Mixed signal modulation recognition method based on temporal depth residual shrinkage network - LIU Jinghua; WEI Xianglin; FAN Jianhua; HU Yongyang; WANG Xiaobo; YU Bing (Dianxin kexue)
Deep learning-based automatic signal modulation recognition has generally outperformed traditional methods in terms of classification accuracy and transferability, garnering widespread attention. However, most existing methods are designed to recognize single signal samples and are not applicable to recognize scenarios involving overlapping signals. To address this limitation, a modulation recognition method for aliased signals was investigated and a temporal deep residual shrinkage network model by integrating LSTM and DRSN was developed. There were three key modules in the model: a residual module, a shrinkage module, and a LSTM module. Salient information from overlapping signals was extracted by the residual module and the shrinkage module and decision thresholds were adaptively generated, while the LSTM module is tasked with extracting temporal hidden signals within the aliased data. The recognition accuracy of aliased signals was enhanced by the combination of these modules significantly. Testing on both public and private datasets demonstrates that the proposed method outperforms five state-of-the-art approaches, achieving an average recognition and classification accuracy of 92.7% under high signal-to-noise ratio conditions. Notably, the recognition accuracy for 12 out of 21 types of aliased signals approaches 100%.