A Measurement-Based Spatially Consistent Channel Model for Distributed MIMO in Industrial Environments

TL;DR

This paper develops a measurement-based, spatially consistent channel model for distributed MIMO systems in industrial environments, capturing key channel characteristics for URLLC applications.

Contribution

It introduces a novel channel model derived from extensive measurements, including spatial correlation and tail distributions, tailored for industrial D-MIMO scenarios.

Findings

Channel characteristics include small-scale and large-scale fading, delay spread, and LOS transition rates.

Correlations of large-scale fading are modeled using Gaussian random fields.

Tail distributions enable evaluation of reliability and rare events.

Abstract

Future wireless communication systems are envisioned to support ultra-reliable and low-latency communication (URLLC), which will enable new applications such as compute offloading, wireless real-time control, and reliable monitoring. Distributed multiple-input multiple-output (D-MIMO) is one of the most promising technologies for delivering URLLC. This paper classifies obstructions and derives a channel model from a D-MIMO measurement campaign carried out at a carrier frequency of 3.75 GHz with a bandwidth of 35 MHz using twelve fully coherent distributed dipole antennas in an industrial environment. Channel characteristics are investigated, including statistical measures such as small-scale fading, large-scale fading, delay spread, and transition rates between line-of-sight and obstructed line-of-sight conditions for the different antenna elements, laying the foundations for an accurate channel model for D-MIMO systems in industrial environments. Furthermore, to ensure spatial consistent simulation results the correlations of large-scale fading between antennas are modeled using Gaussian random fields. Lastly, tail distributions are included to enable proper evaluations of reliability and rare events. Based on the results, a channel model for D-MIMO in industrial environments is presented together with a recipe for its implementation.