Low-Complexity Distributed Combining Design for Near-Field Cell-Free XL-MIMO Systems

TL;DR

This paper develops low-complexity distributed combining schemes for near-field CF XL-MIMO systems, enhancing spectral efficiency analysis and proposing algorithms based on matrix approximation and SSOR for practical implementation.

Contribution

It introduces five novel low-complexity distributed combining schemes for CF XL-MIMO, leveraging matrix approximation and SSOR algorithms for near-field scenarios.

Findings

Proposed schemes achieve near-optimal spectral efficiency.

Reduced computational complexity compared to centralized methods.

Validated effectiveness through theoretical analysis and simulations.

Abstract

In this paper, we investigate the low-complexity distributed combining scheme design for near-field cell-free extremely large-scale multiple-input-multiple-output (CF XL-MIMO) systems. Firstly, we construct the uplink spectral efficiency (SE) performance analysis framework for CF XL-MIMO systems over centralized and distributed processing schemes. Notably, we derive the centralized minimum mean-square error (CMMSE) and local minimum mean-square error (LMMSE) combining schemes over arbitrary channel estimators. Then, focusing on the CMMSE and LMMSE combining schemes, we propose five low-complexity distributed combining schemes based on the matrix approximation methodology or the symmetric successive over relaxation (SSOR) algorithm. More specifically, we propose two matrix approximation methodology-aided combining schemes: Global Statistics \& Local Instantaneous information-based MMSE (GSLI-MMSE) and Statistics matrix Inversion-based LMMSE (SI-LMMSE). These two schemes are derived by approximating the global instantaneous information in the CMMSE combining and the local instantaneous information in the LMMSE combining with the global and local statistics information by asymptotic analysis and matrix expectation approximation, respectively. Moreover, by applying the low-complexity SSOR algorithm to iteratively solve the matrix inversion in the LMMSE combining, we derive three distributed SSOR-based LMMSE combining schemes, distinguished from the applied information and initial values.