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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 2

Methods on intelligent jamming-strategy making based on reinforcement learning for wireless communication networks

Yang Xiangyu
Gao Zhenzhen
Miao Weijian
School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an Shaanxi 710049, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0216

Abstract

With increasing demands for cognitive capabilities and jamming decision technologies in modern military systems, jamming methods against communication equipment and combat networks with sensing and decision-making capacities face significant challenges. To address the limitations of traditional jamming schemes in non-cooperative scenarios, this study proposed a jamming decision-making method based on reinforcement learning. The method employed the Multi-Armed Bandit Upper Confidence Bound (MAB-UCB) algorithm. It jointly optimized the jammer’s position, emission angle, and power. By formulating a utility function that integrated power cost and jamming effect, adaptive learning of jamming strategies was achieved under constraints such as safe distance and maximum power. Simulation results demonstrate that, without prior information of the communication network, the proposed method achieves 92.5% of the utility of the optimal jamming strategy. This optimal strategy requires known prior information. The method also attains a communication transmission blocking rate of 0.6753. Furthermore, it exhibits strong adaptability to changes in network topology. The algorithm can reconverge within seconds when the topology changes. This work provides an effective solution for multi-link communication jamming in non-cooperative confrontation scenarios.

Foundation Support

国家自然科学基金资助项目(62071367)
国家重点研发计划(2021YFB2900502)
国家重点实验室开放课题﹝CEMEE2023K0201﹞

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0216
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 2

Publish History

[2025-10-27] Accepted Paper

Cite This Article

杨翔宇, 高贞贞, 苗伟建. 基于强化学习的无线通信网络智能干扰策略方法 [J]. 计算机应用研究, 2026, 43 (2). (2025-11-04). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0216. (Yang Xiangyu, Gao Zhenzhen, Miao Weijian. Methods on intelligent jamming-strategy making based on reinforcement learning for wireless communication networks [J]. Application Research of Computers, 2026, 43 (2). (2025-11-04). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0216. )

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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  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
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  • Journal of China Computer Federation
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  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
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  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
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