用智能波束成形提升5G抗移动干扰能力，显著改善信号质量。
Mobile Jamming Mitigation in 5G Networks: A MUSIC-Based Adaptive Beamforming Approach
- 融合MUSIC算法与机器学习，精准定位干扰源方向。
- 实测平均信噪比提升9.58分贝，方向估计准确率达99.8%。
- 适合军事通信等复杂动态干扰场景的实时抗干扰需求。
移动干扰对5G网络构成严重威胁，尤其在军事通信中。本文提出一种智能抗干扰框架，结合多重信号分类（MUSIC）实现高分辨率到达方向（DoA）估计，最小方差无失真响应（MVDR）波束成形实现自适应干扰抑制，并引入机器学习增强对移动干扰源的DoA预测能力。在真实高速公路场景下的大量仿真表明，该混合方法平均信噪比提升9.58分贝（最高达11.08分贝），方向估计准确率高达99.8%。该框架计算效率高，能有效适应动态干扰移动模式，优于传统抗干扰技术，是保障对抗环境中5G通信安全的可靠解决方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。