A few-shot learning-based automatic modulation classification method for Internet of Things

Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment, we consider few-shot learning-based automatic modulation classification (AMC) to improve its reliability. A data enhancement module (DEM) is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC. Multimodal network is designed to have multiple residual blocks, where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction. Moreover, a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM. Since different model may output different results, cooperative classifier is designed to avoid the randomness of single model and improve the reliability. Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.