Mobile Jamming Mitigation in 5G Networks: A MUSIC-Based Adaptive Beamforming Approach
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908659889471488 |
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| author | Holguin, Olivia Donati, Rachel Natanzi, Seyed bagher Hashemi Tang, Bo |
| author_facet | Holguin, Olivia Donati, Rachel Natanzi, Seyed bagher Hashemi Tang, Bo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08046 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mobile Jamming Mitigation in 5G Networks: A MUSIC-Based Adaptive Beamforming Approach Holguin, Olivia Donati, Rachel Natanzi, Seyed bagher Hashemi Tang, Bo Networking and Internet Architecture Machine Learning Signal Processing 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. |
| title | Mobile Jamming Mitigation in 5G Networks: A MUSIC-Based Adaptive Beamforming Approach |
| topic | Networking and Internet Architecture Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2505.08046 |