This paper proposes a data-and model-driven col-laborative resource scheduling method to maximize the spec-tral efficiency(SE)of cell-free(CF)downlink multiuser multiple-input multiple-output(MIMO)systems,subject to d...This paper proposes a data-and model-driven col-laborative resource scheduling method to maximize the spec-tral efficiency(SE)of cell-free(CF)downlink multiuser multiple-input multiple-output(MIMO)systems,subject to delay violation probability and power constraints.The method integrates the weighted minimum mean square error(WMMSE)algorithm within the safety reinforcement learn-ing(Safety-RL)framework.The original optimization problem is decomposed into two coupled subproblems.The Safety-RL algorithm leverages state features to determine user priority weights and allocate bandwidths,while the WMMSE algorithm calculates the precoding matrix and fur-ther schedules resources based on user priority weights to obtain the reward and costs of Safety-RL.Considering dy-namic user access in CF systems,a distributed algorithm with user scalability is also proposed.Simulation results demonstrate that the proposed approach improves the SE while meeting the different delay violation probability con-straints of users.Furthermore,the distributed algorithm of-fers comparable performance to the fully centralized method while considerably reducing model training overhead,par-ticularly as users dynamically access the system.展开更多
基金The National Natural Science Foundation of China (No. 62271140, 62225107)the Natural Science Foundation of Jiangsu Province (No. BK20240174)+1 种基金the Fundamental Research Funds for the Central Universities (No. 2242022k60002)the Fund of Jiangsu Provincial Scientific Research Center of Applied Mathematics (No. BK20233002)。
文摘This paper proposes a data-and model-driven col-laborative resource scheduling method to maximize the spec-tral efficiency(SE)of cell-free(CF)downlink multiuser multiple-input multiple-output(MIMO)systems,subject to delay violation probability and power constraints.The method integrates the weighted minimum mean square error(WMMSE)algorithm within the safety reinforcement learn-ing(Safety-RL)framework.The original optimization problem is decomposed into two coupled subproblems.The Safety-RL algorithm leverages state features to determine user priority weights and allocate bandwidths,while the WMMSE algorithm calculates the precoding matrix and fur-ther schedules resources based on user priority weights to obtain the reward and costs of Safety-RL.Considering dy-namic user access in CF systems,a distributed algorithm with user scalability is also proposed.Simulation results demonstrate that the proposed approach improves the SE while meeting the different delay violation probability con-straints of users.Furthermore,the distributed algorithm of-fers comparable performance to the fully centralized method while considerably reducing model training overhead,par-ticularly as users dynamically access the system.
