移动通信2026，Vol.50Issue(6)：62-70,9.DOI:10.3969/j.issn.1006-1010.20260403-0003
基于深度强化学习的耦合相移STAR-RIS辅助ISAC系统联合优化方法
Deep Reinforcement Learning-Based Joint Optimization for Coupled-Phase STAR-RIS-Assisted ISAC Systems
摘要
Abstract
To address the problems of limited half-space coverage in conventional reconfigurable intelligent surface(RIS)-assisted integrated sensing and communication(ISAC)systems,as well as the severe coupling between communication and sensing resources and the difficulty of joint optimization in dynamic scenarios,this paper proposes a deep reinforcement learning-based joint optimization method for coupled phase-shift STAR-RIS-assisted ISAC systems.First,a STAR-RIS-assisted ISAC system model considering the mobility of sensing targets is established.Under the constraints of base station transmit power,communication quality of service,radar sensing threshold,and STAR-RIS energy conservation and coupled phase-shift,a joint optimization problem is formulated to maximize the weighted sum of communication rate and sensing rate.Furthermore,the problem is modeled as a Markov decision process,and a joint optimization strategy based on the soft actor-critic algorithm is proposed to realize the adaptive collaborative design of base station beamforming and STAR-RIS coefficients.Meanwhile,the receive filter optimization problem is transformed into a Rayleigh quotient problem.Simulation results show that the proposed method outperforms benchmark schemes in terms of both convergence speed and overall communication-sensing performance.关键词
透反射智能超表面/通感一体化/深度强化学习/资源分配Key words
simultaneously transmitting and reflecting reconfigurable intelligent surface/integrated sensing and communication/deep reinforcement learning/resource allocation分类
信息技术与安全科学引用本文复制引用
杨冬东,张晓宇,何继光,蔡国发,李斌..基于深度强化学习的耦合相移STAR-RIS辅助ISAC系统联合优化方法[J].移动通信,2026,50(6):62-70,9.基金项目
高层次留学人才回国资助项目(RCXMA26001) （RCXMA26001）
广东省普通高校创新团队项目-融合智能计算的通信与感知研究创新团队(2024KCXTD047) （2024KCXTD047）
