AI for industrial: automate the network design for 5G URLLC services

Abstract

Fifth generation (5G) mobile networks enable ultra-reliable low-latency communication (URLLC) applications, ushering in an era of endless possibilities for 5G. URLLC supports emerging 5G services and applications with stringent requirements for latency and reliability. Factory automation (FA) is a URLLC application that automates and optimizes workflows and processes in factories. To accommodate diversified FA services, 5G networks employ the “network slicing” technique, which divides the network into slices tailored to different service requirements. Designing a sliced network and translating diversified service-level agreements (SLAs) into network attributes necessitates advanced automation techniques to enhance human–machine collaboration, increase efficiency, minimize manual errors, reduce operating costs, and, most importantly, provide adequate service quality economically and reliably. To apply autonomic computing to FA network design, new architectures and software components have been envisioned. These include information extraction, domain knowledge representation, rule-based reasoning, performance model calculation, and querying using simulators and neural networks (NNs), among others. This paper proposes an innovative approach to network slicing design using advanced automation methods. This approach can be easily extended to include new services or to integrate cutting-edge 5G techniques.