# Scalability Aspects of Cell-Free Massive MIMO

arXiv: https://arxiv.org/abs/1902.11275 · HTML: https://mlsanity.com/paper/1902.11275/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [A Deep Learning Approach for User-Centric Clustering in Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2410.02775/) (2024)
- [Learning Decentralized Power Control in Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2303.02573/) (2023)
- [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)

## Abstract (arXiv)

Ubiquitous cell-free massive MIMO (multiple-input multiple-output) combines massive MIMO technology and user-centric transmission in a distributed architecture. All the access points (APs) in the network cooperate to jointly and coherently serve a smaller number of users in the same time-frequency resource. However, this coordination needs significant amounts of control signalling which introduces additional overhead, while data co-processing increases the back/front-haul requirements. Hence, the notion that the "whole world" could constitute one network, and that all APs would act as a single base station, is not scalable. In this study, we address some system scalability aspects of cell-free massive MIMO that have been neglected in literature until now. In particular, we propose and evaluate a solution related to data processing, network topology and power control. Results indicate that our proposed framework achieves full scalability at the cost of a modest performance loss compared to the canonical form of cell-free massive MIMO.