• Matlab
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  • NS2

In the field of networking, different topics are emerging related to WiFi network simulators nowadays. Our team of experts is dedicated to helping individuals with their WIFI Network Simulator projects by providing them with valuable information, abundant resources, and the necessary assistance.  We provide a list of various WiFi network simulator strategies and topics including the recommendations for their performance analysis metrics:

  1. Coexistence of WiFi and LTE in Unlicensed Spectrum (LTE-U/LAA)
  • Explanation: By aiming at perfect spectrum distribution and intrusion handling, research in what way WiFi networks coincide with LTE techniques in the unlicensed spectrum.
  • Performance Analysis Metrics: Fairness index, throughput degradation, latency influence and spectrum utilization.
  1. Performance Evaluation of WiFi 6 (802.11ax)
  • Explanation: Specifically in dense platforms, simulate the advanced WiFi 6 technology based on ability, throughput and effectiveness to observe its enhancements after existing generations.
  • Performance Analysis Metrics: Effectiveness on various data requests and user densities, network strength, latency and throughput.
  1. WiFi Offloading Strategies
  • Explanation: To enhance user experience and ease network congestion, explore policies for offloading cellular network traffic to WiFi networks.
  • Performance Analysis Metrics: Quality of Service (QoS0, user throughput, network congestion stages and data offloading functionality.
  1. Impact of WiFi Network Configuration on Performance
  • Explanation: In different situations, determine how the performance of the network is impacted by various WiFi network configurations like MIMO settings, channel bonding.
  • Performance Analysis Metrics: Network range, error rates, signal-to-noise (SNR) and throughput.
  1. Energy Efficiency in WiFi Networks
  • Explanation: For IoT devices and effective network allocation discover energy-saving methods in Wi-Fi networks like power-saving modes.
  • Performance Analysis Metrics: Efficiency trade-offs, network accessibility, device battery life and power consumption.
  1. WiFi Security Protocols Performance Analysis
  • Explanation: On network speed and dependability, observe the functionality influence of various WiFi safety protocols like WPA3 and WPA2.
  • Performance Analysis Metrics: Computational overhead, link establishment duration, throughput mitigation because of encryption/decryption.
  1. Mesh WiFi Network Performance
  • Explanation: To interpret their scalability, efficiency features, and flexibility in enclosing huge fields, simulate mesh WiFi networks.
  • Performance Analysis Metrics: Flexibility to node breakdowns, network scalability, coverage area and throughput at different distances.
  1. Interference Management in Dense WiFi Environments
  • Explanation: In dense WiFi platforms like industrial regions and apartment constructions, research technologies for handling interventions.
  • Performance Analysis Metrics: User satisfaction, packet retransmissions, intervention stages and throughput differences.
  1. WiFi Network Planning and Optimization
  • Explanation: By examining user density, channel selection and access point positioning, employ simulation to model and enhance WiFi network deployments.
  • Performance Analysis Metrics: Deployment cost-efficiency, user throughput distribution, capacity planning accuracy and coverage uniformity.
  1. QoS in WiFi Networks for Real-time Applications
  • Explanation: For assisting practical applications such as online gaming and VoIP, check the performance of QoS systems in WiFi networks.
  • Performance Analysis Metrics: Throughput stability, end-to-end latency, packet loss and jitter.

How to simulate wifi network?

NS-3 is a most famous network simulator for both academics and research. It is a discrete-event network simulator for internet frameworks. We suggest you a simple approach on simulating a WiFi network properly through NS-3 simulator:

Step 1: Choose a Network Simulator

  • Along with the complete frameworks for IEEE 11 a/b/g/n/ac/ax qualities, NS-3 is broadly implemented for its huge assistance for WiFi simulations.
  • There are some other choices like OPNET Modeler, OMNeT++ and MATLAB’s Simulink with Communication Toolbox.

Step 2: Install the Simulator

  • Especially for NS-3:
  • From the official website, download the latest version of NS-3.
  • For your specific operating system like Windows, macOS and Linux, it is necessary to adhere to the installation guidelines.

Step 3: Understand the WiFi Model in the Simulator

  • For simulating WiFi networks, NS-3 offers frameworks the contains the properties such as:
  • Various IEEE 802.11 standards.
  • AP modes, architecture and Ad hoc.
  • Layer configurations of MAC and PHY.

Step 4: Create a Simulation Script

  • To state the parameters, network topology, and devices, you should write a simulation script in C++ or Python when assisted by the simulator. Your script will commonly include the following aspects for a basic WiFi network simulation in NS-3:
  • Node Creation: Explain WiFi access points (APs) and user devices which are denoted by the nodes.
  • WiFi Device Configuration: By indicating the channel width, all PHY/MAC layer parameters and WiFi standard like 802.11n, create WiFi devices on nodes.
  • Mobility Model: When required, the mobility designs such as random walk or static for nodes have to be described.
  • Application Layer Setup: To produce traffic like sinks and TCP or UDP traffic sources, configure network applications.
  • Start the Simulation: Begin the simulation by mentioning the timeframe of the simulation.

