典型文献
Distributed Multi-Cell Multi-User MISO Downlink Beamforming via Deep Reinforcement Learning
文献摘要：
The sum rate maximization beamforming problem for a multi-cell multi-user multiple-input single-output interference channel (MISO-IC) system is considered. Conventionally, the centralized and distributed beamforming solutions to the MISO-IC system have high com?putational complexity and bear a heavy burden of channel state information exchange between base stations (BSs), which becomes even much worse in a large-scale antenna system. To address this, we propose a distributed deep reinforcement learning (DRL) based approach with lim?ited information exchange. Specifically, the original beamforming problem is decomposed of the problems of beam direction design and power allocation and the costs of information exchange between BSs are significantly reduced. In particular, each BS is provided with an inde?pendent deep deterministic policy gradient network that can learn to choose the beam direction scheme and simultaneously allocate power to users. Simulation results illustrate that the proposed DRL-based approach has comparable sum rate performance with much less information exchange over the conventional distributed beamforming solutions.
文献关键词：
中图分类号：
作者姓名：
JIA Haonan;HE Zhenqing;TAN Wanlong;RUI Hua;LIN Wei
作者机构：
NationalKey Laboratory of Science and Technology on Communica?tions,University of Electronic Science and Technology of China,Chengdu 611731,China;ZTE Corporation,Shenzhen 518057,China;State Key Laboratory of Mobile Network and Mobile Multimedia Tech?nology,Shenzhen 518055,China
文献出处：
引用格式：
[1]JIA Haonan;HE Zhenqing;TAN Wanlong;RUI Hua;LIN Wei-.Distributed Multi-Cell Multi-User MISO Downlink Beamforming via Deep Reinforcement Learning)[J].中兴通讯技术（英文版）,2022(04):69-77
A类：
B类：
Distributed,Multi,Cell,User,MISO,Downlink,Beamforming,via,Deep,Reinforcement,Learning,sum,maximization,beamforming,cell,multiple,input,single,output,interference,channel,IC,system,considered,Conventionally,centralized,distributed,solutions,have,high,putational,complexity,bear,heavy,burden,state,information,exchange,between,stations,BSs,which,becomes,even,much,worse,large,scale,antenna,To,address,this,deep,reinforcement,learning,DRL,approach,lim,ited,Specifically,original,decomposed,problems,direction,design,power,allocation,costs,are,significantly,reduced,In,particular,each,provided,inde,pendent,deterministic,policy,gradient,network,that,choose,scheme,simultaneously,allocate,users,Simulation,results,illustrate,proposed,has,comparable,performance,less,over,conventional
AB值：
0.627188
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