# A Transformer-Based Channel Estimation Method for OTFS Systems
###### Abstract  
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. To enhance the accuracy of the preliminary results, we utilize a specialized Transformer neural network designed for processing time series data for refinement. The simulation results demonstrate that our proposed scheme outperforms the threshold method and other deep learning (DL) methods in  
within the data symbols without the inclusion of a separate protection interval. This approach leads to improved spectrum utilization while maintaining a similar bit error rate (BER) performance compared to the scheme described in [7]. A new pilot pattern was introduced in [9], building upon the single pilot scheme proposed in [7]. The authors optimized the pilot pattern by removing the protection symbols on the right side of the pilot symbol. Since the data symbols on the right side do not influence the pilot in terms of delay domains, eliminating the protection symbols in this region enhances spectral efficiency effectively. Apart from the single pilot channel estimation techniques, another approach was introduced by the authors in [10], which involved a channel estimation method based on compressed channel sensing. This method takes advantage of the sparsity property of the channel in the DD domain and converts the channel estimation task into signal recovery. By doing so, this method achieves more precise channel estimation while reducing the required pilot overhead. A high-performance algorithm with low complexity is presented in [11]. The memory approximate message passing (AMP) detector can exploit the sparsity of the channel matrix and perform only matrix-vector multiplication in each iteration. To alleviate the performance degradation caused by positive reinforcement during the iteration process, the memory AMP detector utilizes all previous information to ensure the orthogonality principle. In [12], the authors propose a scheme for joint channel estimation and data detection in a hybrid reconfigurable intelligent surface-aided millimeter wave OTFS system. The simulation results demonstrate that the proposed method can accurately obtain the channel and the unknown data symbols. Deep learning (DL) has experienced significant advancements in image and speech recognition, and its application has extended to the field of communication in recent years [13]. In [14], the authors proposed a method that utilizes a deep neural network (DNN) for both channel estimation and signal detection. They trained a neural network model using the received signals and the original transmission data, allowing for online recovery of the transmitted data through the trained model. In [15], the time-frequency response of the fast-fading channel was treated as a two-dimensional image. By leveraging the known values of the pilot, the unknown values of the channel response were reduced, addressing the challenge of estimating the channel in the presence of fast fading. In [16], a multi-layer neural network model based on a recurrent neural network (RNN) was proposed to estimate the DD domain channel with embedded pilots. The simulation results show that this method had better performance than that in [7]. In [17], the authors utilized a deep convolutional neural network (CNN) to address the issue of interference and noise in the channel matrix within the delay-Doppler (DD) domain. By applying the CNN for denoising, they achieved improved channel estimation results compared to existing methods. Additionally, this scheme demonstrated a notable enhancement in spectral efficiency.