Local Observations-Based Energy-Efficient Multi-Cell Beamforming via Multi-Agent Reinforcement Learning  

作　　者：Kaiwen Yu Gang Wu Shaoqian Li Geoffrey Ye Li 

机构地区：[1]The National Key Laboratory of Science and Technology on Communications,University of Electronic Science and Technology of China,Chengdu 611731,China [2]Imperial College London.London SW72AZ,U.K.

出　　处：《Journal of Communications and Information Networks》2022年第2期170-180,共11页通信与信息网络学报（英文）

基　　金：Fundamental Research Funds for the Central Universities(ZYGX2020ZB042)。

摘　　要：With affordable overhead on information exchange,energy-efficient beamforming has potential to achieve both low power consumption and high spectral efficiency.This paper formulates the problem of joint beamforming and power allocation for a multiple-input single-output(MISO)multi-cell network with local observations by taking the energy efficiency into account.To reduce the complexity of joint processing of received signals in presence of a large number of base station(BS),a new distributed framework is proposed for beamforming with multi-cell cooperation or competition.The optimization problem is modeled as a partially observable Markov decision process(POMDP)and is solved by a distributed multi-agent self-decision beamforming(DMAB)algorithm based on the distributed deep recurrent Q-network(D2RQN).Furthermore,limited-information exchange scheme is designed for the inter-cell cooperation to boost the global performance.The proposed learning architecture,with considerably less information exchange,is effective and scalable for a high-dimensional problem with increasing BSs.Also,the proposed DMAB algorithms outperform distributed deep Q-network(DQN)based methods and non-learning based methods with significant performance improvement.

关 键 词：distributed beamforming energy efficiency deep reinforcement learning interference-cooperation POMDP 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]