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

## TL;DR

This paper analyzes cell-free Massive MIMO with MMSE processing, demonstrating that centralized MMSE combining significantly outperforms traditional methods and reduces signaling, making it a promising approach for beyond-5G networks.

## Contribution

It provides the first comprehensive analysis of cell-free Massive MIMO with various cooperation levels, highlighting the superiority of centralized MMSE processing over other methods.

## Key findings

- Global or local MMSE combining outperforms maximum-ratio combining.
- Centralized MMSE implementation maximizes spectral efficiency and reduces fronthaul signaling.
- Non-linear decoding offers negligible improvements.

## Abstract

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.