Sparse Bayesian Learning Based Off-Grid Estimation of OTFS Channels with Doppler Squint

Orthogonal Time Frequency Space (OTFS) modulation has exhibited significant potential to further promote the performance of future wireless communication networks especially in high-mobility scenarios. In practical OTFS systems, the subcarrier-dependent Doppler shift which is referred to as the Doppler Squint Effect (DSE) plays an important role due to the assistance of time-frequency modulation. Unfortunately, most existing works on OTFS channel estimation ignore DSE, which leads to severe performance degradation. In this letter, OTFS systems taking DSE into consideration are investigated. Inspired by the input-output analysis with DSE and the embedded pilot pattern, the sparse Bayesian learning based parameter estimation scheme is adopted to recover the delay-Doppler channel. Simulation results verify the excellent performance of the proposed off-grid estimation approach considering DSE.