Disentangling Domain-Invariant Features from Few Samples for Automatic Modulation Classification

The existing deep learning (DL)-based automatic modulation classification (AMC) models are difficult to apply in non-cooperative communication systems, primarily due to two major challenges. 1.Domain shift: the variable usage scenarios of frequency devices often result in data distribution shifts during signal transmission, significantly compromising the performance of existing models. 2.Sample scarcity: collecting large amounts of samples to support the training of DL models is impractical as it may involve issues of legal privacy and resource consumption. Existing works has addressed these two challenges as separate tasks, making it difficult to implement a comprehensive solution. Consequently, this paper proposes a unified framework named signal feature disentangling network (SFDN). By leveraging dual constraints at the instance and feature levels, SFDN effectively disentangles signal features into domain-specific and content-related components. The content features are subsequently utilized for classification tasks, while the domain features are disregarded, thereby mitigating the challenge of domain shift. Furthermore, the exchange and recombination of domain and content features facilitate the generation of cross-domain samples. These synthetically created samples are then employed in a supervised manner, significantly alleviating the challenge of sample scarcity. The comprehensive experimental verification under three typical channel conditions demonstrates the progressiveness of our SFDN.