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

2026-02-03

IEEE Transactions on Wireless Communications

AI Summary

This work addresses the high computational complexity and limited deployability of centralized signal combining in near-field cell-free extra-large MIMO (CF XL-MIMO) systems. To overcome these challenges, the authors develop an analytical framework for uplink spectral efficiency and, for the first time, integrate global/local statistical information approximation with the symmetric successive over-relaxation (SSOR) iterative algorithm into this context. They propose five low-complexity distributed combining schemes that leverage matrix expectation approximations and asymptotic analysis to closely approach the performance of centralized or local linear minimum mean square error (CMMSE/LMMSE) receivers. These methods significantly reduce computational overhead while maintaining near-optimal spectral efficiency, thereby achieving a favorable balance between system scalability and practical implementation.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search
• Machine Learning: Distributed Machine Learning & Federated Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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.

Problem

• Cell-Free XL-MIMO
• Near-Field
• Distributed Combining
• Low Complexity
• Spectral Efficiency

Innovation

• distributed combining
• low-complexity
• near-field XL-MIMO
• matrix approximation
• SSOR algorithm