Open Set Recognition of Communication Signal Modulation Based on Deep Learning

Abstract

This letter proposes a deep learning-based method for wireless communication signal modulation recognition. Modified generalized end-to-end (GE2E) loss is used to train the designed neural network, which can increase the similarity of the feature vectors of the same modulation type and reduce that of different types. The similarities between training samples and centroid vectors are calculated to set the adaptive thresholds for each known modulation type. The experimental results show that the proposed method can reject unknown signals while remaining the recognition quality of known signals, which has higher efficiency and lower complexity than other open set recognition (OSR) algorithms in modulation recognition.