Fast Burst-Sparsity Learning Approach for Massive MIMO-OTFS Channel Estimation

Accurate channel estimation for multiple-input multiple-output (MIMO)-orthogonal time frequency space (OTFS) systems is challenging due to high-dimensional sparse representation (SR). Existing MIMO-OTFS channel estimation methods often face performance degradation and/or high computational complexity. To address these issues and exploit intricate channel sparsity structure, this letter first leverages a novel hybrid burst-sparsity prior to capture the burst/i.i.d. sparse structures in the angle-delay domain, and then utilizes an independent variational Bayesian inference (VBI) factorization technique to efficiently solve the high-dimensional SR problem. Additionally, an angle-Doppler refinement approach is incorporated into the proposed method to automatically mitigate off-grid mismatches.