Compressive Sensing-Based Channel Estimation for MIMO OTFS Systems

Orthogonal time frequency space (OTFS) modulation is a novel two-dimensional modulation technique that performs in the delay-Doppler (DD) domain. In this work, we present a new compressive sensing (CS)-based algorithm for estimating the channel in the DD domain for multiple-input multiple-output (MIMO) OTFS systems. Exploiting the property that the MIMO channel in the DD domain exhibits structured sparsity, we first obtain a row-block sparse formulation for channel estimation (CE) problem. Then, we propose a row-block orthogonal matching pursuit (RBOMP) algorithm to estimate the channel. Computer simulations demonstrate that the proposed algorithm enhances the estimation accuracy compared with the conventional minimum mean squared error (MMSE)-based and the existing CS-based CE techniques.