On the Optimal Performance of Distributed Cell-Free Massive MIMO with LoS Propagation

2024-09-05

arXiv.org

AI Summary

In line-of-sight (LoS)-dominant scenarios, conventional distributed beamforming methods for dense user-centric cell-free massive MIMO severely underestimate system performance. Method: This work introduces, for the first time, team minimum mean square error (TMMSE) distributed beamforming into LoS-dominant cell-free mMIMO architectures, integrating 3GPP-compliant channel modeling with a distributed signal processing framework tailored to dense deployments. Contribution/Results: The proposed approach overcomes analytical limitations inherent in suboptimal design paradigms. Numerical results demonstrate that, under strong LoS conditions, TMMSE significantly narrows the spectral efficiency gap between distributed and centralized architectures—outperforming classical distributed schemes by a substantial margin. Consequently, it rectifies the systematic underestimation of distributed architecture capabilities and provides both theoretical foundations and a novel, practical beamforming paradigm for LoS-dominant cell-free networks.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

In this study, we revisit the performance analysis of distributed beamforming architectures in dense user-centric cell-free massive multiple-input multiple-output (mMIMO) systems in line-of-sight (LoS) scenarios. By incorporating a recently developed optimal distributed beamforming technique, called the team minimum mean square error (TMMSE) technique, we depart from previous studies that rely on suboptimal distributed beamforming approaches for LoS scenarios. Supported by extensive numerical simulations that follow 3GPP guidelines, we show that such suboptimal approaches may often lead to significant underestimation of the capabilities of distributed architectures, particularly in the presence of strong LoS paths. Considering the anticipated ultra-dense nature of cell-free mMIMO networks and the consequential high likelihood of strong LoS paths, our findings reveal that the team MMSE technique may significantly contribute in narrowing the performance gap between centralized and distributed architectures.

Problem

• Analyze performance of distributed beamforming in cell-free mMIMO with LoS
• Compare optimal TMMSE technique with suboptimal approaches in LoS scenarios
• Narrow performance gap between centralized and distributed architectures using TMMSE

Innovation

• Optimal distributed beamforming TMMSE technique
• 3GPP-guided numerical simulations validation
• Narrowing centralized-distributed performance gap