NS2 Wireless Simulation Projects

NS2 wireless simulation is used in modeling and assessing the effectiveness and activities of various wireless networks. The procedure of seizing the various modules it provides for wireless simulations, the scope of concepts it encompasses, and the programming languages it employs, are included in the interpretation of NS2. Based on these, we offer an elaborate description:

NS2 Wireless Simulation Modules

Encompassing numerous crucial elements which enable thorough designing and exploration of wireless networks, infrastructure of NS2 for wireless simulation is considered as flexible.

Mobile Node: A wireless device which contains the ability to traverse within the simulation region are depicted by the mobile nodes. In ad-hoc networks, mobile nodes serve as routers, hosts, or both.
Channel: It acts as a medium by which wireless nodes interact in an effective manner. The simulation of various channel characteristics such as propagation models and interference are assisted by NS2.
Propagation Model: In what manner signals diminish across distance and by different interruptions are defined. The Shadowing Model and the Two-Ray Ground Reflection Model are the usual frameworks that are involved.
MAC Layer Protocols: Crucial for handling access to the shared wireless medium, NS2 assists different MAC layer protocols. Typically, TDMA, IEEE 802.11, and CSMA/CA might be encompassed.
Networking Layer Protocols: Deployments of numerous ad-hoc network protocols like OLSR (Optimized Link State Routing), AODV (Ad hoc On-Demand Distance Vector), and DSR (Dynamic Source Routing) are involved in NS2 for routing data among a network.
Interface Queue: The packets awaiting to be transmitted across the network could be handled. It also supports in impacting latency and throughput as well as simulating actual world buffer constraints.

Programming Languages

Mainly, through the utilization of two languages, NS2 simulations are developed:

Tcl (Tool Command Language): For setting up the simulation platform, configuring simulations, and explaining network topology, Tcl is employed in an extensive manner. Generally, the links, nodes, and traffic within the simulation could be indicated by Tcl scripts.
C++: In C++, the fundamental simulation frameworks and protocols are executed. Therefore, effective implementation of simulations is enabled. As a means to alter or prolong previous frameworks and protocols, it offers adaptation for scholars.

Subjects Encompassed

The investigation among an extensive scope of concepts in wireless networking are enabled by the abilities of NS2:

Ad-Hoc Networks: The limitations of medium access control in networks without core infrastructure, the effectiveness of routing protocols, and the influence of node mobility could be explored by simulations.
Wireless LANs: Under different settings, the activities of IEEE 802.11 networks might be investigated by scholars. Generally, QoS mechanisms have to be assessed in an effective manner. Within simulated WLAN platforms, it is significant to assess safety protocols.
Sensor Networks: Concentrating on sensor network topology improvement, energy-effective communication, and data gathering approaches, NS2 could be employed to simulate wireless sensor networks (WSNs).
Satellite Communications: For solving problems such as link changeability and long propagation delays, NS2 enables the simulation of satellite communication networks even though it is more unique.
Propagation and Interference Studies: Typically, effectiveness of the network in varied realistic platforms could be forecasted by scholars through simulating various propagation models and intervention impacts.
Network Protocols Evaluation: For the model, assessing, and contrast of network protocols, NS2 is utilized in an extensive manner. The perceptions based on network dynamics and protocol activities under different settings could be provided.

How to write code in MATLAB for planning wireless 5G

The process of writing code in MATLAB for planning wireless 5G is examined as both difficult and captivating. Several instructions must be adhered to while writing it. We suggest a fundamental instruction based on how to begin coding a 5G network simulation in MATLAB:

Step 1: Explain Simulation Parameters

Initially, the metrics of our 5G simulation ought to be explained in an explicit manner. It could encompass frequency band, number of antennas, bandwidth, kind of modulation, and subcarrier spacing. For instance:

simulationParams.Band = ‘n78’; % Frequency band

simulationParams.Bandwidth = 100e6; % 100 MHz

simulationParams.SubcarrierSpacing = 30; % 30 kHz

simulationParams.NumAntennas = [2 2]; % 2 Tx and 2 Rx antennas

simulationParams.ModulationType = ‘QAM16’; % 16-QAM Modulation

Step 2: Set Up the 5G NR Channel

In order to set up the 5G NR channel framework, such as MIMO arrangement, path loss systems, and delay profile like CDL or TDL, it is beneficial to employ the 5G toolbox.

