Semi-supervised Joint Neural Network Based Recognition Algorithm of Modulation Signal

Due to the presence of demanding a large number of labeled samples of communication signal based supervised learning classification algorithm and considering that most of the actual situations can’t meet the requirements of large samples. Proposing a data driven model method based on semi-supervised learning, which combined Contrastive Predictive Coding with unsupervised pre-training algorithm and supervised learning algorithm, using LSTM(long short term memory) and ResNet(residual network) joint neural network to realize automatic extraction feature and improved signal recognition accuracy on small sample conditions. Experimental results show, on real communication modulation signal sets, joint neural network structures compared with previous methods have obvious advantages that recognition accuracy increased by 3%-20%, 60% performance improvement under small sample conditions and the average recognition rate of 11 kinds of modulation type is 92% at low SNR condition with 0dB.