通信学报 ›› 2020, Vol. 41 ›› Issue (7): 38-48.doi: 10.11959/j.issn.1000-436x.2020149

• 专题:移动人工智能 • 上一篇    下一篇

基于多智能体深度强化学习的分布式干扰协调

刘婷婷,罗义南,杨晨阳   

  1. 北京航空航天大学电子信息工程学院,北京 100191
  • 修回日期:2020-03-07 出版日期:2020-07-25 发布日期:2020-08-01
  • 作者简介:刘婷婷(1982- ),女,陕西西安人,博士,北京航空航天大学副教授,主要研究方向为基于机器学习和无线大数据的干扰管理、资源规划和信息预测|罗义南(1995- ),男,辽宁丹东人,北京航空航天大学硕士生,主要研究方向为超密集网络中的分布式干扰协调|杨晨阳(1965- ),女,浙江杭州人,博士,北京航空航天大学教授、博士生导师,主要研究方向为基于机器学习、无线大数据的缓存、传输资源管理、超可靠低延时通信等
  • 基金资助:
    国家自然科学基金资助项目(61731002);国家自然科学基金资助项目(61671036)

Distributed interference coordination based on multi-agent deep reinforcement learning

Tingting LIU,Yi’nan LUO,Chenyang YANG   

  1. School of Electronic and Information Engineering,Beihang University,Beijing 100191,China
  • Revised:2020-03-07 Online:2020-07-25 Published:2020-08-01
  • Supported by:
    The National Natural Science Foundation of China(61731002);The National Natural Science Foundation of China(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%。

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

Abstract:

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.

Key words: multi-agent deep reinforcement learning, non-realtime traffic, distributed interference coordination, ultra-dense network

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