In this example:

• computeChannelEffect: Mimics the impact of the channel on the signal.
• equalizeSignal: Modifies the signal based on the estimated channel effect.
• processSignal: Further processes the equalized signal like decoding.

5. Simulate and Analyse the System:

Run the simulation and monitor the effect of channel equalization on the received signals. Compare the act with and without equalization by analysing metrics like signal quality, bit error rate (BER), or throughput.

Example Configuration for Recording Metrics:

[General]

network = ChannelEqualizationNetwork

**.hostB.app[0].signalQuality.recordScalar = true

**.hostB.app[0].bitErrorRate.recordScalar = true

This configuration records the signal quality and bit error rate at the receiver, which can be used to assess the efficiency of the equalization.

6. Implement and Compare Different Equalization Techniques:

We can execute and compare several equalization techniques, like:

• Linear Equalization: Simple linear scaling based on channel estimation.
• Zero-Forcing Equalization: Tries to invert the channel effects completely, which can lead to noise improvement.
• Minimum Mean Square Error (MMSE) Equalization: Balances channel inversion with noise reduction.

Example: Zero-Forcing Equalizer

double Receiver::equalizeSignal(const Ptr<const ByteCountChunk>& signal, double channelEffect) {

// Zero-Forcing Equalization: directly invert the channel effect

return signal->getLength().get() * (1.0 / channelEffect);

}

Example: MMSE Equalizer

double Receiver::equalizeSignal(const Ptr<const ByteCountChunk>& signal, double channelEffect) {

double noiseVariance = 0.01;  // Example noise variance

return signal->getLength().get() * (channelEffect / (channelEffect * channelEffect + noiseVariance));

}

7. Document and Report Findings:

After finishing the simulations, document the results, containing:

• The performance of various equalization methods.
• The impact of channel impairments on the received signal.
• The development in signal quality and bit error rate due to equalization.

It will support understanding the trade-offs among numerous equalization strategies and how they perform under different channel conditions.

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