Research on modulation identification of digital signals based on deep learning

Abstract

Modulation identification shows great significance for any receiver that has little knowledge of the modulation scheme of the received signal. In this paper, we compare the performance of a deep autoencoder network and three shallow algorithms including SVM, Naive Bayes and BP neural network in the field of communication signal modulation recognition. Firstly, cyclic spectrum is used to pre-process the simulation communication signals, which are at various SNR (from −10dB to 10dB). Then, a deep autoencoder network is established to approximate the internal properties from great amount of data. A softmax regression model is used as a classifier to identify the five typical communication signals, which are FSK, PSK, ASK, MSK, QAM. The results for the experiment illustrate the excellent classification performance of the networks. At last, we discuss the comparison of these methods and three traditional shallow machine learning models.