# On the Achievable Rates of Decentralized Equalization in Massive MU-MIMO Systems

## TL;DR

This paper analyzes the achievable data rates of decentralized equalization methods in massive MU-MIMO systems, proposing architectures and algorithms that reduce complexity with minimal rate loss.

## Contribution

It introduces two decentralized BS architectures and a novel AMP-based equalization algorithm, demonstrating near-centralized performance in massive MU-MIMO.

## Key findings

- Decentralized equalization achieves comparable rates to centralized methods.
- AMP-based algorithms show negligible rate loss.
- Architectures reduce interconnect and processing complexity.

## Abstract

Massive multi-user (MU) multiple-input multiple-output (MIMO) promises significant gains in spectral efficiency compared to traditional, small-scale MIMO technology. Linear equalization algorithms, such as zero forcing (ZF) or minimum mean-square error (MMSE)-based methods, typically rely on centralized processing at the base station (BS), which results in (i) excessively high interconnect and chip input/output data rates, and (ii) high computational complexity. In this paper, we investigate the achievable rates of decentralized equalization that mitigates both of these issues. We consider two distinct BS architectures that partition the antenna array into clusters, each associated with independent radio-frequency chains and signal processing hardware, and the results of each cluster are fused in a feedforward network. For both architectures, we consider ZF, MMSE, and a novel, non-linear equalization algorithm that builds upon approximate message passing (AMP), and we theoretically analyze the achievable rates of these methods. Our results demonstrate that decentralized equalization with our AMP-based methods incurs no or only a negligible loss in terms of achievable rates compared to that of centralized solutions.