Mobile Jamming Mitigation in 5G Networks: A MUSIC-Based Adaptive Beamforming Approach

TL;DR

This paper presents a hybrid anti-jamming framework for 5G networks using MUSIC-based DoA estimation, MVDR beamforming, and machine learning, significantly improving signal quality and jammer detection accuracy in dynamic scenarios.

Contribution

It introduces an integrated approach combining MUSIC, MVDR, and ML for adaptive anti-jamming in 5G, outperforming traditional methods in accuracy and efficiency.

Findings

Achieved an average SNR improvement of 9.58 dB.

Up to 99.8% DoA estimation accuracy.

Outperformed conventional anti-jamming techniques.

Abstract

Mobile jammers pose a critical threat to 5G networks, particularly in military communications. We propose an intelligent anti-jamming framework that integrates Multiple Signal Classification (MUSIC) for high-resolution Direction-of-Arrival (DoA) estimation, Minimum Variance Distortionless Response (MVDR) beamforming for adaptive interference suppression, and machine learning (ML) to enhance DoA prediction for mobile jammers. Extensive simulations in a realistic highway scenario demonstrate that our hybrid approach achieves an average Signal-to-Noise Ratio (SNR) improvement of 9.58 dB (maximum 11.08 dB) and up to 99.8% DoA estimation accuracy. The framework's computational efficiency and adaptability to dynamic jammer mobility patterns outperform conventional anti-jamming techniques, making it a robust solution for securing 5G communications in contested environments.