A Dual-Branch Network With Cooperative Supervised Contrastive Learning for Automatic Modulation Recognition

Automatic modulation recognition (AMR) is a pivotal technology in modern communication systems. While deep learning(DL) has significantly advanced AMR, effectively leveraging the complementary information in different signal representations(e.g., in-phase/quadrature (I/Q) and amplitude/phase (A/P)) remains challenging. This letter proposes a novel dual-branch network with cooperative supervised contrastive learning (CSCL) for AMR. The CSCL is composed of two main components: 1) a modality-level cooperative learning (CL) module that aligns the projected features of I/Q and A/P sequences onto a common latent space, and 2) an extended supervised contrastive learning (ESCL) module that employs a queue memory mechanism to enrich positive and negative pairs for enhanced feature discriminability. The experimental results on three datasets demonstrate the effectiveness of our proposals and its superiority over other methods.