6G无线系统中用强化学习对抗城市环境下的恶意波束攻击
6G Sensing Security: Distributed Game-Theoretic RL for Urban Beamforming and Attacker Detection
- 用博弈论建模合法用户与攻击者互动，结合强化学习优化波束成形
- 仿真显示该方法能有效识别并抵御恶意干扰，提升系统安全性
- 适合研究6G安全、智能波束成形或对抗性无线系统的学者
在下一代网络中，通信系统将不仅传输数据，还将感知周围环境。这催生了集成感知与通信（ISAC）概念，即利用同一无线基础设施同时实现通信与环境感知。因此，ISAC使系统能够高效传输信息，并观测和解析信道变化及用户行为。本文聚焦于城市环境中检测主动攻击者的问题，攻击者会故意操纵波束成形方向以增加干扰，并诱骗发射端将主波束指向自身而非合法用户。我们采用博弈论方法建模合法用户与攻击者之间的交互，并将所得的效用函数整合进强化学习框架。仿真结果表明，所提方法能有效应对动态6G ISAC系统中的安全挑战。
原文摘要 · Abstract (English)
In next-generation networks, communication systems will no longer be limited to data transmission and will be expected to acquire awareness of the surrounding environment. This leads to the concept of integrated sensing and communication (ISAC), where the same wireless infrastructure is used for both communication and environmental sensing. Thus, ISAC enables the system to transmit information efficiently and observe and interpret channel variations and user behavior. Motivated by this capability, this work focuses on detecting an active attacker in an urban environment scenario, where the attacker intentionally manipulates beamforming directions to increase interference and mislead the transmitter into allocating the main lobe of beam toward itself instead of legitimate users. We apply game-theoretic approaches to model the interaction between legitimate users and the attacker, and integrate the resulting utility-based formulation into a reinforcement learning (RL) framework. Simulation results demonstrate that the proposed method effectively addresses security challenges in dynamic 6G ISAC systems.
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。