Supervised Contrastive Learning-Based Modulation Classification of Underwater Acoustic Communication

Abstract

Modulation parameters are very significant to underwater target recognition. But influenced by the severe and time-space varying channel, most currently proposed intelligent classification networks cannot work well under these large dynamic environments. Based on supervised contrastive learning, an underwater acoustic (UWA) communication modulation classifier named UMC-SCL is proposed. Firstly, the UMC-SCL uses a simply convolutional neural networks (CNN) to identify the presence of the UWA signals. Then, the UMC-SCL uses ResNet50 as an encoder and updates the network by supervised contrastive learning loss function, which can effectively use the category information and make the eigenvector distribution of the same category more concentrated. Then, the classifier uses the feature vector output by the encoder to distinguish the final modulation categories. Finally, extensive ocean, pool, and simulation experiments are done to verify the performance of the UMC-SCL. Without any prior information, the average classification accuracy for MPSK and MFSK can reach 98.6% at 0 dB and is increased by 6% compared to the benchmark algorithm under low SNR.