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

TL;DR

This paper analyzes how imperfect LoS phase tracking affects uplink performance in cell-free massive MIMO, proposing a realistic model and deriving estimators and beamformers that account for phase errors.

Contribution

It introduces a Rician fading model with phase-error penalties and develops MMSE estimators and beamformers that incorporate residual phase uncertainties.

Findings

Performance degrades with increased phase estimation errors.

The proposed estimators outperform traditional methods under realistic conditions.

Framework provides practical benchmarks for 6G cell-free network design.

Abstract

We study the impact of imperfect line-of-sight (LoS) phase tracking on the uplink 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 \textit{phase-error penalty factor}. We derive a linear MMSE channel estimator that accounts for statistical phase errors and unifies prior results, reducing to the Bayesian MMSE estimator when phase is perfectly known and to a zero-mean model when no phase information is available. 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 virtual centralized and distributed MMSE beamformers. To ensure fair assessment of network performance, we apply these virtual beamformers to the operational uplink model that reflects the actual physical channel 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.