CAEP: Cross-Modal Adaptive Embedding Prediction for Self-Supervised Modulation Classification

Although self-supervised learning methods have shown promising progress in addressing the issue of scarce labeled data in automatic modulation classification, they remain constrained by heavy reliance on extensive negative samples and an inability to effectively capture inter-modal feature correlations. To overcome these limitations, we propose a novel self-supervised automatic modulation classification algorithm based on multi-path embedding prediction, termed CAEP. In CAEP, the raw signal is first dynamically segmented into current and future sub-series. Then, dedicated encoders are utilized to extract embeddings for both sub-series and leverage current information to predict future states, while randomly masking the corresponding time–frequency images transformed from the time-domain signal to predict the obscured spectral components. Furthermore, latent temporal embeddings are deployed to predict information within the time–frequency domain to achieve cross-modal retrieval. Finally, a classification head is connected alongside a temporal modal encoder, which is fine-tuned using a limited set of labeled samples to accomplish modulation classification. Experimental results on two benchmark datasets demonstrate that the proposed method achieves robust performance across varying noise conditions.