Quynh Tu Ngo;Khoa Tran Phan;Abdun Mahmood;Wei Xiang
{"title":"混合 IRS 辅助安全卫星下行链路通信:快速深度强化学习方法","authors":"Quynh Tu Ngo;Khoa Tran Phan;Abdun Mahmood;Wei Xiang","doi":"10.1109/TETCI.2024.3378605","DOIUrl":null,"url":null,"abstract":"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joint beamforming is formulated to maximize the system's worst-case secrecy rate, considering practical models of the outdated channel state information and IRS power consumption.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.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"2858-2869"},"PeriodicalIF":5.3000,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid IRS-Assisted Secure Satellite Downlink Communications: A Fast Deep Reinforcement Learning Approach\",\"authors\":\"Quynh Tu Ngo;Khoa Tran Phan;Abdun Mahmood;Wei Xiang\",\"doi\":\"10.1109/TETCI.2024.3378605\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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joint beamforming is formulated to maximize the system's worst-case secrecy rate, considering practical models of the outdated channel state information and IRS power consumption.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.\",\"PeriodicalId\":13135,\"journal\":{\"name\":\"IEEE Transactions on Emerging Topics in Computational Intelligence\",\"volume\":\"8 4\",\"pages\":\"2858-2869\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Emerging Topics in Computational Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10483088/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10483088/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Hybrid IRS-Assisted Secure Satellite Downlink Communications: A Fast Deep Reinforcement Learning Approach
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.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.