SCLMR: An End-to-End Network for Long-Tailed Modulation Recognition Based on Supervised Contrastive Learning

Deep learning-based automatic modulation recognition (AMR) methods assume that all classes have the same number of samples. However, in practical applications, all classes are usually imbalanced and exhibit a long-tailed distribution, i.e., only some classes have many samples while the rest have fewer. There is a scarcity of research addressing this issue in AMR. In this article, we propose a long-tailed AMR network based on supervised contrastive learning (SCL). Specifically, a reversed sampler augmentation module is introduced to improve the diversity of tail classes with fewer samples. The uniformly sampled and the reversely sampled augmentation samples in the batch are passed through the weight-sharing encoder and the projection head to obtain a balance feature space by the SCL loss. To achieve a tradeoff between feature representation and classification, the SCL loss and the cross-entropy loss form a joint loss of dynamic weights that decay exponentially to update the model end-to-end. For the encoder, a Transformer with global dependency modeling is employed to enhance the representation of signals. Simulation results show that the proposed network effectively improves the recognition accuracy of tail classes under multiple sample size settings for different signal datasets.