Research on a Convolutional Neural Network Method for Modulation Waveform Classification

Modulated signal recognition is difficult but essential for applications like cognitive radio, intelligent communication, radio supervision, and electronic countermeasure. Current modulation recognition models lack comprehensiveness and typicality of various signals and primarily rely on artificial feature extraction. In this study, a convolutional neural network (CNN)-based method for modulated signal recognition is proposed. The proposed method converts modulation recognition into image identification. To increase the acuity of CNN for learning time-frequency features, channel attention and spatial attention are further introduced based on the fused features. Eight different types of modulated signals, including Rect, LFM, Barker, GFSK, CPFSK, B-FM, DSB-AM, and SSB-AM, are used in the experiments. The recognition rate of the proposed model is greater than 85% when the SNR (signal-to-noise ratio) is greater than -lOdB, and it ranges from 92% to 98% when the SNR is OdB. The recognition rate of the proposed method outperforms the two other comparison methods, CNN without an attention mechanism and LSTM. © (2023), (International Association of Engineers). All Rights Reserved.