A Few-Shot Automatic Modulation Classification Method Based on Temporal Singular Spectrum Graph and Meta-Learning

Abstract

Deep learning-based Automatic Modulation Classification (AMC) has achieved excellent classification accuracy. However, most deep learning-based AMC methods have an inherent drawback. They exhibit a strong dependency on massive labeled samples, which is precisely difficult to obtain in real-world scenarios. This paper presents a few-shot AMC approach that integrates signal transformation and meta-learning. The former enhances class separability, while the latter addresses challenges posed by limited sample sizes. The results of simulation experiments conducted on the RadioML.2018.01a dataset demonstrate that the proposed technique achieves a classification accuracy of 74.21% when using one sample per class and increases to 82.27% when using five samples. The few-shot AMC efficacy of this proposed approach exhibits an outperformance over the classical deep learning methods.