# Scalable Cell-Free Massive MIMO Systems

## TL;DR

This paper introduces a scalable framework for Cell-Free Massive MIMO systems that enhances network coverage and interference management by employing dynamic cooperation clusters and scalable algorithms, outperforming traditional methods.

## Contribution

The paper proposes a novel scalable framework and algorithms for joint access, pilot assignment, and cluster formation in Cell-Free Massive MIMO, making large networks practically feasible.

## Key findings

- Scalable algorithms for joint access, pilot assignment, and cluster formation.
- Proved uplink/downlink duality for scalable precoding and combining.
- Proposed methods outperform conventional maximum ratio processing.

## Abstract

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.