Cross-Domain Automatic Modulation Classification: A Multimodal-Information-Based Progressive Unsupervised Domain Adaptation Network

Abstract

Automatic modulation classification (AMC) is crucial for ensuring secure and efficient operation of the Internet of Things (IoT). In this study, we focus on addressing the challenge of cross-domain modulation classification, which arises due to differences in data distribution between the source and target domains. Current research focuses on leveraging multimodal information to improve performance, but struggles with mapping diverse domains into a common feature space for effective classification. Additionally, most existing domain-adversarial approaches measure the distribution differences between the source and target domains using batch sample average differences, without considering the labels of the samples. To tackle these issues, we propose a progressive multisource partial domain adaptation modulation classification (PMSPDMC) method. PMSPDMC utilizes multiple sources comprising three sequence modalities and an auxiliary modality. It consists of two main components: 1) feature space alignment and 2) decision space alignment. In feature space alignment, we design a hybrid metric function to extract distinctive features that maintain tight intraclass distances and separable interclass distances, achieving fine-grained alignment between the source and target domains in the feature space. To achieve decision space alignment, a weighted fusion strategy and prediction consistency regularization are applied to the final classification prediction. This design of distribution difference measurement criteria effectively addresses the practical situation where the target domain has fewer modulation types than the source domain. The simulation results demonstrate that the proposed PMSPDMC exhibits performance gains in the cross-domain modulation classification tasks.