TDDC: A Transformer-Based Data and Knowledge Dual-Driven Scheme for Automatic Modulation Classification

Automatic Modulation Classification (AMC) plays a critical role in cognitive radio services, and latest AMC algorithms still suffers from several challenges, such as low detection accuracy, weak robustness, high dependence on labeled samples and so on in low signal-to-noise environments. To address these problems, we propose an efficient automatic modulation classification algorithm using data and knowledge dual-driven mechanism (TDDC). Initially, we design a vision transformer (ViT)-based model to extract attribute-level knowledge from the raw signal. Besides, we propose a data-driven multi-scale feature extraction model based on skip connection, to effectively capture spatio-temporal correlations of signals.Finally, to validate the effectiveness of our approach, we conduct extensive simulation experiments using the RML2016.10a and RML2016.10b datasets.