Channel Estimation for Non-Stationary Extremely Large-Scale MIMO

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a promising technology for future 6G communications. To realize effective precoding, channel estimation schemes are essential to acquire precise channel state information (CSI), while most existing schemes work relying on the spatial stationary assumption. In XL-MIMO systems, however, the spatial non-stationary effect naturally exists. Such effect can hardly be recognized by existing channel estimation schemes, leading to a severe accuracy loss of channel estimation. To address this problem, in this paper, we study the spatial non-stationary channel estimation for XL-MIMO systems. Specifically, we propose a group time block code (GTBC) based signal extraction scheme. The key idea is to artificially create and exploit the time-domain relevance of non-stationary effect, which allows XL-MIMO to recognize such effect in the space domain. In this way, the spatial non-stationary channel is converted to a series of spatial stationary channels. To effectively estimate these channels, a GTBC-based polar-domain simultaneous orthogonal matching pursuit (GP-SOMP) algorithm is proposed as a solution. Simulation results reveal that the proposed GP-SOMP algorithm can recognize the spatial non-stationary effect in XL-MIMO systems and realize a much more accurate channel estimation than existing schemes.