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

• Published in: 2024 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
• Language: English
• 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
• DOI: 10.1109/SPIC62469.2024.10691420

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.