Near-Field Channel Estimation for Extremely Large-scale MIMO with Hybrid Precoding

Abstract

Extremely large-scale multiple-input-multiple-output (XL-MIMO) with hybrid precoding is a promising technique to meet the high rate requirements for future 6G. To realize efficient precoding, accurate channel estimation is essential. Existing channel estimation algorithms with low pilot overhead heavily rely on the channel sparsity in angle domain, which is achieved by the classical far-field planar-wavefront assumption. However, this sparsity is not available, due to the non-negligible near-field spherical-wavefront property in XL-MIMO. Therefore, existing far-field estimation schemes will suffer from severe performance loss. To address this problem, in this paper, we study the near-field channel estimation by exploiting the polar-domain sparsity. Specifically, unlike the classical angle-domain representation that only considers the angle information of channel, we propose a polar-domain representation, which simultaneously accounts both the angle and distance information. In this way, the near-field channel also exhibits sparsity in polar domain. Exploiting this polar-domain sparsity, we propose an polar-domain simultaneous orthogonal matching pursuit (P-SOMP) algorithm to efficiently estimate the near-field channel. Finally, simulations are provided to verify the effectiveness of our schemes.