Mobile Jamming Mitigation in 5G Networks: A MUSIC-Based Adaptive Beamforming Approach
Mobile Jamming Mitigation in 5G Networks: A MUSIC-Based Adaptive Beamforming Approach
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.
Olivia Holguin、Rachel Donati、Seyed bagher Hashemi Natanzi、Bo Tang
无线电设备、电信设备电子对抗通信无线通信
Olivia Holguin,Rachel Donati,Seyed bagher Hashemi Natanzi,Bo Tang.Mobile Jamming Mitigation in 5G Networks: A MUSIC-Based Adaptive Beamforming Approach[EB/OL].(2025-05-12)[2025-07-21].https://arxiv.org/abs/2505.08046.点此复制
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