# Enabling Ultra Reliable Wireless Communications for Factory Automation with Distributed MIMO

## TL;DR

This paper investigates distributed MIMO schemes for factory automation to achieve ultra-reliable wireless communication, proposing a max-min power allocation algorithm and analyzing the impact of impulsive noise through extensive simulations.

## Contribution

It introduces a max-min power allocation algorithm for distributed MIMO in factory scenarios and evaluates its effectiveness under realistic conditions including impulsive noise.

## Key findings

- Distributed MIMO with ZF beamforming and MPA can significantly improve SINR.
- Impulsive noise can severely degrade system performance, requiring mitigation techniques.
- Simulations show potential SINR gains of tens of dB over centralized MIMO.

## Abstract

Factory automation is one of the most challenging use cases for 5G-and-beyond mobile networks due to strict latency, availability and reliability constraints. In this work, an indoor factory scenario is considered, and distributed multiple-input multiple-output (MIMO) schemes are investigated in order to enable reliable communication to the actuators (ACs) active in the factory. Different levels of coordination among the access points serving the ACs and several beamforming schemes are considered and analyzed. To enforce system reliability, a max-min power allocation (MPA) algorithm is proposed, aimed at improving the signal to interference plus noise ratio (SINR) of the ACs with the worst channel conditions. Extensive system simulations are performed in a realistic scenario, which includes a new path-loss model based on recent measurements in factory scenarios, and, also, the presence of non-Gaussian impulsive noise. Numerical results show that distributed MIMO schemes with zero-forcing (ZF) beamforming and MPA have the potential of providing SINR gains in the order of tens of dB with respect to a centralized MIMO deployment, as well as that the impulsive noise can strongly degrade the system performance and thus requires specific detection and mitigation techniques.