Mobile Jamming Mitigation in 5G Networks: A MUSIC-Based Adaptive Beamforming Approach
This addresses security for 5G networks, particularly in military communications, by mitigating mobile jammers, though it appears incremental as it combines existing techniques with ML enhancements.
The paper tackles mobile jamming threats in 5G networks by proposing an intelligent anti-jamming framework that integrates MUSIC for DoA estimation, MVDR beamforming, and machine learning. The approach achieves an average SNR improvement of 9.58 dB and up to 99.8% DoA estimation accuracy in simulations.
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.