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

Olivia Holguin, Rachel Donati, Seyed bagher Hashemi Natanzi, Bo Tang

TL;DR

The paper tackles the vulnerability of 5G networks to mobile jammers by proposing an integrated framework that combines MUSIC-based DoA estimation with MVDR beamforming, augmented by a lightweight linear-regression refinement to cope with jammer mobility. Empirical results in a simulated highway setting show DoA accuracy up to 99.8% and an average SNR improvement of 9.58 dB, with real-time operation demonstrated through measured processing times. The work highlights the robustness and efficiency of the MUSIC-MVDR baseline, while finding that the additional simple refinement offers only modest gains, suggesting avenues for exploring more powerful predictors and multi-jammer scenarios. Overall, the approach provides a practical, low-complexity solution for securing 5G communications in contested environments and informs the design of anti-jamming strategies for mobile networks.

Abstract

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.

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

TL;DR

The paper tackles the vulnerability of 5G networks to mobile jammers by proposing an integrated framework that combines MUSIC-based DoA estimation with MVDR beamforming, augmented by a lightweight linear-regression refinement to cope with jammer mobility. Empirical results in a simulated highway setting show DoA accuracy up to 99.8% and an average SNR improvement of 9.58 dB, with real-time operation demonstrated through measured processing times. The work highlights the robustness and efficiency of the MUSIC-MVDR baseline, while finding that the additional simple refinement offers only modest gains, suggesting avenues for exploring more powerful predictors and multi-jammer scenarios. Overall, the approach provides a practical, low-complexity solution for securing 5G communications in contested environments and informs the design of anti-jamming strategies for mobile networks.

Abstract

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.
Paper Structure (10 sections, 7 equations, 5 figures, 2 tables, 1 algorithm)

This paper contains 10 sections, 7 equations, 5 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Proposed Framework
  4. Anti-Jamming System
  5. Performance Quantification
  6. Experiment
  7. Setup
  8. Results
  9. Discussion
  10. Conclusion

Figures (5)

  • Figure 1: Simulated highway scenario illustrating the interaction between a stationary transmitter (Tx), a mobile receiver (Rx), and a mobile jammer (Jammer), designed to evaluate dynamic anti-jamming performance.
  • Figure 2: Optimized linear regression model showing the relationship between predicted and actual azimuth angles with 95% confidence bounds.
  • Figure 3: Block diagram of the proposed MUSIC-MVDR-ML framework showing the step-by-step mitigation process.
  • Figure 4: Example MVDR beam pattern demonstrating effective null steering towards the jammer direction (e.g., at 30$^{\circ}$) while maintaining gain towards the desired signal (e.g., at -40$^{\circ}$).
  • Figure 5: SNR improvement achieved by the proposed MUSIC-MVDR-ML framework along a representative highway trajectory, showcasing consistent enhancement despite jammer mobility.