An Efficient Model for Few-Shot Automatic Modulation Recognition Based on Supervised Contrastive Learning

The application of deep learning (DL) has improved the reliability and intelligence of automatic modulation recognition (AMR). However, real-world scenarios often involve a limited number of signal samples. Furthermore, most existing DL-based AMR models improve performance at the cost of computational complexity. Therefore, we propose an efficient contrastive multi-stage sparse attention network (CMSSAN) model for few-shot AMR without auxiliary datasets. Specifically, supervised contrastive learning is utilized to enhance the feature representation of the signal, and a joint loss with dynamic weights is constructed to balance the representation and classification tasks. In addition, a lightweight MSSAN encoder is proposed to enhance the recognition performance with lower computations and parameters. Simulation experiments are conducted on the ablation experiment and hyperparameter analysis of the proposed model, and the superiority of the model is verified on several datasets.