# Variational Bayesian Channel Estimation and Data Detection for Cell-Free Massive MIMO with Low-Resolution Quantized Fronthaul Links

arXiv: https://arxiv.org/abs/2506.18863 · HTML: https://mlsanity.com/paper/2506.18863/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 23 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [Scalable Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/1908.03119/) (2019)
- [Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation](https://mlsanity.com/paper/1903.10611/) (2019)
- [Five Disruptive Technology Directions for 5G](https://mlsanity.com/paper/1312.0229/) (2013)
- [Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems](https://mlsanity.com/paper/2110.15928/) (2021)
- [Joint Activity Detection and Channel Estimation in Cell-Free Massive MIMO Networks with Massive Connectivity](https://mlsanity.com/paper/2112.13013/) (2021)
- [A Variational Bayesian Perspective on Massive MIMO Detection](https://mlsanity.com/paper/2205.11649/) (2022)
- [Grant-Free Access via Bilinear Inference for Cell-Free MIMO with Low-Coherent Pilots](https://mlsanity.com/paper/2009.12863/) (2020)
- [Variational Bayes for Joint Channel Estimation and Data Detection in Few-Bit Massive MIMO Systems](https://mlsanity.com/paper/2212.01717/) (2022)
- [Joint CFO, Gridless Channel Estimation and Data Detection for Underwater Acoustic OFDM Systems](https://mlsanity.com/paper/2107.07101/) (2021)
- [Variational Bayes Inference for Data Detection in Cell-Free Massive MIMO](https://mlsanity.com/paper/2301.04260/) (2023)
- [Bilinear Expectation Propagation for Distributed Semi-Blind Joint Channel Estimation and Data Detection in Cell-Free Massive MIMO](https://mlsanity.com/paper/2312.11688/) (2023)

## Abstract (arXiv)

We study the joint channel estimation and data detection (JED) problem in a cell-free massive multiple-input multiple-output (CF-mMIMO) network, where access points (APs) communicate with a central processing unit (CPU) over fronthaul links. However, the bandwidth of these links is limited, and thus, presents challenges to the applicability of CF-mMIMO, especially with an ever-increasing number of users. To address this, we propose a method based on variational Bayesian (VB) inference for performing the JED process, where the APs forward low-resolution quantized versions of the signals to the CPU. We consider two approaches: quantization-and-estimation (Q-E) and estimation-and-quantization (E-Q). In the Q-E approach, each AP uses a low-bit quantizer to quantize the signal before forwarding it to the CPU, while in the E-Q approach, each AP first performs local channel estimation and then sends a low-bit quantized version of the estimated channel to the CPU. We evaluate the performance of our VB-based approach under perfect fronthaul link (PFL) with unquantized received signals, Q-E, and E-Q in terms of symbol error rate (SER), normalized mean square error (NMSE) of the channel estimation, computational complexity, and fronthaul signaling overhead. We also compare these results with those of the linear minimum mean squared error (LMMSE) method under the PFL scenario. Our numerical results show that both the VB(Q-E) and VB(E-Q) approaches achieve superior performance compared to LMMSE(PFL), benefiting from the nonlinear modeling inherent in VB. Furthermore, the VB(Q-E) method outperforms VB(E-Q) due to errors in the local channel estimation process at the APs within the VB(E-Q) approach.