Block Sparse Bayesian Learning-Based Channel Estimation for MIMO-OTFS Systems

作者：Lei Zhao, Jei Yang, Yueliang Liu, Wenbin Guo · 发表于：IEEE Communications Letters · 年份：2022 · DOI：10.1109/lcomm.2022.3144674 · 被引用次数：43 · 研究领域：PAPR reduction in OFDM、Advanced Power Amplifier Design、Radar Systems and Signal Processing

In this letter, we propose an efficient channel estimation method for multiple input multiple output orthogonal time-frequency-space systems in which each delay path cluster of the channel has multiple Dopplers. Under the channel model, the relationship between the input and output in the delay-Doppler (DD) domain is first analysed. Thereafter, based on the channel characteristics of the DD domain, we cast the channel estimation problem as a block sparse signal recovery problem, which is solved by the proposed block sparse Bayesian learning with block reorganization (BSBL-BR) method. In contrast to the traditional BSBL method, we update iteratively the size of non-sparse blocks to obtain a better channel estimation accuracy. Simulation results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art methods in terms of system performance and noise robustness.