Adaptive Block Sparse Backtracking Based Channel Estimation for Massive MIMO-OTFS Systems

Orthogonal time frequency space (OTFS) modulation, combined with massive multiple-input multiple-output (MIMO) technology, offers robust performance in high-mobility environments and high user densities by capturing the full diversity of the wireless channel and effectively utilizing spatial multiplexing. This paper introduces an adaptive block sparse backtracking (ABSB) algorithm designed to enhance channel estimation in massive MIMO-OTFS systems. The proposed ABSB algorithm features dynamic block size adjustment based on the residual signal, improving its adaptability to the varying sparsity structure of the channel. Additionally, the algorithm extends the selection range of related block atoms to increase redundancy, reducing the risk of underfitting. Comprehensive simulation results demonstrate that the ABSB algorithm significantly outperforms traditional pilot-based methods in terms of channel estimation accuracy. It also surpasses the block orthogonal matching pursuit (BOMP) method as well as other classical compressed sensing methods. Specifically, the ABSB algorithm achieves up to a 20% reduction in estimation error compared to some of these traditional methods. The enhanced adaptability and robustness of the ABSB algorithm make it a promising solution for channel estimation in massive MIMO-OTFS systems, paving the way for more reliable and efficient next-generation wireless communications.