Sun Teng, Lv Jiebiao, Zhou Tao
The 54th Research Institute of CETC, Shijiazhuang 050081, China.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Entropy (Basel). 2023 Oct 7;25(10):1423. doi: 10.3390/e25101423.
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 terms of normalized mean squared error and bit error rate. Additionally, the temporal complexity and spatial complexity of different DL models are compared. The results indicate that our proposed algorithm achieves superior accuracy while maintaining an acceptable computational complexity.
正交时频空间（OTFS）是一种新型调制方案，可在高移动性环境中实现可靠通信。在本文中，我们提出了一种基于Transformer的OTFS系统信道估计方法。首先，利用阈值方法获得初步的信道估计结果。为了进一步增强信道估计，我们利用信道之间固有的时间相关性，并执行一种新的信道响应预测方法。为了提高初步结果的准确性，我们使用专门设计用于处理时间序列数据的Transformer神经网络进行细化。仿真结果表明，我们提出的方案在归一化均方误差和误码率方面优于阈值方法和其他深度学习（DL）方法。此外，还比较了不同DL模型的时间复杂度和空间复杂度。结果表明，我们提出的算法在保持可接受的计算复杂度的同时，实现了更高的准确性。
