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Technology of Network & Communication
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 3
880-887

Federated learning-cooperative spectrum sensing based on belief accumulation

Liang Yana,b
Sun Yongxina,b
a. School of Communication & Information Engineering, b. Chongqing Key Laboratory of Signal & Information Processing, Chongqing University of Posts & Telecommunications, Chongqing 400065, China
DOI: 10.19734/j.issn.1001-3695.2024.06.0258

Abstract

Aiming at the problem that CSS has low detection probability and is susceptible to interference from poorly perfor-ming nodes under low signal-to-noise ratio, this paper proposed a federated learning-cooperative spectrum sensing method(FL-CSS) based on belief accumulation. The FL-CSS was divided into two parts: the offline training part and the online detection part. It designed the model fusion and update strategy in the offline training part, which ensured the local model performance of secondary users(SU), and avoided the interference of the global model by the poor performance of SUs. It designed the data fusion strategy in the online detection part, and proposed the belief accumulation algorithm to assign weights to the SUs, which integrally took into account the weights of the SUs in model fusion, the influence of the SUs in the last decision, and the influence of the historical factors of the decision. The proposed FL-CSS method ensured the detection probability and improved the accuracy of fusion data decisions by addressing the varying environmental conditions of SUs in CSS and the susceptibility to interference during data fusion. Simulation results indicate that this method enhances the detection probability at low signal-to-noise ratios and efficiently addresses interference and aggressive behaviors, which is better than the existing cooperative spectrum sensing scheme.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.06.0258
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 3
Section: Technology of Network & Communication
Pages: 880-887
Serial Number: 1001-3695(2025)03-031-0880-08

Publish History

[2024-12-10] Accepted Paper
[2025-03-05] Printed Article

Cite This Article

梁燕, 孙永鑫. 基于信任度累积的联邦协作频谱感知 [J]. 计算机应用研究, 2025, 42 (3): 880-887. (Liang Yan, Sun Yongxin. Federated learning-cooperative spectrum sensing based on belief accumulation [J]. Application Research of Computers, 2025, 42 (3): 880-887. )

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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