Research Article
DRL Based Secure Optimization for RIS Aided SATINs with RSMA
@INPROCEEDINGS{10.1007/978-3-031-86196-3_7, author={Min Wu and Kefeng Guo and Zhi Lin and Huiyun Xia and Kang An and Liang Yang and Jiangzhou Wang}, title={DRL Based Secure Optimization for RIS Aided SATINs with RSMA}, proceedings={Wireless and Satellite Systems. 14th EAI International Conference, WiSATS 2024, Harbin, China, August 23--25, 2024, Proceedings, Part I}, proceedings_a={WISATS}, year={2025}, month={3}, keywords={Satellite aerial terrestrial integrated networks reconfigurable intelligent surface rate splitting multiple access deep reinforcement learning security}, doi={10.1007/978-3-031-86196-3_7} }- Min Wu
Kefeng Guo
Zhi Lin
Huiyun Xia
Kang An
Liang Yang
Jiangzhou Wang
Year: 2025
DRL Based Secure Optimization for RIS Aided SATINs with RSMA
WISATS
Springer
DOI: 10.1007/978-3-031-86196-3_7
Abstract
Amid the escalating demand for accessible users and security insurance in satellite aerial terrestrial integrated networks (SATINs), security and energy efficiency emerge as pivotal indicators. This paper proposes a secure beamforming scheme in reconfigurable intelligent surface (RIS) aided SATINs, in presence with multiple eavesdroppers, where rate splitting multiple access (RSMA) and RIS are adopted at the secondary UAV networks for achieving multiuser diversity and antijamming. To optimize the secrecy energy efficiency (SEE) for secondary networks while adhering to constraints on ground earth station (GES) secrecy rate, a deep reinforcement learning (DRL) framework is proposed to address the coupling between optimization variables through the improved proximal policy optimization (PPO) method, of which from existing DRL scheme is that the proposed one builds a unified learning framework. Simulation results indicate that the SEE derived by the proposed DRL scheme is superior to that of benchmark schemes, which validate the advantage of this work.
