Semi-Supervised Modulation Classification via an Ensemble SigMatch Method

In recent years, data-driven deep learning methods have significantly improved the performance of automatic modulation classification (AMC). However, labeling the vast number of signal samples obtained in a complex electromagnetic environment is challenging due to data security concerns and the drain on manpower and material resources. The scarcity of labeled samples constrains the applicability of these methods. In this article, an ensemble SigMatch (ESM) semi-supervised AMC method is proposed to fully leverage the unlabeled modulated signals. First, a SigMatch (SM) semi-supervised AMC framework is proposed, combining pseudo-labeling, consistency regularization, and modulated signal augmentation for direct identification of raw timing signals. Three different types of signal augmentation methods are investigated through mathematical analysis of the signal model. Second, based on SM and multiview learning, the ESM method is proposed to further enhance the performance of semi-supervised AMC through consistency learning of multiple augmentation views of unlabeled signals. A multiview consistency loss is designed in ESM, with additional data augmentation as complementary views. Multiple perturbed views are guided by the same sample to achieve consistent classification through a shared classification model, thus achieving more robust feature representation. Our method demonstrates remarkable performance on data sets RML2016.10A and RML2016.04C, especially with few labeled samples. On RML2016.10A, with only 110 labeled samples, the ESM enhances the overall classification accuracy from 35.77% to 70.44% compared with supervised learning.