Skip to content
Open access

QA-P-MMSE Scalable and High-Performance Receiver for Cell-Free Massive MIMO with Quantized Fronthaul P

Jul 2026 · International Journal of Electronics and Telecommunications · Vol 72, pp. 1-7 · 0 citations

TL;DR

This paper proposes a novel, scalable receiver scheme: the Quantization-Aware Partial MMSE (QA-P-MMSE) receiver, which significantly outperforms other scalable schemes, such as Maximum-Ratio (MR) and Partial-MMSE (P-MMSE), in terms of both average spectral efficiency and user fairness.

Abstract

Cell-free Massive MIMO systems promise unprecedented spectral efficiency by coherently serving users with a large number of distributed Access Points (APs). A key practical challenge, however, is the high capacity required for the fronthaul links connecting these APs to a central processing unit. To reduce cost and power, these links must employ low-resolution quantization, which introduces distortion that can severely degrade system performance. This paper tackles this problem by proposing a novel, scalable receiver scheme: the Quantization-Aware Partial MMSE (QA-P-MMSE) receiver. Unlike conventional methods that either ignore quantization effects or require non-scalable centralized processing, our proposed receiver explicitly incorporates the statistics of the quantization noise into its design. We demonstrate through simulations that the QA-P-MMSE receiver significantly outperforms other scalable schemes, such as Maximum-Ratio (MR) and Partial-MMSE (P-MMSE), in terms of both average spectral efficiency and user fairness. Crucially, it approaches the performance of an ideal, non-scalable MMSE receiver with unquantized fronthaul, proving its efficacy as a practical and high-performance solution for next-generation cellfree networks. Furthermore, energy efficiency analysis reveals that the proposed scheme maximizes bits-per-joule performance at 4-bit resolution, aligning with green 6G targets.

Read PDF

Similar papers

Conference Aug 2026

Achievable Rate Analysis of Non-Ideal Cell-Free Massive MIMO Systems with RSMA

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a promising architecture for future wireless networks, yet its practical deployment is severely hindered by hardware impairments and time-varying channel conditions. To address these challenges, we investigate downlink transmission in a rate-splitting multi...

Le-Chen Li, Yao Zhang, Wenchao Xia et al. · 0 citations
2026

Efficient MMSE Receiver Design for MU-MIMO Systems With Symbol-Level Precoding

Symbol-level precoding (SLP) can achieve remarkable gains in multi-user multiple-input-single-output (MU-MISO) systems, while its extension to multi-user multiple-input-multiple-output (MU-MIMO) systems offers even greater potential by exploiting the spatial dimensions at both the transmitter and receiver. However, exi...

Xiao Tong, Lei Lei, Xiao-Yan Hu et al. · 0 citations
2026

Performance Analysis of Repeater-Assisted Massive MIMO Systems With Asynchronous Reception

Repeater-assisted massive MIMO (RA-MIMO) provides a cost-effective solution for distributed macro-diversity without the high-capacity fronthaul of cell-free architectures. However, the distributed deployment of repeaters introduces propagation delay differences and timing misalignments, causing asynchronous reception a...

Peng-Zhe Xin, Wan-Qing Cao, Yue Wu et al. · 0 citations
Preprint Aug 2026

Joint Quantized Precoding and Bit Allocation for Fronthaul-Constrained Cell-Free Massive MIMO

We study quantization-aware precoding for the downlink of cell-free massive MIMO systems with limited-resolution fronthaul. In such systems, the centrally designed precoder must be quantized before being conveyed to distributed access points (APs), creating a strong coupling between precoder design and fronthaul compre...

Özlem Tuğfe Demir · 1 citation
Preprint Aug 2026

Robust Beamforming and Power Allocation for Coherent Cell-Free Massive MIMO with Residual Calibration Errors

A time-evolving RCE model is developed that characterizes the joint effects of residual phase mismatches, residual carrier frequency offsets, and oscillator phase noise, and a Gauss--Legendre quadrature-based weighted minimum mean square error (WMMSE) optimization framework is developed.

Mingjun Sun, Xi-Dong Mu, Shaochuan Wu et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.