Sensing-Enhanced Channel Estimation for Near-Field XL-MIMO Systems

TL;DR

This paper introduces a sensing-enhanced channel estimation scheme for near-field XL-MIMO systems that reduces sampling requirements and computational complexity by using power sensors and a novel eigen-based dictionary.

Contribution

It proposes a single-slot sensing method with power sensors and a low-complexity localization algorithm, along with an eigen-dictionary for efficient near-field channel estimation.

Findings

Achieves accurate user and scatterer localization using power measurements.

Reduces baseband sampling by up to 66% with a compact eigen-dictionary.

Outperforms existing algorithms in computational efficiency.

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. The spherical wavefront characteristics in the near field introduce additional degrees of freedom (DoFs), namely distance and angle, into the channel model, which leads to unique challenges in channel estimation (CE). In this paper, we propose a new sensing-enhanced uplink CE scheme for near-field XL-MIMO, which notably reduces the required quantity of baseband samples and the dictionary size. In particular, we first propose a sensing method that can be accomplished in a single time slot. It employs power sensors embedded within the antenna elements to measure the received power pattern rather than baseband samples. A time inversion algorithm is then proposed to precisely estimate the locations of users and scatterers, which offers a substantially lower computational complexity. Based on the estimated locations from sensing, a novel dictionary is then proposed by considering the eigen-problem based on the near-field transmission model, which facilitates efficient near-field CE with less baseband sampling and a more lightweight dictionary. 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). Simulation results unveil that the proposed time inversion algorithm achieves accurate localization with power measurements only, and remarkably outperforms various widely-adopted algorithms in terms of computational complexity. Furthermore, the proposed eigen-dictionary considerably improves the accuracy in CE with a compact dictionary size and a drastic reduction in baseband samples by up to 66%.