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

Noor Ul Ain, Lorenzo Miretti, Sławomir Stańczak

TL;DR

This work reevaluates distributed beamforming in dense, user-centric cell-free mMIMO under line-of-sight propagation by applying the optimal team MMSE (LTMMSE) framework. It shows that conventional distributed schemes can severely underperform in strong LoS regimes, while LTMMSE substantially narrows the performance gap to centralized MMSE, especially in ultra-dense deployments. Through phase-aware, 3GPP-aligned simulations with spatially correlated Rician fading and uplink training, the study demonstrates that LTMMSE approaches centralized performance as LoS strength increases and network density grows, validating its practical promise for next-generation CF mMIMO. The results highlight the importance of globally coordinated beamforming design in LoS-rich, dense networks and provide actionable insights for power control and large-scale fading decoding in distributed architectures.

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.

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

TL;DR

This work reevaluates distributed beamforming in dense, user-centric cell-free mMIMO under line-of-sight propagation by applying the optimal team MMSE (LTMMSE) framework. It shows that conventional distributed schemes can severely underperform in strong LoS regimes, while LTMMSE substantially narrows the performance gap to centralized MMSE, especially in ultra-dense deployments. Through phase-aware, 3GPP-aligned simulations with spatially correlated Rician fading and uplink training, the study demonstrates that LTMMSE approaches centralized performance as LoS strength increases and network density grows, validating its practical promise for next-generation CF mMIMO. The results highlight the importance of globally coordinated beamforming design in LoS-rich, dense networks and provide actionable insights for power control and large-scale fading decoding in distributed architectures.

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.
Paper Structure (14 sections, 2 theorems, 24 equations, 2 figures, 1 table)

This paper contains 14 sections, 2 theorems, 24 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Channel and CSI Model
  3. Spatially Correlated Rician Fading with Phase Shifts
  4. Uplink Channel Estimation
  5. Uplink Data Transmission
  6. Beamforming Schemes
  7. Centralized Beamforming
  8. Distributed Beamforming
  9. Local MMSE with Optimal LSFD
  10. Local Team MMSE
  11. Impact of LoS propagation
  12. Numerical Results
  13. Parameters and Setup
  14. Results and Conclusions

Key Result

Proposition 1

If $\hat{\bm{h}}_{k,l}$ is independently distributed as $\mathcal{N}_{\mathbb{C}}(\bm{0},\bm{R}_{k,l}-\bm{C}_{k,l})$ for all $k\in \mathcal{K}$ and $l\in \mathcal{L}$, then

Figures (2)

  • Figure 1: Comparison of ul SEs achieved by different beamforming schemes for the case $v=-1$ in \ref{['eq:power_control']}. The solid lines refer to the uatf bound \ref{['eq:se_uatf']}, and the dotted lines refer to the coherent decoding bound \ref{['eq:se_cd']}. a) Minimum ul se for different values of $\kappa$ ($d=1$ km, $p_{\max} = 100$ mW); b) Minimum ul se for different lengths of the square service area $d$; c) CDF of the ul per-user se in a dense network ($d=200$m, $p_{\max} = 20$ mW).
  • Figure 2: Comparison of ul SEs achieved by different beamforming schemes for the case $v=0$ in \ref{['eq:power_control']}. The solid lines refer to the uatf bound \ref{['eq:se_uatf']}, and the dotted lines refer to the coherent decoding bound \ref{['eq:se_cd']}. a) Sum ul se for different values of $\kappa$ ($d=1$ km, $p_{\max} = 100$ mW); b) Sum ul se for different lengths of the square service area $d$; c) CDF of the ul per-user se in a dense network ($d=200$m, $p_{\max} = 20$ mW).

Theorems & Definitions (6)

  • Remark 1
  • Remark 2
  • Proposition 1
  • proof
  • Proposition 2
  • proof