PD-CEViT: A Novel Pilot Pattern Design and Channel Estimation Network for OFDM Systems

Abstract

Deep learning has been widely applied to channel estimation (CE), yielding significant performance improvements. However, existing research primarily focuses on static channel scenarios, leading to substantial performance degradation in dynamic environments. Furthermore, the use of fixed pilot patterns fails to adequately capture channel dynamics, resulting in unnecessary pilot overhead. In this study, we propose a Vision Transformer-based joint pilot design (PD) and CE network (PD-CEViT) for orthogonal frequency division multiplexing (OFDM) systems. The PD module leverages maximum Doppler shift and delay spread information to determine pilot positions, effectively capturing channel variations in dynamic scenarios. To further improve CE accuracy and robustness across diverse environments, the coarse CE from the PD module is passed to a CE module that utilizes a Vision Transformer (ViT), forming the joint PD-CEViT structure. Additionally, we introduce a pilot number switch network, named SwitchPD-CEViT, which dynamically adjusts between different PD-CEViT configurations based on the current channel conditions. This strategy balances network performance and pilot overhead, accommodating varying pilot requirements across different scenarios. Simulation results demonstrate that our proposed structure more effectively tracks channel variations compared to fixed pilot patterns. Even under challenging conditions with large Doppler shifts and delay spreads, our method significantly outperforms traditional and deep learning approaches in terms of mean square error (MSE) performance. Moreover, the integration of channel information further enhances estimation performance and robustness. Meanwhile, SwitchPD-CEViT achieves superior CE performance with reduced pilot overhead by efficiently managing pilot utilization.