Modulation Signal Classification Algorithm Based on Denoising Residual Convolutional Neural Network

Abstract

Traditional denoising algorithms are easy to lose signal details, resulting in low recognition accuracy of modulated signals. A modulation signal classification algorithm based on denoising residual Convolutional Neural Network (DRCNet) is proposed. DRCNet inserts a soft threshold function as a nonlinear transformation layer into the deep architecture to build a soft threshold learning network (STLNet). STLNet obtains an appropriate threshold according to the signal-to-noise ratio of the input signal samples, which is used to remove useless noise features, thereby effectively denoising the signal.This paper builds a serial network (RCTLNet) connected by Convolutional Neural Networks and Long-Short-Term Memory Networks. RCTLNet uses the convolutional neural network to extract the spatial features of the signal samples, and uses the long and short-term memory network to extract the temporal features of the signal samples. And then realize the classification and identification of the modulated signal. Experiments show that the recognition accuracy of the proposed modulated signal classification model is 92%, which is 7% higher than that of the Convolutional Long-Short Term Memory Network (CNN-LSTM).