Channel Estimation for Massive MIMO-OTFS Systems via Sparse Bayesian Learning with 2-D Local Beta Process

Massive multiple-input multiple-output (MIMO) enables reliability in low latency communication systems. This paper proposes a channel estimation scheme in orthogonal time frequency space (OTFS) massive MIMO systems, where the inherent channel cluster structure in Doppler-angle domain is considered. In our proposed scheme, a local Beta process (LBP) is utilized to characterize a two-dimensional (2-D) structure in Doppler-angle domain. The uplink channel estimation problem is modelled as a sparse channel reconstruction problem, and it is solved by estimating channel parameters (i.e., Doppler, angle and delay) with sparse Bayesian learning (SBL). Thus, downlink channel estimation can be performed with the help of these parameters. Finally, simulation results verify that the proposed algorithm can not only achieve ideal performance of channel estimation, but also have robust adaptation to variable Doppler.