用动态图神经网络检测蜂窝式无小区系统中的干扰攻击
Jamming Detection in Cell-Free MIMO with Dynamic Graphs
- 将网络建模为动态图，捕捉通信链路变化
- 结合GCN与Transformer，准确识别恶意干扰
- 适用于移动用户和复杂信道环境的实时检测
干扰攻击对无线网络构成严重威胁，尤其在分布式接入点与用户设备（UE）构成复杂时变拓扑的无小区大规模MIMO系统中。本文提出一种基于动态图与图卷积神经网络（GCN）的新型干扰检测框架。通过将网络建模为动态图，捕捉通信链路的演化过程，并将干扰攻击视为图结构演变中的异常。采用基于GCN-Transformer的模型，通过监督学习训练图嵌入，实现对恶意干扰的识别。在模拟场景中评估性能，涵盖移动用户、多变干扰条件及信道衰落情况，结果以准确率和F1分数衡量，表现出优异的检测效果。
原文摘要 · Abstract (English)
Jamming attacks pose a critical threat to wireless networks, particularly in cell-free massive MIMO systems, where distributed access points and user equipment (UE) create complex, time-varying topologies. This paper proposes a novel jamming detection framework leveraging dynamic graphs and graph convolutional neural networks (GCN) to address this challenge. By modeling the network as a dynamic graph, we capture evolving communication links and detect jamming attacks as anomalies in the graph evolution. A GCN-Transformer-based model, trained with supervised learning, learns graph embeddings to identify malicious interference. Performance evaluation in simulated scenarios with moving UEs, varying jamming conditions and channel fadings, demonstrates the method's effectiveness, which is assessed through accuracy and F1 score metrics, achieving promising results for effective jamming detection.
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。