Toward Secure and Reliable SAGIN: Learning-Driven Multi-Dimensional Resource Scheduling for Multi-RIS-Assisted OTFS Transmission

As a key component of the space-air-ground integrated network (SAGIN), low Earth orbit (LEO) satellites aim to provide global coverage and reliable services under high-speed mobile conditions, which are critically challenged by severe Doppler effect and inherent broadcast security threats. To address these issues, this paper investigates a multi-reconfigurable intelligent surface (RIS)-assisted orthogonal time frequency space (OTFS) downlink transmission system, where a LEO satellite serves multiple information receivers and potential eavesdroppers acting as energy receivers via simultaneous wireless information and power transfer (SWIPT). By jointly optimizing multi-dimensional resource variables, such as transmit beamforming and RIS reflection coefficients of the spatial domain, and the symbol scheduling matrix of the time-frequency domain, this paper aims to maximize the sum secrecy rate while satisfying constraints on satellite transmit power, the legitimate users’ quality of service, and energy-harvesting requirements. Given the high-dimensional, non-convex, and NP-hard nature of this problem, we develop an enhanced actor-critic deep reinforcement learning (DRL) framework. The core innovation lies in designing an episodic return-prioritized experience selection mechanism with online mixing, which significantly improves the sampling efficiency and policy stability by intelligently selecting training data. Simulation results demonstrate that the proposed approach outperforms existing schemes in achieving a higher sum secrecy rate, providing a practical and highly efficient resource scheduling solution for building secure and reliable next-generation SAGIN.