Learning-based reliable and secure transmission for UAV-RIS-assisted communication systems

Mounting reconfigurable intelligent surface (RIS) on unmanned aerial vehicle (UAV), called UAV-RIS, combines the benefits of these two techniques, which can further improve the communication performance. However, high-quality air-ground channel links are more vulnerable to both the adversarial eavesdropping and the malicious jamming. Therefore, this paper proposes a reliable and secure communication approach assisted by the UAV-RIS to maximize the secrecy rate, while ensuring the quality of service (QoS) requirement of the legitimate user against both the eavesdroppers and the jammer. Specifically, with the imperfect channel state information and behaviors of mixed attacks, we try to maximize the achievable worst-case secrecy rate by jointly designing the transmit beamforming, artificial noise, UAV-RIS placement, and RIS's passive beamforming. As the optimization problem is non-convex and the environment is highly dynamic, a post-decision state deep Q-network combined with Fourier feature mapping algorithm (called PDS-DQN-FFM) is further designed to effectively achieve the robust anti-attack transmission strategy. Simulation results demonstrate that our proposed learning based reliable and secure transmission approach significantly enhances both the secrecy rate and QoS satisfaction level as compared with existing approaches.