Joint Channel Estimation and Data Detection for OTFS Systems: A Lightweight Deep Learning Framework With a Novel Data Augmentation Method

Yuan Gao, Xinchen Xu, Yanliang Jin, Weijie Yuan, Jie Zhang, Shugong Xu

Abstract: Orthogonal time frequency space (OTFS) modulation is expected to address the performance degradation of orthogonal frequency division multiplexing (OFDM) modulated signals, particularly due to issues like Doppler shifts in mobile communication environments. In this article, we propose a lightweight deep learning-based framework for end-to-end joint channel estimation and data detection (JCEDD) in an OTFS communication system. To fully exploit the characteristics of OTFS modulation, we introduce a data padding preprocessing method and a slicing data augmentation technique. Furthermore, the performance of the proposed deep learning-based framework could be enhanced dramatically with only a small overhead compared to the superimposed pilot scheme. Ablation experiments demonstrate that the proposed data padding preprocessing method and the slicing data augmentation technique significantly improve the performance of the deep learning-based framework. Simulation results show that the proposed framework outperforms existing algorithms in terms of JCEDD performance, while maintaining a relatively low level of computational complexity.

External IDs:doi:10.1109/jiot.2025.3586012