Performance Analysis and Low-Complexity Design for XL-MIMO with Near-Field Spatial Non-Stationarities

TL;DR

This paper analyzes the near-field performance of XL-MIMO systems considering spatial non-stationarities, and proposes low-complexity algorithms for user detection and grouping based on visibility regions, achieving near-optimal performance with reduced complexity.

Contribution

It introduces a near-field EM channel model for XL-MIMO, analyzes the impact of aperture and polarization, and develops VR detection and user partition algorithms for low-complexity system design.

Findings

Limited part of the array receives most of the signal power

Proposed algorithms approach traditional performance with lower complexity

Numerical results validate analytical analysis and algorithm effectiveness

Abstract

Extremely large-scale multiple-input multiple-output (XL-MIMO) is capable of supporting extremely high system capacities with large numbers of users. In this work, we build a framework for the analysis and low-complexity design of XL-MIMO in the near field with spatial non-stationarities. Specifically, we first analyze the theoretical performance of discrete-aperture XL-MIMO using an electromagnetic (EM) channel model based on the near-field spherical wavefront. We analytically reveal the impact of the discrete aperture and polarization mismatch on the received power. We also complement the classical Fraunhofer distance based on the considered EM channel model. Our analytical results indicate that a limited part of the XL-array receives the majority of the signal power in the near field, which leads to a notion of visibility region (VR) of a user. Thus, we propose a VR detection algorithm and leverage the acquired VR information to devise a low-complexity symbol detection scheme. Furthermore, we propose a graph theory-based user partition algorithm, relying on the VR overlap ratio between different users. Partial zero-forcing (PZF) is utilized to eliminate only the interference from users allocated to the same group, which further reduces computational complexity in matrix inversion. Numerical results confirm the correctness of the analytical results and the effectiveness of the proposed algorithms. It reveals that our algorithms approach the performance of conventional whole array (WA)-based designs but with much lower complexity.