Semi-Supervised Domain Adaptation for Automatic Modulation Recognition in Unseen Scenarios
Maomao Zhang, Guofeng Wei, Peng Tang, Xue Ni, Guoru Ding, Huali Wang
Abstract
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.