nrChannel = nrTDLChannel; % Using Tapped Delay Line (TDL) model

nrChannel.DelayProfile = ‘TDL-C’; % Delay profile

nrChannel.NumTransmitAntennas = simulationParams.NumAntennas(1);

nrChannel.NumReceiveAntennas = simulationParams.NumAntennas(2);

Step 3: Create Waveform

Through the utilization of the specified metrics, we plan to produce a 5G NR waveform. The process of developing a test signal, arranging the carrier, and a kind of waveform such as TDD or FDD could be encompassed.

carrier = nrCarrierConfig(‘NSizeGrid’, 52, …

‘SubcarrierSpacing’, simulationParams.SubcarrierSpacing, …

‘NCellID’, 1);

pdsch = nrPDSCHConfig(‘Modulation’, simulationParams.ModulationType, …

‘PRBSet’, (0:51)’);

% Generate random bits for PDSCH

bits = randi([0 1], 1000, 1);

% Generate PDSCH symbols

waveform = nrWaveformGenerator(bits, carrier, pdsch);

Step 4: Simulate the Channel

As a means to simulate the impact of the wireless channel, the produced waveform ought to be travelled through the arranged 5G NR channel framework.

nrChannel.SampleRate = waveform.SampleRate;

[rxWaveform, pathGains] = nrChannel(waveform.Signal);

Step 5: Carry Out Receiver Processing

To assess the channel and balance the signal, it is significant to process the obtained waveform at the receiver. Generally, demodulation, synchronization, and channel estimation are the signal processing steps that could be included.

% Example: Channel Estimation and Equalization

[estChannelGrid, noiseEst] = nrChannelEstimate(rxWaveform, carrier, nrChannel);

eqSignal = nrEqualizeMMSE(rxWaveform, estChannelGrid, noiseEst);

Step 6: Examine the Effectiveness

As a final point, the effectiveness of our 5G simulation has to be examined. Throughput, Bit Error Rate (BER), and Signal-to-Noise Ratio (SNR), are usual parameters encompassed in this procedure.

% Example: Calculate BER

rxBits = nrPDSCHDecode(eqSignal, carrier, pdsch, estChannelGrid, noiseEst);

ber = sum(rxBits ~= bits) / length(bits);

disp([‘BER: ‘ num2str(ber)]);

Together with NS2 wireless simulation modules, programming languages it employs, and the scope of concepts it involves, a thorough explanation on NS2 are provided by us. Also, we have recommended a simple direction on the basis of how to begin coding a 5G network simulation in MATLAB in this article.

NS2 Wireless Simulation Projects

NS2 Wireless Simulation Projects For beginners and also for researchers are provided below, stay in touch with us if you are looking for a customised NS2 Wireless Simulation Projects .Send us all your project details we will help you with expertise reasech solutions.

A novel extended Kalman filter-based optimized routing approach for IoV environment
NIE-GAT: node importance evaluation method for inter-domain routing network based on graph attention network
A two-phase total optimization on aircraft stand assignment and tow-tractor routing considering energy-saving and attributes
EMVLight: A multi-agent reinforcement learning framework for an emergency vehicle decentralized routing and traffic signal control system
Adaptively prioritizing candidate forwarding set in opportunistic routing in VANETs
A framework for the elderly first aid system by integrating vision-based fall detection and BIM-based indoor rescue routing
An evolutionary algorithm with indirect representation for droplet routing in digital microfluidic biochips
A genetic algorithm model for optimizing vehicle routing problems with perishable products under time-window and quality requirements
CAIN: An energy-aware and intelligent increasing coverage area routing protocol for future 6G networks
A bi-objective home care routing and scheduling problem considering patient preference and soft temporal dependency constraints
GOR: Group-oblivious multicast routing in airborne tactical networks under uncertainty
GRADE: Deep learning and garlic routing-based secure data sharing framework for IIoT beyond 5G
MetaLearn: Optimizing routing heuristics with a hybrid meta-learning approach in vehicular ad-hoc networks
Online eco-routing for electric vehicles using combinatorial multi-armed bandit with estimated covariance
Low-carbon routing for cold-chain logistics considering the time-dependent effects of traffic congestion
Infectious waste management during a pandemic: A stochastic location-routing problem with chance-constrained time windows
The time-dependent electric vehicle routing problem with drone and synchronized mobile battery swapping
A partially flexible routing strategy for assigning emergency department patients to inpatient wards
Revised solution technique for a bi-level location-inventory-routing problem under uncertainty of demand and perishability of products
Routing and scheduling optimization for UAV assisted delivery system: A hybrid approach