Distributed interference coordination based on multi-agent deep reinforcement learning

被引：0
|
作者：
Liu T. [1 ]
Luo Y. [1 ]
Yang C. [1 ]
机构：
[1] School of Electronic and Information Engineering, Beihang University, Beijing
来源：
Tongxin Xuebao/Journal on Communications | 2020年 / 41卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Distributed in-terference coordination; Multi-agent deep reinforcement learning; Non-realtime traffic; Ultra-dense network;
D O I：
10.11959/j.issn.1000-436x.2020149
中图分类号：
学科分类号：
摘要：
A distributed interference coordination strategy based on multi-agent deep reinforcement learning was investigated to meet the requirements of file downloading traffic in interfe-rence networks. By the proposed strategy transmission scheme could be adjusted adaptive-ly 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. © 2020, Editorial Board of Journal on Communications. All right reserved.
引用
收藏
页码：38 / 48
页数：10
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