Spectrum allocation algorithm based on multi-agent reinforcement learning in smart grid

被引：0
|
作者：
Yan F. [1 ]
Lin X. [2 ]
Li Z. [3 ]
Xu X. [4 ]
Xia W. [1 ]
Shen L. [1 ]
机构：
[1] National Mobile Communications Research Laboratory, Southeast University, Nanjing
[2] School of Software, Southeast University, Nanjing
[3] State Grid Shandong Information and Telecommunication Company, Jinan
[4] State Grid Jinan Power Supply Company, Jinan
来源：
Tongxin Xuebao/Journal on Communications | 2023年 / 44卷 / 09期
关键词：
integrated access and backhaul; multi-agent reinforcement learning; smart grid; spectrum allocation;
D O I：
10.11959/j.issn.1000-436x.2023179
中图分类号：
学科分类号：
摘要：
In view of the fact that 5G networks are used to meet the service requirements of various power terminals in smart grid, a spectrum allocation algorithm based on multi-agent reinforcement learning was proposed. Firstly, for the integrated access backhaul system deployed in smart grid, considering the different communication requirements of services in lightweight and non-lightweight terminal, the spectrum allocation problem was formulated as a non-convex mixed-integer programming aiming to maximize the overall energy efficiency. Secondly, the above problem was modeled as a partially observable Markov decision process and transformed into a fully cooperative multi-agent problem, then a spectrum allocation algorithm was proposed which was based on multi-agent proximal policy optimization under the framework of centralized training and distributed execution. Finally, the performance of the proposed algorithm was verified by simulation. The results show that the proposed algorithm has a faster convergence speed and can increase the overall transmission rate by 25.2% through effectively reducing intra-layer and inter-layer interference and balancing the access and backhaul link rates. © 2023 Editorial Board of Journal on Communications. All rights reserved.
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收藏
页码：12 / 24
页数：12
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