Heuristic Deep Reinforcement Learning for Phase Shift Optimization in RIS-assisted Secure Satellite Communication Systems with RSMA

Abstract

This paper presents a novel heuristic deep reinforcement learning (HDRL) framework designed to optimize reconfigurable intelligent surface (RIS) phase shifts in secure satellite communication systems utilizing rate splitting multiple access (RSMA). The proposed HDRL approach addresses the challenges of large action spaces inherent in deep reinforcement learning by integrating heuristic algorithms, thus improving exploration efficiency and leading to faster convergence toward optimal solutions. We validate the effectiveness of HDRL through comprehensive simulations, demonstrating its superiority over traditional algorithms, including random phase shift, greedy algorithm, exhaustive search, and Deep Q-Network (DQN), in terms of secure sum rate and computational efficiency. Additionally, we compare the performance of RSMA with non-orthogonal multiple access (NOMA), highlighting that RSMA, particularly when implemented with an increased number of RIS elements, significantly enhances secure communication performance. The results indicate that HDRL is a powerful tool for improving the security and reliability of RSMA satellite communication systems, offering a practical balance between performance and computational demands.