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

Abstract--Mobile jammers pose a critical threat to 5G networks, particularly in military communications. This paper investigates an anti-jamming framework that enhances a strong adaptive beamforming baseline comprising Multiple Signal Classification (MUSIC) for Direction-of-Arrival (DoA) estimation and Minimum V ariance Distortionless Response (MVDR) for interference suppression with a lightweight machine learning (ML) model for predictive error correction. Extensive simulations in a realistic highway scenario demonstrate that the integrated system achieves a high DoA estimation accuracy of up to 99.8% and an average Signal-to-Noise Ratio (SNR) improvement of 9.58 dB. Analysis reveals that the MUSIC-MVDR baseline alone accounts for the vast majority of this performance gain (9.46 dB), indicating that the primary benefit of the simple ML model lies in correcting outlier estimates rather than providing a substantial systemic SNR increase. The framework's computational efficiency validates the effectiveness of the core beamforming approach and highlights the critical trade-off between ML model complexity and practical performance gains for securing 5G communications in contested environments.