An Enhanced Masked Autoencoder Framework for CSI Prediction in Wireless Systems

Abstract

Accurate channel state information (CSI) prediction is essential for mitigating channel aging and feedback delay in communication systems. This letter proposes an enhanced masked autoencoder (MAE) framework for CSI prediction. Specifically, singular value decomposition (SVD) is first applied to CSI reconstruction and noise suppression, preserving dominant signal components while reducing noise interference. Then, a time–frequency hopping sampling strategy is designed to refine the MAE random masking mechanism, improving uniform subcarrier coverage over the time–frequency grid and enabling the model to learn representative channel features. Furthermore, a multi-scale aligned fusion mechanism is employed to aggregate information across resolutions for capturing diverse multipath dynamics with varying time–frequency scales. These three modules act complementarily on input enhancement, observation coverage, and multi-scale representation learning. Experimental results demonstrate that the proposed model achieves higher prediction accuracy while maintaining an improved accuracy–complexity tradeoff compared with baselines. Our code is publicly available at https://github.com/OpenCommAI/Enhanced_MAE