A semi-supervised multimodal fusion approach for automatic modulation classification with label scarcity

Label scarcity poses a critical challenge for data-driven Automatic Modulation Classification (AMC) in realistic electromagnetic environments. To address this limitation, we propose Semi-supervised Multimodal Fusion (SS-MMF), a semi-supervised framework that integrates Mean Teacher consistency learning with multimodal feature fusion for leveraging large-scale unlabeled data. SS-MMF introduces an Attention-Based Multimodal Fusion (ABM-Fusion) module that adaptively integrates time-domain, frequency-domain and time-frequency representations via auxiliary-guided gating, thereby leveraging complementary information while suppressing noise interference. A Modulation-Aware Gated Convolution (MA-GConv) module is introduced to enhance local feature discrimination, while an additive attention mechanism captures long-range dependencies. Extensive experiments demonstrate that SS-MMF consistently outperforms existing methods under limited supervision, achieving improvements of 4.58%, 3.50%, and 2.90% over state-of-the-art semi-supervised approaches on RadioML2016.10a, RadioML2016.10b, and RadioML2018.01a, respectively, with only 10% labeled data, while maintaining stable performance across SNRs and superior accuracy in low-SNR and scarce-label settings.