Semi-Supervised Domain Adaptation for Automatic Modulation Recognition in Unseen Scenarios

With the rapid development of wireless communication, automatic modulation recognition (AMR) plays a key role in spectrum management of cognitive radio (CR). However, the dynamic attributes of real-world communication environments, characterized by variations in channels, noise, and other factors, present formidable challenges to AMR systems based on deep learning (DL) technologies. Conventional DL-based AMR approaches, which presuppose data independence and identical distribution (i.i.d.), typically falter in adapting to these perturbations, thereby impeding their efficacy. To rectify this predicament, In this paper, a novel semi-supervised domain-adaptive automatic modulation recognition (SSDA-AMR) method is proposed. The proposed framework seamlessly combines labeled source domain data, sparsely labeled target domain data, and employs semi-supervised domain-adaptive techniques to harmonize features across domains. Data preprocessing encompasses the transformation of in-phase/quadrature (I/Q) signals into enhanced gray-scale contour stellar images (GCSI). By optimizing through the application of adversarial domain-adaptive loss and constraint functions, effective adaptation both inter-domain and intra-domain is achieved. Comprehensive experimentation, conducted on public datasets and custom dataset, conclusively affirms the remarkable generalization capabilities of the SSDA-AMR algorithm for disparate data distributions across various channels.