# Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation

arXiv: https://arxiv.org/abs/1903.10611 · HTML: https://mlsanity.com/paper/1903.10611/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2019.

## Built on by (most cited first, 16 of 16)

- [Cell-Free Multi-User MIMO Equalization via In-Context Learning](https://mlsanity.com/paper/2404.05538/) (2024)
- [Deep Learning-based Power Control for Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2102.10366/) (2021)
- [Rendering Wireless Environments Useful for Gradient Estimators: A Zero-Order Stochastic Federated Learning Method](https://mlsanity.com/paper/2401.17460/) (2024)
- [How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?](https://mlsanity.com/paper/2107.09577/) (2021)
- [Hierarchical Multi-Agent DRL Based Dynamic Cluster Reconfiguration for UAV Mobility Management](https://mlsanity.com/paper/2412.16167/) (2024)
- [Meta-Heuristic Fronthaul Bit Allocation for Cell-free Massive MIMO Systems](https://mlsanity.com/paper/2403.19132/) (2024)
- [In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory](https://mlsanity.com/paper/2506.15176/) (2025)
- [Handoff Design in User-Centric Cell-Free Massive MIMO Networks Using DRL](https://mlsanity.com/paper/2507.20966/) (2025)
- [Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks](https://mlsanity.com/paper/2512.17466/) (2025)
- [Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning](https://mlsanity.com/paper/2106.16194/) (2021)
- [Deep Learning-Based Power Control for Uplink Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2110.09001/) (2021)
- [Exploiting Deep Reinforcement Learning for Edge Caching in Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2208.12453/) (2022)
- [Joint Sequential Fronthaul Quantization and Hardware Complexity Reduction in Uplink Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2405.01303/) (2024)
- [Robust Learning-Based Sparse Recovery for Device Activity Detection in Grant-Free Random Access Cell-Free Massive MIMO: Enhancing Resilience to Impairments](https://mlsanity.com/paper/2503.10280/) (2025)
- [Variational Bayesian Channel Estimation and Data Detection for Cell-Free Massive MIMO with Low-Resolution Quantized Fronthaul Links](https://mlsanity.com/paper/2506.18863/) (2025)
- [Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems](https://mlsanity.com/paper/2606.24416/) (2026)

## Abstract (arXiv)

Cell-free Massive MIMO is considered as a promising technology for satisfying the increasing number of users and high rate expectations in beyond-5G networks. The key idea is to let many distributed access points (APs) communicate with all users in the network, possibly by using joint coherent signal processing. The aim of this paper is to provide the first comprehensive analysis of this technology under different degrees of cooperation among the APs. Particularly, the uplink spectral efficiencies of four different cell-free implementations are analyzed, with spatially correlated fading and arbitrary linear processing. It turns out that it is possible to outperform conventional Cellular Massive MIMO and small cell networks by a wide margin, but only using global or local minimum mean-square error (MMSE) combining. This is in sharp contrast to the existing literature, which advocates for maximum-ratio combining. Also, we show that a centralized implementation with optimal MMSE processing not only maximizes the SE but largely reduces the fronthaul signaling compared to the standard distributed approach. This makes it the preferred way to operate Cell-free Massive MIMO networks. Non-linear decoding is also investigated and shown to bring negligible improvements.