Sparse Channel Estimation utilizing Optimal Wiener-Hopf Filtering in MIMO-OTFS Paradigm

This article focuses on the MIMO-OTFS system paradigm and analyzes its three-dimensional clustering sparse characteristics based on the convolution process of signals and dual dispersion fast time-varying channels. A iterative parameter estimation algorithm based on Wiener-Hopf optimal filtering was designed in the Delay-Doppler-Angle (DDA) domain, which is the Segmented Orthogonal Matching Tracking Scheme with the assistance of minimum mean square error iteration (MAI StOMP). The performance of the proposed algorithm was compared with that of the previous proposed algorithm, and numerical simulations were conducted. The results showed that the algorithm proposed in this paper has good Normalized Mean Square Error (NMSE) performance.