基于多智能体深度强化学习的分布式干扰协调  

Distributed interference coordination based on multi-agent deep reinforcement learning

作　　者：刘婷婷[1] 罗义南 杨晨阳[1] LIU Tingting;LUO Yi’nan;YANG Chenyang(School of Electronic and Information Engineering,Beihang University,Beijing 100191,China)

机构地区：[1]北京航空航天大学电子信息工程学院,北京100191

出　　处：《通信学报》2020年第7期38-48,共11页Journal on Communications

基　　金：国家自然科学基金资助项目(No.61731002,No.61671036)。

摘　　要：针对干扰网络中的文件下载业务,提出了一种基于多智能体深度强化学习的分布式干扰协调策略。所提策略能够在节点之间只需交互少量信息的条件下,根据干扰环境和业务需求的特点自适应调整传输策略。仿真结果表明,对于任意的用户数和业务需求,所提策略相对于未来信息预测理想时最优策略的用户满意度损失不超过11%。A distributed interference coordination strategy based on multi-agent deep reinforcement learning was investigated to meet the requirements of file downloading traffic in interference networks.By the proposed strategy transmission scheme could be adjusted adaptively based on the interference environment and traffic requirements with limited amount of information exchanged among nodes.Simulation results show that the user satisfaction loss of the proposed strategy from the optimal strategy with perfect future information does not exceed 11%for arbitrary number of users and traffic requirements.

关 键 词：多智能体深度强化学习 非实时业务 分布式干扰协调 超密集网络 

分 类 号：TN929.53[电子电信—通信与信息系统]