Distributed interference coordination based on multi-agent deep reinforcement learning

Tingting LIU; Yi’nan LUO; Chenyang YANG

doi:10.11959/j.issn.1000-436x.2020149

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Distributed interference coordination based on multi-agent deep reinforcement learning

Topics: Mobile AI | 更新时间：2024-06-05

- Distributed interference coordination based on multi-agent deep reinforcement learning
- Journal on Communications Vol. 41, Issue 7, Pages: 38-48(2020)
- 作者机构：
  
  北京航空航天大学电子信息工程学院，北京 100191
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2020149
  CLC： TN929.53
- Online First：2020-07，
  
  Published：25 July 2020
- 稿件说明：
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Tingting LIU, Yi’nan LUO, Chenyang YANG. Distributed interference coordination based on multi-agent deep reinforcement learning[J]. Journal on Communications, 2020, 41(7): 38-48.
DOI：

Tingting LIU, Yi’nan LUO, Chenyang YANG. Distributed interference coordination based on multi-agent deep reinforcement learning[J]. Journal on Communications, 2020, 41(7): 38-48. DOI： 10.11959/j.issn.1000-436x.2020149.

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%。

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.

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references

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