Practice on fifth-generation core (5GC) network fault self-recovery based on a Digital Twin

Abstract

Background: The development of cloud-based, service-focused and intelligent networks has increased the demand for highly reliable, error-tolerant and computationally efficient means of reducing the costs associated with network operation, maintenance, testing and innovations. Methods: We present a fault self-recovery method for fifth-generation core (5GC) networks. Data models are built according to the data governance approach to include the equipment, links and services of the physical network in the digital twin. Visual topology technology is used to extract knowledge-as-a-service (KaaS) capabilities such as call quality tests, fault-propagation chain reasoning and disaster recovery analysis. Results: The proposed method realises 5GC closed-loop self-recovery through four processes: perception, analysis, decision-making and execution. In tests, it achieved 5GC network fault detection in 1 min, delimitation in 20 min, and recovery in 5 min. Conclusions: Through the network digital twin technology, based on the model and state data, the twinning capabilities such as simulation and event topology can be used to realize the network anomaly perception, fault rapid confinement and service survival decision, thus effectively improving the fault processing efficiency and reducing the fault impact.