# Scalable Cell-Free Massive MIMO Systems

arXiv: https://arxiv.org/abs/1908.03119 · HTML: https://mlsanity.com/paper/1908.03119/

## Key facts

- Cited by 19 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 8 Aug 2019.

## Built on by (most cited first, 19 of 19)

- [Towards Semantic Communication Protocols: A Probabilistic Logic Perspective](https://mlsanity.com/paper/2207.03920/) (2022)
- [Deep Learning-based Power Control for Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2102.10366/) (2021)
- [Hybrid Knowledge-Data Driven Channel Semantic Acquisition and Beamforming for Cell-Free Massive MIMO](https://mlsanity.com/paper/2307.03070/) (2023)
- [Self-Organizing mmWave MIMO Cell-Free Networks With Hybrid Beamforming: A Hierarchical DRL-Based Design](https://mlsanity.com/paper/2103.11823/) (2021)
- [Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO](https://mlsanity.com/paper/2212.06482/) (2022)
- [A Deep Learning Approach for User-Centric Clustering in Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2410.02775/) (2024)
- [Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systems](https://mlsanity.com/paper/2502.13390/) (2025)
- [Handoff Design in User-Centric Cell-Free Massive MIMO Networks Using DRL](https://mlsanity.com/paper/2507.20966/) (2025)
- [Deep Learning-Based Power Control for Uplink Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2110.09001/) (2021)
- [Learning Optimal Linear Precoding for Cell-Free Massive MIMO with GNN](https://mlsanity.com/paper/2406.04456/) (2024)
- [Rapid and Power-Aware Learned Optimization for Modular Receive Beamforming](https://mlsanity.com/paper/2408.00439/) (2024)
- [Scalable Multivariate Fronthaul Quantization for Cell-Free Massive MIMO](https://mlsanity.com/paper/2409.06715/) (2024)
- [A General Framework for Scalable UE-AP Association in User-Centric Cell-Free Massive MIMO based on Recurrent Neural Networks](https://mlsanity.com/paper/2503.04278/) (2025)
- [CWGAN-GP Augmented CAE for Jamming Detection in 5G-NR in Non-IID Datasets](https://mlsanity.com/paper/2506.15075/) (2025)
- [Robust Precoding for Resilient Cell-Free Networks](https://mlsanity.com/paper/2512.00531/) (2025)
- [Scalable Cross-Attention Transformer for Cooperative Multi-AP OFDM Uplink Reception](https://mlsanity.com/paper/2602.04728/) (2026)
- [A General Deep Learning Framework for Wireless Resource Allocation under Discrete Constraints](https://mlsanity.com/paper/2603.19322/) (2026)
- [Toward Resilient 5G Networks: Comparative Analysis of Federated and Centralized Learning for RF Jamming Detection](https://mlsanity.com/paper/2605.01705/) (2026)
- [Beamforming Design Via GNN in mmWave Cell-Free Massive MIMO Using Sub-6 GHz CSI](https://mlsanity.com/paper/2608.30524/) (2026)

## Abstract (arXiv)

Imagine a coverage area with many wireless access points that cooperate to jointly serve the users, instead of creating autonomous cells. Such a cell-free network operation can potentially resolve many of the interference issues that appear in current cellular networks. This ambition was previously called Network MIMO (multiple-input multiple-output) and has recently reappeared under the name Cell-Free Massive MIMO. The main challenge is to achieve the benefits of cell-free operation in a practically feasible way, with computational complexity and fronthaul requirements that are scalable to large networks with many users. We propose a new framework for scalable Cell-Free Massive MIMO systems by exploiting the dynamic cooperation cluster concept from the Network MIMO literature. We provide a novel algorithm for joint initial access, pilot assignment, and cluster formation that is proved to be scalable. Moreover, we adapt the standard channel estimation, precoding, and combining methods to become scalable. A new uplink and downlink duality is proved and used to heuristically design the precoding vectors on the basis of the combining vectors. Interestingly, the proposed scalable precoding and combining outperform conventional maximum ratio processing and also performs closely to the best unscalable alternatives.