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.

翻译：本文探讨了在真实第五代工业物联网（IIoT）网络的时空相关性特征下实现超可靠低延迟通信（URLLC）的问题。在此背景下，我们考虑了5G互联工业与自动化联盟（5G-ACIA）推广的常见独立非公共网络（SNPN）架构，并提出了一种5G NR半持久调度器（SPS）的新变体以处理上行流量相关性。将采用"智能"调度器（SSPS）的基准方案与需要调度器估计某些未知网络参数的更实际的自适应方案（ASPS）进行了比较。通过仿真验证，两种方案均能满足URLLC的1毫秒延迟要求，但需在流量管理方面引入一定复杂度作为代价。最后，我们根据用例、工厂内机器数量，并考虑周期性和非周期性流量，提供了用于规模设计IIoT网络的数值指导原则。