Fractional Doppler channel estimation for OTFS high-speed railway train-to-ground communication system

Abstract

Orthogonal time frequency space (OTFS) modulation effectively mitigates the Doppler effect in high-speed railway (HSR) train-to-ground communication, leveraging its robustness in time-frequency doubly-selective fading environments. However, current off-grid sparse Bayesian learning (OGSBL) methods based on fixed grids suffer from two primary limitations: insufficient accuracy in frequency shift quantization and the accumulation of errors from Taylor approximations. In response, this paper proposes a non-uniform grid optimization-based OGSBL channel estimation method. Firstly, a non-uniform dynamic grid partitioning strategy based on an exponential growth law is proposed to address the quantization inaccuracy caused by the Doppler effect. This method assigns higher resolution to high Doppler frequency regions while maintaining lower sampling density in low Doppler frequency regions, striking a balance between accuracy and computational complexity. Secondly, a sensing matrix optimization mechanism based on a multi-variable joint update is proposed to reduce Taylor approximation error accumulation. This mechanism facilitates dynamic reconstruction of the sensing matrix, suppressing error accumulation and accelerating convergence through the alternate update of the integer Doppler matrix, offset coupling matrix, and off-grid Doppler coefficients. Simulation results demonstrate that compared to on-grid estimation and conventional OGSBL methods, the proposed solution achieves significant improvement in channel estimation precision and convergence rate.