移动通信2025，Vol.49Issue(4)：20-27,8.DOI:10.3969/j.issn.1006-1010.20250109-0002
基于深度强化学习的多功能超表面辅助无蜂窝网络资源分配
Deep Reinforcement Learning-Based Resource Allocation for Multi-Functional RIS-Assisted Cell-Free Networks
摘要
Abstract
In recent years,the emerging cell-free network architecture has eliminated cell boundaries defined by time-frequency resources,thereby enhancing spectrum utilization,system coverage,and user fairness.It is regarded as a potential key technology for the sixth-generation mobile communications.However,implementing such an architecture typically requires the large-scale deployment of access points(APs),resulting in high system costs and power consumption.Moreover,the achievable performance of cell-free networks is often constrained by dynamic and uncontrollable signal propagation environments.To address these issues,this paper introduces the multi-functional reconfigurable intelligent surface(MF-RIS)into cell-free networks.By simultaneously reflecting,refracting,and amplifying incident signals,MF-RIS can reconfigure the wireless propagation environment of cell-free networks in an effective manner.To maximize the minimum achievable rate at the user side,this paper formulates a joint scheduling problem involving AP beamforming,and MF-RIS mode selection and coefficient optimization.To solve this mixed-integer nonlinear programming problem,this paper proposes a dual-agent algorithm based on deep reinforcement learning,where one agent determines the operating mode of MF-RIS elements,and the other designs the beamforming at APs and MF-RIS coefficients based on the selected modes.Simulation results demonstrate the effectiveness of the proposed algorithm.关键词
多功能超表面/无蜂窝网络/深度强化学习/资源管理Key words
multi-functional reconfigurable intelligent surface/cell-free network/deep reinforcement learning/resource management分类
电子信息工程引用本文复制引用
王雯,倪万里,魏昊,张铖,黄永明..基于深度强化学习的多功能超表面辅助无蜂窝网络资源分配[J].移动通信,2025,49(4):20-27,8.基金项目
国家资助博士后研究人员计划(GZB20240386) （GZB20240386）
中国博士后科学基金资助项目(2024M761669) （2024M761669）
北京邮电大学博士研究生创新基金项目(CX2023110) （CX2023110）
国家自然科学基金面上项目(62271140) （62271140）
江苏省优秀青年科学基金项目(BK20240174) （BK20240174）
