A Framework of Near-Field Communication with Different Array Geometries: Analysis, Optimization, and General Channel Estimation Algorithms Based on Deep Learning

TL;DR

This paper develops a comprehensive framework for near-field XL-MIMO communication, analyzing array geometries, proposing deep learning-based channel estimation algorithms, and optimizing array design for improved data rates.

Contribution

It introduces a novel near-field channel model for various array geometries, and proposes a deep learning-assisted channel estimation and array optimization method.

Findings

The AE-AMP algorithm accurately estimates near-field channels.

Array curvature significantly extends the near-field region.

Optimized array geometry improves data rates in XL-MIMO systems.

Abstract

This work establishes a framework of near-field communication under different array geometries of extremely large-scale multiple-input multiple-output (XL-MIMO). We first formulate the near-field spatial non-stationary channel model which is characterized by the distance between the user and each antenna on uniform and modular curved arrays. By fixing the total number of antennas while varying the degree of curvature, we investigate a fair case where the horizontal arc length of the curved array is the same as the planar array. We explicitly unveil the non-trivial impact of array curvature on extending the near-field region for cell edges. Then, for arbitrary array geometries and arbitrary-field channels, we estimate the spatial-domain channel by tackling a compressed sensing problem with a learned regularizer. Without relying on specific codebooks, we propose a denoising autoencoder (AE)-aided approximated message passing (AMP) algorithm and provide the corresponding theoretical replica bound. Finally, based on the estimated channel, we propose an optimization algorithm to maximize the sum user rate for sub-connected XL-MIMO systems by jointly designing the array geometry and hybrid precoding in the downlink. Numerical results demonstrate that the proposed AE-AMP algorithm can effectively estimate the spatial non-stationary near-field channels with robustness and generalities compared to several conventional and deep-learning-based benchmarks. The improvement of data rate by using modular curved arrays with the estimated channel is also validated.