App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 8

Low signal-to-noise ratio automatic modulation recognition method based on CGDNN

Zhou Shunyonga,b
Lu Huana,b
Hu Qina,b
Peng Ziyanga,b
Zhang Hanglinga,b
a. School of Automation & Information Engineering, b. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science & Engineering, Yibin 644000, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0581

Abstract

To overcome AMR's limited generalization and low classification accuracy in non-cooperative communication contexts with low signal-to-noise ratio, This paper proposed a model named Convolutional Gated Recurrent Units Deep Neural Networks (CGDNN) . The model integrated convolutional neural networks (CNN) , gated recurrent units (GRU) , and deep neural networks. To mitigate noise impact on modulation detection, this paper initially denoised I/Q sampled signal using wavelet thresholding. Subsequently, this paper utilized CNN and GRU for extracting spatial and temporal features from signals before proceeding to classification through fully connected layers. Besides enhancing AMR performance, the CGDNN model significantly reduced computational complexity compared to competitors. Experiment results demonstrated an average recognition accuracy of 64.32% on the RML2016.10b dataset, with an enhancement in signal classification accuracy from -12dB to 0dB. Moreover, the model substantially decreased confusion between 16QAM and 64QAM, achieving a peak recognition accuracy of 93.9% at 18dB. CGDNN model effectively improved AMR detection accuracy under low signal-to-noise ratio conditions and enhanced model training efficiency.

Foundation Support

国家自然科学基金资助项目(61801319)
四川省科技厅省院省校重点项目(2020YFSY0027)
四川轻化工大学研究生创新基金资助项目(Y2023314,Y2023290)
四川轻化工大学留学归国项目(2023RC24)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0581
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 8

Publish History

[2024-02-06] Accepted Paper

Cite This Article

周顺勇, 陆欢, 胡琴, 等. 基于CGDNN的低信噪比自动调制识别方法 [J]. 计算机应用研究, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.11.0581. (Zhou Shunyong, Lu Huan, Hu Qin, et al. Low signal-to-noise ratio automatic modulation recognition method based on CGDNN [J]. Application Research of Computers, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.11.0581. )

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  • Application Research of Computers Monthly Journal
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    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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