How to Implement Network Channel Characterization in NS2
To implement the Network Channel Characterization using NS2 that contains changing or improving the simulation models to account for several physical layer features of the communication channel, like path loss, fading, shadowing, and interference. It helps to replicate the real-world conditions more precisely, particularly for wireless networks that the propagation environment considerably effects the performance. You can believe in our experts for top project ideas and topics .
The following is a step-by-step approach to executing network channel characterization in NS2:
Step-by-Step Implementation:
- Install NS2
Make sure that NS2 is installed on the machine. For Ubuntu or Debian-based systems, we can install it using the below command:
sudo apt-get install ns2
Check the installation by running a basic example.
- Understand Channel Models in NS2
NS2 has numerous built-in propagation models, which we can be used or expanded for the channel characterization:
- Free Space Model: A basic model in which the received signal power reductions with the square of the distance.
- Two-Ray Ground Model: A model, which encompasses both line-of-sight and ground-reflected signals.
- Shadowing Model: Accounts for random differences in signal strength because of obstacles.
- Fading Models: Replicates the fast changes in the received signal strength by reason of multipath propagation like Rayleigh, Rician fading.
This models can be setup in the TCL script or expanded in the source code to denote more difficult scenarios.
- Modify or Use Propagation Models
To execute the network channel characterization, we will require to change the existing channel models or make a new ones to emulate the real-world conditions like path loss, fading, shadowing, and interference.
(a) Free-Space Model (Default)
The free-space model supposes no obstacles and direct line-of-sight. It is the basic form of channel characterization:
set opt(prop) Propagation/FreeSpace
(b) Two-Ray Ground Model
The two-ray ground model deliberates both direct and ground-reflected signals then it is more exact for larger distances:
set opt(prop) Propagation/TwoRayGround
(c) Shadowing Model
To append the random variations in the signal strength, we can use the shadowing model. Shadowing model accounts for the signal fluctuations triggered by obstacles among the transmitter and receiver:
set opt(prop) Propagation/Shadowing
$shadowingModel set pathlossExp 3.5 # Set path loss exponent
$shadowingModel set std_db 4.0 # Standard deviation for log-normal shadowing
We can change these characteristics in the shadowing.cc and shadowing.h files to replicate numerous shadowing environments.
(d) Fading Models
To mimic small-scale fading triggered by multipath effects, we can launch the Rayleigh or Rician fading. But, NS2 doesn’t directly deliver a fading model, so we will require to execute it in physical layer (phy.cc and phy.h files).
- Rayleigh Fading: We can use for non-line-of-sight (NLOS) environments in which the signal is subject to several reflections.
- Rician Fading: We can be used which there is a direct line-of-sight component together with numerous scattered signals.
For Rayleigh fading, we would model the received power using the following command:
double rayleigh_fading = pow(10.0, (gaussian() * std_dev) / 10.0);
receivedPower = rayleigh_fading * originalReceivedPower;
- Enhance SINR Calculations
Signal-to-Interference-plus-Noise Ratio (SINR) is a significant metric for typifying the network channel. In the simulation environment NS2, SINR is measured while packet reception to determine if the packet is appropriately received.
- Modify SINR Calculation: We can alter the recv() function in phy.cc to combine more channel characteristics like interference from neighbouring nodes, noise, and signal power fluctuations because of fading.
Example in phy.cc:
double interference = calcInterference(); // Calculate interference from nearby nodes
double noise = getNoisePower(); // Add noise power
double sinr = receivedPower / (interference + noise);
This SINR value can use to ascertained packet errors or decide whether to drop or forward a packet.
- Introduce Multi-path Effects
To create the simulation more realistic, we can launch the multi-path effects that the signal travels via several paths to the receiver. Every single path may have a various delay and attenuation.
Example of multi-path propagation:
double multi_path_delay = calcMultiPathDelay();
receivedPower = calcMultiPathAttenuation() * receivedPower;
- Implement Channel Quality Indicator (CQI)
We can execute a Channel Quality Indicator (CQI) to support the protocol layers are know the channel conditions well. These CQI can be a function of SINR and fading values and also can use for adaptive modulation and coding (AMC).
Steps:
- Compute the SINR and fading.
- Map the values to a CQI range.
- We can use the CQI in the MAC layer to adjust the data rate or transmission power.
Example of CQI calculation in phy.cc:
double cqi = mapSINRToCQI(sinr);
- Modify MAC Layer for Channel-Aware Protocols
To acquire benefits of the channel characterization, we can change the MAC protocols to turn out to be channel-aware. For sample, the AODV routing protocol would create decisions depends on the channel’s SINR or fading levels while selecting routes.
In aodv.cc, which contains channel characterization metrics in the route discovery process:
double linkQuality = calcSINR() + calcFadingEffect();
if (linkQuality > THRESHOLD) {
// Accept the route
}
- Implement Power Control Based on Channel Conditions
Adaptive power control is other path to improve the network performance. We can modify the transmission power rely on the present channel conditions (SINR, fading, etc.) to make sure that least interference and energy consumption.
By the end of this module, we clearly displayed the step-by-step approach to implementing network channel characterization with some instances in NS2. More details relevant to this content will also be shared.