mlsanityData to 14 Oct 2026

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

arXiv 1903.10611 ↗First posted 25 Mar 2019cs.IT eess.SP math.ITcited by 16

Not on the map. Why?

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.

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

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 1; 2023: 0; 2024: 5; 2025: 5; 2026: 1.

16 papers.

Sort:

By area:

  1. 2024Cell-Free Multi-User MIMO Equalization via In-Context Learningcited by 8
  2. 2021Deep Learning-based Power Control for Cell-Free Massive MIMO Networkscited by 4
  3. 2024Rendering Wireless Environments Useful for Gradient Estimators: A Zero-Order Stochastic Federated Learning Methodcited by 4
  4. 2021How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?cited by 3
  5. 2024Hierarchical Multi-Agent DRL Based Dynamic Cluster Reconfiguration for UAV Mobility Managementcited by 3
  6. 2024Meta-Heuristic Fronthaul Bit Allocation for Cell-free Massive MIMO Systemscited by 2
  7. 2025In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theorycited by 1
  8. 2025Handoff Design in User-Centric Cell-Free Massive MIMO Networks Using DRLcited by 1
  9. 2025Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networkscited by 1
  10. 2021Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learningcited by 0
  11. 2021Deep Learning-Based Power Control for Uplink Cell-Free Massive MIMO Systemscited by 0
  12. 2022Exploiting Deep Reinforcement Learning for Edge Caching in Cell-Free Massive MIMO Systemscited by 0
4 more
  1. 2024Joint Sequential Fronthaul Quantization and Hardware Complexity Reduction in Uplink Cell-Free Massive MIMO Networkscited by 0
  2. 2025Robust Learning-Based Sparse Recovery for Device Activity Detection in Grant-Free Random Access Cell-Free Massive MIMO: Enhancing Resilience to Impairmentscited by 0
  3. 2025Variational Bayesian Channel Estimation and Data Detection for Cell-Free Massive MIMO with Low-Resolution Quantized Fronthaul Linkscited by 0
  4. 2026Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systemscited by 0