DenoMAE2.0: Improving Denoising Masked Autoencoders by Classifying Local Patches for Automatic Modulation Classification

We introduce DenoMAE2.0, an enhanced denoising masked autoencoder designed to significantly improve representation learning for Automatic Modulation Classification (AMC) in wireless communications. Unlike standard Masked Autoencoders (MAEs), which solely reconstruct masked inputs, DenoMAE2.0 jointly performs denoising and reconstruction by incorporating a position-aware local patch classification objective. This approach enables the model to simultaneously denoise corrupted signals and accurately reconstruct missing information, effectively capturing both global context and local structural patterns crucial for modulation classification under noisy and data-scarce scenarios. Extensive experiments demonstrate that DenoMAE2.0 achieves superior denoising and classification performance, outperforming baseline methods and its predecessor, DenoMAE, particularly under extremely low Signal-to-Noise Ratios (SNR) (down to -7dB), where other approaches significantly degrade. Our method achieves state-of-the-art accuracy of 82.4%, improving upon the original DenoMAE by 1.1%, while consistently enhancing reconstruction quality across all modulation classes. Additionally, DenoMAE2.0 exhibits robust transfer learning capabilities, achieving consistent improved accuracy over a Vision Transformer (ViT) at different SNRs with a 6.13% highest gain at noise-signal equilibrium point. These results highlight the effectiveness of our dual-objective, self-supervised framework for robust AMC in challenging wireless communication environments.