Research on automatic modulation recognition of shortwave signals in few-shot scenarios based on knowledge distillation

Abstract

Automatic modulation recognition plays an important role in wireless communication and radio regulation. Existing deep learning-based automatic modulation recognition techniques perform well with large datasets and high computational power but require significant resources for labelled data and complex pre-processing. This paper proposes a multi-information fusion method for few-shot modulation recognition, which involves converting I/Q signals into A/P signals and training with a combination of VGG and LSTM network models. An ensemble knowledge distillation (EKD) approach is employed to streamline the network model, meeting the demands for deploying neural network models on compact devices. Experimental results demonstrate that using only 1% of the shortwave modulation signal dataset as the training set, the proposed model achieves an average classification accuracy of 71.08% under all signal-to-noise ratios, surpassing the currently popular deep learning models. Moreover, two small-scale networks, MobileNetV3 and convolutional neural network are trained, through EKD. Compared to the teacher network, the floating-point operations of the distilled models are reduced by 99.8% and 99.7%, respectively, and the average prediction accuracy only decreases by 16.05% and 8.09%. The lightweight, few-shot networks designed in this study for shortwave modulation signals aim to achieve fast and accurate modulation recognition on compact devices.