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.
@article{arxiv.2505.08046,
title = {Mobile Jamming Mitigation in 5G Networks: A MUSIC-Based Adaptive Beamforming Approach},
author = {Olivia Holguin and Rachel Donati and Seyed bagher Hashemi Natanzi and Bo Tang},
journal= {arXiv preprint arXiv:2505.08046},
year = {2025}
}