# A New Scheduler for URLLC in 5G NR IIoT Networks with Spatio-Temporal Traffic Correlations

## TL;DR

This paper proposes a new scheduler for URLLC in 5G IIoT networks that accounts for spatio-temporal traffic correlations, demonstrating latency fulfillment through simulations with adaptive and benchmark approaches.

## Contribution

It introduces a novel variant of the 5G NR semi-persistent scheduler tailored for IIoT URLLC with correlated traffic, including an adaptive method for unknown network parameters.

## Key findings

- Both schedulers meet 1-ms latency in simulations.
- Adaptive scheduler estimates network parameters effectively.
- Guidelines for network dimensioning based on use case and traffic type.

## Abstract

This paper explores the issue of enabling Ultra-Reliable Low-Latency Communications (URLLC) in view of the spatio-temporal correlations that characterize real 5th generation (5G) Industrial Internet of Things (IIoT) networks. In this context, we consider a common Standalone Non-Public Network (SNPN) architecture as promoted by the 5G Alliance for Connected Industries and Automation (5G-ACIA), and propose a new variant of the 5G NR semi-persistent scheduler (SPS) to deal with uplink traffic correlations. A benchmark solution with a "smart" scheduler (SSPS) is compared with a more realistic adaptive approach (ASPS) that requires the scheduler to estimate some unknown network parameters. We demonstrate via simulations that the 1-ms latency requirement for URLLC is fulfilled in both solutions, at the expense of some complexity introduced in the management of the traffic. Finally, we provide numerical guidelines to dimension IIoT networks as a function of the use case, the number of machines in the factory, and considering both periodic and aperiodic traffic.