A channel estimation method based on transformer for OTFS system

Orthogonal time-frequency space (OTFS) is a novel modulation scheme that enables reliable communication in high-mobility environments. In this paper, we propose a Transformer-based channel estimation method for OTFS systems. Initially, the threshold method is utilized to obtain preliminary channel estimation results. To further enhance the channel estimation, we leverage the inherent temporal correlation between channels and a new method of channel response prediction is performed. Based on this, Transformer neural network is applied to our work, which is specialized in processing time series, to refine the preliminary results. Simulation results demonstrate that the performance of the proposed scheme in terms of normalized mean squared error (NMSE) and bit error rate (BER) is better than the threshold method and other deep learning (DL) methods.