ViT-MAE Based Foundation Model for Automatic Modulation Classification

Foundation models represented by ChatGPT, have initiated an outstanding revolution across various domains. With the pre-trained foundation model in specific fields, numerous downstream tasks exhibit state-of-the-art performances. This paper extends this paradigm to automatic modulation classification (AMC), employing a masked autoencoder vision transformer (ViT-MAE) as a foundation model to advance AMC. The experimental results show that our signal constellation diagram-based foundation model outperforms traditional deep learning methodologies, underscoring the vast potential of foundation models in AMC and wireless communication systems.