CE-ViT: A Robust Channel Estimator Based on Vision Transformer for OFDM Systems
Fangyu Liu, Jing Zhang, Peiwen Jiang, Chao-Kai Wen, Shi Hong Jin · 2023
Deep learning (DL) has been widely utilized for channel estimation and has resulted in significant performance improvements. However, most existing research only performs training and testing in relatively static scenarios, leading to a serious deterioration in dynamic scenarios. In this paper, we propose a robust channel estimator for orthogonal frequency-division multiplexing (OFDM) systems in dynamic scenarios called channel estimator Vision Transformer (CE-ViT) based on attention mechanism. We perform a patch embedding operation to process data in both the time and frequency domains, addressing the limitations of the attention mechanism in extracting 2D correlations. Additionally, we introduce tokens that reflect channel characteristics into the network to enhance the robustness. Experimental results show that CE-ViT outperforms the state-of-the-art DL-based methods. Moreover, the addition of tokens significantly improves the performance of CE-ViT in dynamic channel conditions.