Hybrid IRS-Assisted Secure Satellite Downlink Communications: A Fast Deep Reinforcement Learning Approach

• Published in: IEEE transactions on emerging topics in computational intelligence Vol. 8; no. 4; pp. 2858 - 2869
• Main Authors: Ngo, Quynh Tu, Phan, Khoa Tran, Mahmood, Abdun, Xiang, Wei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Array signal processing; Beamforming; Communications systems; Convergence; Deep learning; Downlink; Downlinking; fast reinforcement learning; Hybrid IRS; Hybrid power systems; Hybrid systems; Machine learning; MISO communication; Optimization; physical layer security; Reconfigurable intelligent surfaces; Robust design; satellite downlink communications; Satellites; System dynamics; Vectors
• ISSN: 2471-285X; 2471-285X
• DOI: 10.1109/TETCI.2024.3378605

This paper considers a secure satellite downlink communication system with a hybrid intelligent reflecting surface (IRS). A robust design problem for the satellite and IRS joint beamforming is formulated to maximize the system's worst-case secrecy rate, considering practical models of the outdated channel state information and IRS power consumption. We leverage deep reinforcement learning (DRL) to solve the problem by proposing a fast DRL algorithm, namely the deep post-decision state-deterministic policy gradient (DPDS-DPG) algorithm. In DPDS-DPG, the prior known system dynamics are exploited by integrating the PDS concept into the traditional deep DPG (DDPG) algorithm, resulting in faster learning convergence. Simulation results show a faster learning convergence of 50% for DPDS-DPG compared to DDPG, with a comparable achievable system secrecy rate. Additionally, the results demonstrate system secrecy rate gains of 52% and 35% when employing active IRS and hybrid IRS, respectively, over conventional passive IRS, thereby supporting secure communications.