Performance Analysis of Cell-Free Massive MIMO under Imperfect LoS Phase Tracking

2026-01-16

AI Summary

This work addresses the modeling gap between idealized assumptions and practical hardware limitations in cell-free massive MIMO systems, where imperfect line-of-sight (LoS) phase tracking introduces residual phase errors. To bridge this gap, the authors develop a Rician fading channel model incorporating residual phase uncertainty and propose a unified linear minimum mean square error (MMSE) channel estimation framework that accommodates arbitrary levels of prior phase knowledge. By leveraging a virtual uplink transformation, they derive both centralized and distributed MMSE beamformers. Notably, this approach is the first to unify the extremes of perfect and no phase prior within a statistical phase error model, enabling tractable performance analysis even in non-Gaussian environments. Numerical results demonstrate that the proposed framework effectively narrows the theory-practice gap, offering a rigorous performance benchmark and design guidance for 6G cell-free networks.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Security and Privacy: Large-scale security measurements

Abstract

We study the impact of imperfect line-of-sight (LoS) phase tracking on the performance of cell-free massive MIMO networks. Unlike prior works that assume perfectly known or completely unknown phases, we consider a realistic regime where LoS phases are estimated with residual uncertainty due to hardware impairments, mobility, and synchronization errors. To this end, we propose a Rician fading model where LoS components are rotated by imperfect phase estimates and attenuated by a deterministic phase-error penalty factor. We derive a linear MMSE channel estimator that captures statistical phase errors and unifies prior results, reducing to the Bayesian MMSE estimator with perfect phase knowledge and to a zero-mean model in the absence of phase knowledge. To address the non-Gaussian setting, we introduce a virtual uplink model that preserves second-order statistics of channel estimation, enabling the derivation of tractable centralized and distributed MMSE beamformers. To ensure fair assessment of the network performance, we apply these beamformers to the true uplink model and compute the spectral efficiency bounds available in the literature. Numerical results show that our framework bridges idealized assumptions and practical tracking limitations, providing rigorous performance benchmarks and design insights for 6G cell-free networks.

Problem

• cell-free massive MIMO
• imperfect phase tracking
• LoS phase uncertainty
• Rician fading
• spectral efficiency

Innovation

• cell-free massive MIMO
• imperfect LoS phase tracking
• linear MMSE estimator
• virtual uplink model
• Rician fading with phase error