IRS-Assisted OFDM Communication System

Overview

This repository contains the implementation of an Intelligent Reflecting Surface (IRS)-assisted Orthogonal Frequency Division Multiplexing (OFDM) communication system. The project explores multi-user mobility patterns and their impact on spectral efficiency and bit-error rate (BER). It includes detailed simulations of user mobility using linear and 2D random walk models, equalization techniques, and IRS integration for enhanced signal strength and coverage.

Features

• Simulates an OFDM transceiver with Rayleigh fading and IRS integration.
• Models user mobility with linear and 2D random walks.
• Implements modulation schemes like BPSK, QPSK, and 16QAM for BER vs. SNR analysis.
• Incorporates Long Short-Term Memory (LSTM) networks for user mobility prediction.
• Provides insights into IRS grid size, proximity effects, and multi-user scenarios.

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Requirements

• Python 3.8 or above
• Libraries: numpy, matplotlib, scipy, tensorflow, sklearn, jupyter

Install dependencies using:

pip install -r requirements.txt

How to Run

1. Clone the repository:

   git clone https://github.com/YourUsername/IRS_OFDM_Project.git
   cd IRS_OFDM_Project

2. Set up a Python environment and install dependencies.
3. Run the Jupyter Notebooks in /notebooks for simulation:

   jupyter notebook

4. Alternatively, execute Python scripts in /src for specific modules:

   python src/ofdm_transceiver.py

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Key Results

1. BER vs. SNR Analysis:

   • BPSK provides better BER performance than QPSK and 16QAM.
   • Increasing IRS grid size improves signal quality marginally for static users.

2. Spectral Efficiency with User Mobility:

   • Spectral efficiency peaks near the IRS and decreases with distance.
   • LSTM predictions smoothen mobility simulation results.

3. Multi-User Scenario:

   • Sum rates increase exponentially with SNR in multi-user simulations.

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References

• Wu, Q., Zhang, S., Zheng, B., You, C., & Zhang, R. (2021). "Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial." IEEE Transactions on Communications.
• Ahmed, Q. (2023). "BER Analysis of IRS-Assisted Wireless Communications in Generalized Gaussian Noise." IEEE Transactions on Vehicular Technology.