Variational Bayesian Inference Based Channel Estimation for OTFS System with LSM Prior

Abstract

Orthogonal time frequency space (OTFS) is a new emerging modulation scheme that performs better than orthogonal frequency division multiplexing (OFDM) in high mobility scenarios. In this paper, we consider the delay-Doppler (DD) channel estimation problem in an OTFS system. By exploiting the inherent sparse nature of the DD channel, the channel estimation problem is modeled as a sparse signal recovery problem. Next, we build a two-layer graphical model with the Laplacian scale mixture (LSM) prior utilized to model the sparse channel. Then, a variational Bayesian inference (VBI) based algorithm is proposed to solve this problem. Simulation results are presented to show that the proposed algorithm can achieve better performance than other existing channel estimation algorithms.