IAFNet: Few-Shot Learning for Modulation Recognition in Underwater Impulsive Noise

Deep learning (DL)-based modulation recognition methods are challenging in the case of few labeled samples and underwater impulsive noise. In this letter, we propose a novel network structure named IAFNet to achieve higher recognition accuracy of modulation signals with fewer samples in underwater impulsive noise environment. The IAFNet integrates impulsive noise preprocessing (INP), attention network (AN) and few-shot learning (FSL) to extract features more effectively through denoising and task-driven. Experimental results on simulation and practical data show that the IAFNet attains stronger anti-noise performance and better recognition performance on fewer labeled samples. Compared with other methods, the classification accuracy is improved by about 7%.