5G网络中的移动干扰缓解：一种基于MUSIC的自适应波束成形方法
Mobile Jamming Mitigation in 5G Networks: A MUSIC-Based Adaptive Beamforming Approach
AI总结：
针对移动干扰器对5G网络的威胁，提出集成MUSIC、MVDR波束成形与机器学习的智能抗干扰框架，仿真显示其平均信噪比提升9.58dB，DoA估计精度达99.8%，优于传统技术。
摘要：
移动干扰器对5G网络构成严重威胁，尤其在军事通信中。我们提出一种智能抗干扰框架，集成多信号分类（MUSIC）用于高分辨率波达方向（DoA）估计、最小方差无失真响应（MVDR）波束成形用于自适应干扰抑制，以及机器学习（ML）以增强移动干扰器的DoA预测。在真实高速公路场景下的大量仿真表明，我们的混合方法实现了平均9.58 dB（最大11.08 dB）的信噪比（SNR）提升，以及高达99.8%的DoA估计精度。该框架的计算效率和对动态干扰器移动模式的适应性优于传统抗干扰技术，使其成为竞争环境中保障5G通信安全的稳健解决方案。
英文摘要：
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.
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