Intelligent identification technology for high-order digital modulation signals under low signal-to-noise ratio conditions

Abstract

Based on the successful application of generative adversarial network (GAN) models in the field of image generation, this article introduces GANs into the field of deep learning for communication systems and surveys its application in modulation classification. To solve the difficulties in feature extraction, to address the low recognition accuracy of existing radio signal modulation-type recognition methods, and to adapt to complex electromagnetic environments with high noise interference intensity, this article presents a modulation recognition model for high-order digital signals. This model uses the Morlet wavelet transform to analyse time-frequency signals, uses the excellent image generation performance of a GAN model to extract and reconstruct the features of noise-contaminated time-frequency images, and designs an integrated classification network architecture to classify and predict reconstructed images. The experimental results show that the algorithm model proposed in this article can significantly improve the recognition accuracy of high-order digital modulated signals under low signal-to-noise ratio conditions and can achieve 90% recognition accuracy at a signal-to-noise ratio of 1 dB.