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Master-Assisted Distributed Uplink Operation for Cell-Free Massive MIMO Networks

Andreas Angelou, Pourya Behmandpoor, Marc Moonen

TL;DR

The paper tackles uplink processing in CFmMIMO by balancing fronthaul signaling and computational load while preserving high spectral efficiency. It introduces MADUO, where each UE has a Master AP and ASAPs that forward soft estimates and fused CSI to the MAP, enabling near-centralized performance with controlled resources; a scalable variant MADUO$^{\text{scl}}$ further reduces fronthaul and complexity by using local CSI and LP-MMSE. Key contributions include a practical MAP-ASAP architecture, a derived receive combiner that maximizes a generalized Rayleigh quotient, and a complexity/fronthaul analysis showing favorable scaling compared to fully centralized or distributed schemes. Numerical results confirm near-centralized SE with reduced resource use, and code is provided for reproducibility.

Abstract

Cell-free massive multiple-input-multiple-output is considered a promising technology for the next generation of wireless communication networks. The main idea is to distribute a large number of access points (APs) in a geographical region to serve the user equipments (UEs) cooperatively. In the uplink, one of two types of operations is often adopted: centralized or distributed. In centralized operation, channel estimation and data decoding are performed at the central processing unit (CPU), whereas in distributed operation, channel estimation occurs at the APs and data detection at the CPU. In this paper, we propose a novel uplink operation, termed Master-Assisted Distributed Uplink Operation (MADUO), where each UE is assigned a master AP, which receives soft data estimates from the other APs and decodes the data using its local signals and the received data estimates. Numerical experiments demonstrate that the proposed operation performs comparably to the centralized operation and balances fronthaul signaling and computational complexity.

Master-Assisted Distributed Uplink Operation for Cell-Free Massive MIMO Networks

TL;DR

The paper tackles uplink processing in CFmMIMO by balancing fronthaul signaling and computational load while preserving high spectral efficiency. It introduces MADUO, where each UE has a Master AP and ASAPs that forward soft estimates and fused CSI to the MAP, enabling near-centralized performance with controlled resources; a scalable variant MADUO further reduces fronthaul and complexity by using local CSI and LP-MMSE. Key contributions include a practical MAP-ASAP architecture, a derived receive combiner that maximizes a generalized Rayleigh quotient, and a complexity/fronthaul analysis showing favorable scaling compared to fully centralized or distributed schemes. Numerical results confirm near-centralized SE with reduced resource use, and code is provided for reproducibility.

Abstract

Cell-free massive multiple-input-multiple-output is considered a promising technology for the next generation of wireless communication networks. The main idea is to distribute a large number of access points (APs) in a geographical region to serve the user equipments (UEs) cooperatively. In the uplink, one of two types of operations is often adopted: centralized or distributed. In centralized operation, channel estimation and data decoding are performed at the central processing unit (CPU), whereas in distributed operation, channel estimation occurs at the APs and data detection at the CPU. In this paper, we propose a novel uplink operation, termed Master-Assisted Distributed Uplink Operation (MADUO), where each UE is assigned a master AP, which receives soft data estimates from the other APs and decodes the data using its local signals and the received data estimates. Numerical experiments demonstrate that the proposed operation performs comparably to the centralized operation and balances fronthaul signaling and computational complexity.
Paper Structure (10 sections, 2 theorems, 13 equations, 3 figures)

This paper contains 10 sections, 2 theorems, 13 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Cell-Free Massive MIMO Model
  3. Channel Model and Channel Estimation
  4. Data Transmission and Receive Combining
  5. Master-Assisted Distributed Uplink Operation
  6. Master AP Receive Combining
  7. Scalable MADUO
  8. Fronthaul Signaling and Computational Complexity
  9. Numerical Experiments
  10. Conclusion

Key Result

Lemma 1

An achievable SE of UE $k$ in MADUO is where the expectation is with respect to channel realizations and the effective SINR is with $\widehat{\bm{\mathrm{z}}}_{l,kk} =$, where $\widehat{\bm{\mathrm{g}}}_{kk}[r,:] = \bm{\mathrm{v}}_{r,k}^H \bm{\mathrm{D}}_{r,k} \widehat{\bm{\mathrm{h}}}_{r,k}$ is the $r$-th row of $\widehat{\bm{\mathrm{g}}}_{kk}\in \mathbb{C}^{L-1}$ (the row $r=l$ is excluded). M

Figures (3)

  • Figure 1: Cumulative distribution function (CDF) of the per-UE SE for $L=100$, $N=4$, and $K=40$. MADUO and MADUO$^{\text{scl}}$ perform comparably to the centralized operation.
  • Figure 2: Fronthaul signaling for $L=100$, $N=4$, and varying $K$. For $K>60$, MADUO$^{\text{scl}}$ requires less fronthaul signaling than the distributed operation.
  • Figure 3: Number of complex multiplications for the service of one UE for varying $K$, with $L=100$ and $N=4$. The number of computations of MADUO$^{\text{scl}}$ decreases with $K$, approaching that of the distributed operation.

Theorems & Definitions (4)

  • Lemma 1
  • proof
  • Corollary 1
  • proof