MCLRL: A Multi-Domain Contrastive Learning With Reinforcement Learning Framework for Few-Shot Modulation Recognition

With the rapid development of wireless communication technology, Automatic Modulation Recognition (AMR) is essential for ensuring the security and reliability of communication systems. However, challenges have emerged, including increasing demands for higher device performance and difficulties in data acquisition due to special scenarios. Few-Shot Learning (FSL) provides an effective solution to these challenges. This paper presents a FSL framework for modulation recognition, called MCLRL. In the unsupervised pretraining phase, a combination of multi-representation domain contrastive learning and reinforcement learning enables effective feature extraction. The multi-representation domains of the signals enrich the features, while the reinforcement learning architecture extracts deep features for classification. In the supervised fine-tuning phase, a lightweight attention module and linear classifier are used to filter and classify the features, effectively avoiding overfitting. This approach requires only a few samples and minimal training epochs to achieve strong model performance. The experiments demonstrate that the MCLRL framework effectively extracts key features from the signals, performs well in FSL tasks, and remains flexible in signal model selection.