Step 5: Execute the Simulation and Collect Data

  • Run the script of the simulation.
  • Signal strength, packet loss, delay and throughput are the performance metrics that have to be gathered. To collect this data, NS-3 provides different logging and monitoring choices.

Step 6: Analyze Results and Iterate

  • Interpret the efficacy of the network on the simulated criteria by observing the gathered data.
  • To discover various features of Wi-Fi network activity, alter parameters and situations in terms of your detections.

Example: Basic NS-3 Simulation Script

To simulate a simple WiFi network, we give this basic example of an NS-3 script snippet:

// Basic includes for WiFi simulation

#include “ns3/core-module.h”

#include “ns3/network-module.h”

#include “ns3/mobility-module.h”

#include “ns3/wifi-module.h”

#include “ns3/internet-module.h”

using namespace ns3;

int main (int argc, char *argv[])


// Disable fragmentation and RTS/CTS

Config::SetDefault (“ns3::WifiRemoteStationManager::FragmentationThreshold”, StringValue (“999999”));

Config::SetDefault (“ns3::WifiRemoteStationManager::RtsCtsThreshold”, StringValue (“999999”));

// Create nodes: 1 AP and 1 station (STA)

NodeContainer wifiNodes;

wifiNodes.Create (2);

// Set up WiFi devices and channel

WifiHelper wifi;

wifi.SetStandard (WIFI_PHY_STANDARD_80211n_5GHZ);

YansWifiChannelHelper channel = YansWifiChannelHelper::Default ();

YansWifiPhyHelper phy = YansWifiPhyHelper::Default ();

phy.SetChannel (channel.Create ());

// Configure MAC layer and install devices

WifiMacHelper mac;

Ssid ssid = Ssid (“ns-3-wifi”);

mac.SetType (“ns3::StaWifiMac”, “Ssid”, SsidValue (ssid));

NetDeviceContainer staDevice = wifi.Install (phy, mac, wifiNodes.Get (0)); // STA device

mac.SetType (“ns3::ApWifiMac”, “Ssid”, SsidValue (ssid));

NetDeviceContainer apDevice = wifi.Install (phy, mac, wifiNodes.Get (1)); // AP device

// More configuration and simulation setup code goes here…

Simulator::Run ();

Simulator::Destroy ();

return 0;


The specified example is targeted to represent the process of establishment and is simpler. To design and observe the efficiency of WiFi networks in a precise manner, the real simulation situations act as highly difficult aspects which need a thorough configuration of the application traffic, mobility frameworks and network devices.

WIFI Network Simulator Topics

WIFI Network Simulator Topics & Ideas

We prioritize building strong relationships with our students, understanding their needs, and exceeding their expectations. Our online experts are available 24/7/365 to offer professional services in the latest technology and innovative ideas. With their extensive qualifications and years of experience, our professionals have successfully served clients from various countries around the globe. Now, let’s take a glimpse at the recent WIFI Network Simulator topics and ideas that we have worked on.

  1. Humidity Estimation Using WiFi Channel State Information
  2. Performance Modeling of Tags-to-WiFi Transmissions for Contention-based WiFi Backscatter Networks
  3. Design and Deployment of a Practical Wireless Sensing System for HAR with WiFi CSI in the 5GHz Band
  4. Human Pose Estimation Using Commodity WiFi and Deep Learning Approach
  5. Real-Time NLOS/LOS Identification for Smartphone-Based Indoor Positioning Systems Using WiFi RTT and RSS
  6. An Efficient Human Activity Recognition System Using WiFi Channel State Information
  7. Hand Movement Velocity Estimation From WiFi Channel State Information
  8. Respiration detection of sedentary person using ubiquitous WiFi signals
  9. Doppler Sensing Using WiFi Round-Trip Channel State Information
  10. WiFi Based Multi-Task Sensing via Selective Sharing Module
  11. WiFi-Based Spatiotemporal Human Action Perception
  12. Statistical Channel Model Based on Passive Measurements for Indoor WiFi Communications at 2.4 GHz and 5.8 GHz Bands
  13. Cross-Technology Communication between WiFi and ZigBee
  14. An Encoded LSTM Network Model for WiFi-based Indoor Positioning
  15. An improved WiFi fingerprint location method for indoor positioning
  16. A DRQN-based Initial Contention Window Optimization Algorithm for NR-U and WiFi Coexistence Networks
  17. Deep Transfer Learning for Actions Recognition with WiFi Signals
  18. NR-U Deep Receiver for WiFi Presence Detection
  19. WiFi Localization Algorithm Based on Snake Optimization Algorithm to Optimize BP Neural Network
  20. WIP: Impulsive Noise Source Recognition with OFDM-WiFi Signals Based on Channel State Information Using Machine Learning

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