Technology of Network & Communication
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 7
2138-2145

Method of improving performance of congestion control in wireless Ad hoc network based on deep reinforcement learning

Chen Shihe
Xu Yanyan
Pan Shaoming
State Key Laboratory of Information Engineering for Surveying, Mapping & Remote Sensing, Wuhan University, Wuhan 430079, China
DOI: 10.19734/j.issn.1001-3695.2022.11.0775

Abstract

Most existing traditional congestion control algorithms are difficult to adapt to the highly dynamic link environment of wireless Ad hoc network. In order to solve the above problem, this paper proposed a method of improving the performance of congestion control based on deep reinforcement learning, Enhanced-CC. It conducted a preliminary detection of the congestion window by using the traditional congestion control algorithm. On this basis, the method used deep reinforcement technology to learn the real-time optimal congestion window range of the link. When the congestion window calculated by the traditional congestion control algorithm was too large or too small, the method adjusted the congestion window, so that the sending rate could match the highly dynamic link bandwidth, and the method could improve the transmission performance of the traditional congestion control algorithm. The experimental results show that Enhanced-CC can significantly improve the performance of traditional congestion control algorithms such as BBR, CUBIC, Westwood, Reno, and is superior to the performance of fully lear-ning based congestion control algorithms such as PCC and PCC Vivace, and the combination of deep reinforcement learning and traditional congestion control algorithms such as Orca and DeepCC.

Foundation Support

国家自然科学基金资助项目(41871312,42271425)
国家重点研发计划资助项目(2022YFB3903404,2022YFB3902804)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.11.0775
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 7
Section: Technology of Network & Communication
Pages: 2138-2145
Serial Number: 1001-3695(2023)07-033-2138-08

Publish History

[2023-02-13] Accepted Paper
[2023-07-05] Printed Article

Cite This Article

陈世河, 徐彦彦, 潘少明. 基于深度强化学习的无线自组网拥塞控制性能提升方法 [J]. 计算机应用研究, 2023, 40 (7): 2138-2145. (Chen Shihe, Xu Yanyan, Pan Shaoming. Method of improving performance of congestion control in wireless Ad hoc network based on deep reinforcement learning [J]. Application Research of Computers, 2023, 40 (7): 2138-2145. )

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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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