Two-dimensional sparse recovery method for channel estimation in massive MIMO OTFS systems - WANG Han; WEN Fangqing; LI Xingwang; WANG Xianpeng (Tongxin xuebao)
Massive multiple-input multiple-output (MIMO) orthogonal time frequency space (OTFS) systems play a vital role in high-mobility scenarios like vehicle-to-everything and high-speed railways, offering strong resilience against Doppler effects and mitigating channel degradation. However, accurate channel estimation remains a challenge, especially in dynamic environments where conventional methods like orthogonal matching pursuit (OMP) struggle. A two-dimensional regularized orthogonal matching pursuit (2D-ROMP) algorithm tailored was introduced for channel estimation in massive MIMO OTFS systems. Unlike traditional OMP-based methods, in which delay and Doppler shifts were estimated independently, the proposed algorithm combined these two dimensions into a unified delay-Doppler (DD) domain, effectively leveraging the multi-dimensional sparsity of OTFS channels. By employing regularization, the most relevant DD indices were first identified, , followed by refinement in the antenna angle domain to leverage burst sparsity. Compared to conventional OMP and three-dimensional structured OMP (3D-SOMP) methods, the proposed algorithm was demonstrated to achieve improved channel estimation accuracy and computational efficiency. Simulation results demonstrate that it outperforms existing approaches with reduced pilot overhead and enhanced robustness in dynamic environments.