Technology of Network & Communication
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 12
3772-3777

Distributed reinforcement learning based power control for frequency division multiple access systems

Li Ye
Si Ke
School of Optical-Electrical & Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2023.03.0169

Abstract

In recent years, deep reinforcement learning has been used as a model-free resource allocation method to solve the problem of co-channel interference in wireless networks. However, networks based on conventional experience replay strategies are difficult to learn valuable experiences, resulting in slower convergence speed. The manual method of determining the exploration step size does not take into account the learning situation of the algorithm in each training cycle, resulting in blind exploration of the environment and limited improvement of the system spectral efficiency. This paper proposed a distributed reinforcement learning power control method for frequency division multiple access systems, which adopted a priority experience replay strategy to encourage agents to learn more important data from the environment to accelerate the learning process. Moreover, this paper designed an exploration strategy with dynamic adjustment of step size suitable for distributed reinforcement learning. The strategy allowed agents to explore the local environment based on their own learning situation and hence reduced the blindness caused by manually setting step sizes. The experimental results show that compared to existing algorithms, the proposed method accelerates the convergence speed, improves the ability of co-channel interference suppression in mobile scenarios, and gains higher performance in large networks.

Foundation Support

华为技术有限公司合作资助项目(YBN2019115054)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.03.0169
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 12
Section: Technology of Network & Communication
Pages: 3772-3777
Serial Number: 1001-3695(2023)12-039-3772-06

Publish History

[2023-07-12] Accepted Paper
[2023-12-05] Printed Article

Cite This Article

李烨, 司轲. 频分多址系统分布式强化学习功率控制方法 [J]. 计算机应用研究, 2023, 40 (12): 3772-3777. (Li Ye, Si Ke. Distributed reinforcement learning based power control for frequency division multiple access systems [J]. Application Research of Computers, 2023, 40 (12): 3772-3777. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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