AI-Assisted Jamming Detection and Mitigation in MIMO-OFDM Wireless Systems

Overview

This project focuses on enhancing the resilience of MIMO-OFDM wireless communication systems against jamming attacks through an AI-assisted detection and mitigation framework. Given the widespread use of MIMO-OFDM in standards such as Wi-Fi, DVB, and 5G, jamming poses a serious threat to communication reliability in security-sensitive and mission-critical applications. The proposed approach leverages supervised machine learning to detect jamming signals, estimate jammer direction, and enable adaptive null-steering.

Time-Modulated Arrays (TMA) are incorporated to improve flexibility and effectiveness in suppressing both narrowband and broadband jammers, including scenarios with multiple simultaneous interferers. The framework will be validated through MATLAB simulations and, where possible, software-defined radio (SDR) experiments, contributing to improved wireless security and performance.