Reconfigurable intelligent surfaces (RISs) are recognized with great potential to strengthen wireless security, yet the performance gain largely depends on the deployment location of RISs in the network topology. In this paper, we consider the anti-eavesdropping communication established through a RIS at a fixed location, as well as an aerial platform mounting another RIS and a friendly jammer to further improve the secrecy. The aerial RIS helps enhance the legitimate signal and the aerial cooperative jamming is strengthened through the fixed RIS. The security gain with aerial reflection and jamming is further improved with the optimized deployment of the aerial platform. We particularly consider the imperfect channel state information issue and address the worst-case secrecy for robust performance. The formulated robust secrecy rate maximization problem is decomposed into two layers, where the inner layer solves for reflection and jamming with robust optimization, and the outer layer tackles the aerial deployment through deep reinforcement learning. Simulation results show the deployment under different network topologies and demonstrate the performance superiority of our proposal in terms of the worst-case security provisioning as compared with the baselines.

翻译：可重构智能表面（RIS）因其在增强无线安全方面的巨大潜力而受到认可，然而其性能增益很大程度上取决于RIS在网络拓扑中的部署位置。本文考虑通过固定位置RIS建立的反窃听通信，并引入搭载另一RIS与友好干扰机的空中平台以进一步提升保密性。空中RIS有助于增强合法信号，而空中协同干扰则通过固定RIS得到增强。通过优化空中平台的部署位置，空中反射与干扰带来的安全增益可进一步提升。我们特别考虑非完美信道状态信息问题，并针对鲁棒性能处理最坏情况下的保密性。所构建的鲁棒保密率最大化问题被分解为两层：内层通过鲁棒优化求解反射与干扰方案，外层通过深度强化学习处理空中部署问题。仿真结果展示了不同网络拓扑下的部署方案，并证明了与基线方法相比，本方案在最坏情况安全防护方面的性能优越性。