Automatic Modulation Recognition of Communication Signal Based on Deconvolution Generation Adversarial Network

To solve the problem of low signal recognition accuracy and insufficient network training under the few-shot condition, this paper proposes a generative adversarial network modulation recognition algorithm based on deconvolution feature reconstruction, which uses a small number of labeled samples to...

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Bibliographic Details
Published in2024 2nd International Conference on Signal Processing and Intelligent Computing (SPIC) pp. 16 - 20
Main Authors Wang, Zhengyu, Zhou, Fan, Han, Xiao, Zhang, Lan
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.09.2024
Subjects
Accuracy
automatic modulation recognition
Convolutional neural networks
Deconvolution
Deep learning
few-shot learning
generative adversarial network
Generative adversarial networks
Modulation
Signal processing algorithms
Signal to noise ratio
Simulation
Training
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DOI10.1109/SPIC62469.2024.10691420

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Summary:To solve the problem of low signal recognition accuracy and insufficient network training under the few-shot condition, this paper proposes a generative adversarial network modulation recognition algorithm based on deconvolution feature reconstruction, which uses a small number of labeled samples to generate signal fake samples satisfying convolutional neural networks. The simulation results show that under the few-shot condition, the average recognition accuracy of the proposed algorithm can be improved by 9.3% compared with the traditional convolutional neural network algorithm when the SNR ranges from -6 to 6dB. Compared with the traditional generative adversarial network algorithm, the average recognition accuracy can be improved by 2.13%, effectively realizing the signal modulation recognition under the few-shot condition.
DOI:10.1109/SPIC62469.2024.10691420