DPSS-based Codebook Design for Near-Field XL-MIMO Channel Estimation

TL;DR

This paper introduces a DPSS-based codebook design for near-field XL-MIMO channel estimation, reducing codebook size and improving estimation accuracy by leveraging eigenvector properties related to the electromagnetic wave model.

Contribution

It proposes a novel near-field codebook design based on DPSS and a two-step channel estimation scheme, addressing challenges of near-field channel sparsification and accuracy.

Findings

Achieves lower leakage in channel sparsification.

Improves accuracy of compressive sensing-based channel estimation.

Reduces codebook size for near-field channel representation.

Abstract

Future sixth-generation (6G) systems are expected to leverage extremely large-scale multiple-input multiple-output (XL-MIMO) technology, which significantly expands the range of the near-field region. While accurate channel estimation is essential for beamforming and data detection, the unique characteristics of near-field channels pose additional challenges to the effective acquisition of channel state information. In this paper, we propose a novel codebook design, which allows efficient near-field channel estimation with significantly reduced codebook size. Specifically, we consider the eigen-problem based on the near-field electromagnetic wave transmission model. Moreover, we derive the general form of the eigenvectors associated with the near-field channel matrix, revealing their noteworthy connection to the discrete prolate spheroidal sequence (DPSS). Based on the proposed near-field codebook design, we further introduce a two-step channel estimation scheme. Simulation results demonstrate that the proposed codebook design not only achieves superior sparsification performance of near-field channels with a lower leakage effect, but also significantly improves the accuracy in compressive sensing channel estimation.