Unsupervised Modulation Recognition Method Based on Multi-Domain Representation Contrastive Learning

Abstract

Automatic modulation recognition(AMR) is a key technology in the field of electronic reconnaissance. Benefiting from the powerful feature extraction ability of deep neural networks, the AMR algorithm based on supervised deep learning usually achieves pleasant performance. However, in non-cooperative scenarios high-quality and reliable modulation labels are difficult to obtain. Therefore, In this article, a novel unsupervised automatic modulation recognition method based on multi-domain representation contrastive learning (MRC-based UAMR) is proposed. By carefully utilizing unlabeled signal data with a self-supervised contrastive training step across multiple diverse representation domains, which is a more effective way than directly using IQ data as inputs, MRC aims to obtain high-quality signal feature information from unlabelled signals. The conducted experiments clearly showed that MRC remarkably outperforms existing unsupervised algorithms in modulation recognition and exhibits superior generalization ability. Our work significantly reduces the disparity between supervised and unsupervised representation learning in the field of automatic modulation recognition.