Fewer-Sample Fast Automatic Modulation Recognition with Multimodal Deep Learning and Resized Signal Representation

Fast automatic modulation recognition (AMR) is a crucial technique for intelligent communications systems. Short recognition time, consisting of sampling time and calculation time, is the key performance factor of fast AMR. The existing research on fast AMR mostly focuses on reducing the calculation time without considering the impact of sampling time. In this letter, a novel fast AMR method is proposed in which fewer signal samples are collected and utilized to reduce the sampling time. To combat the problem of recognition accuracy loss arising from using fewer signal samples, multimodal deep learning is utilized to guarantee recognition accuracy. Moreover, to solve the problem of model size mismatch between the training and inferring stages, resized signal representation is used to avoid model retraining. Experiments show that the proposed method reduces at least 34.94% of recognition time compared with the traditional single-modality method. In addition, more reduction in recognition time can be achieved at low SNR regions, e.g., up to 72.04% when SNR is 0 dB.