Contrastive Learning-Based Multimodal Fusion Model for Automatic Modulation Recognition

Multimodal fusion-based methods are a research hotspot for Automatic Modulation Recognition (AMR). But the existing methods primarily emphasize information integration and neglect the balance between the modalities. This paper proposes a novel Contrastive Learning-based Multimodal Fusion (CLMF) model, which integrates both signals and key features to enhance AMR. To obtain adequate signal representations, a contrastive learning architecture is proposed to learn the meaningful representations from the multimodal fusion data, and a Multi-Layer Perceptron (MLP) is incorporated for precise signal classification. Moreover, a threshold discrimination disturbance strategy is designed to balance the information conflicts arising from the two modalities. The experiments demonstrate the efficiency of the CLMF model for AMR on the public dataset.