Deep Learning Empowered Secure RIS-Assisted Non-Terrestrial Relay Networks

This paper proposes a secure transmission in reconfigurable intelligent surfaces (RIS) aided non-terrestrial cooperative networks (NTCN), where the practical phase-dependent model is considered in which the RIS reflection amplitudes change with the corresponding discrete phase shifts. Moreover, we employ a full-duplex transmission scheme at the relay nodes to reduce the long-range signal loss and improve the security between the satellite and the relay node. To solve the complex non-convex optimization problem of the joint RIS reflection coefficient and relay selection optimization, we propose the deep cascade correlation learning (DCCL) algorithm to enhance optimization efficiency. Simulation results show that the proposed DCCL-based method significantly improves the secrecy capacity compared to the random relay selection and RIS coefficient methods.