GH-Twin: Graph Learning Empowered Hierarchical Digital Twin for Optimizing Self-Healing Networks

Abstract

Communication networks are witnessing a fast evolution towards Beyond 5G (B5G), bringing unprecedented complexities and challenges for optimizing networks in guaranteeing self-healing abilities and maintaining quality of services (QoS). To this end, this study presents a Graph Learning-driven Hierarchical Digital Twin framework, called GH-Twin, to build a reliable virtual replica of network components and their communications between different layers, leading to inclusive network representation. The proposed framework introduces graph cross-learning (GCL) distributed across different participants to devise competent predictive modelling of network performance collaboratively and preemptively recognize abnormalities in network settings. To preserve local privacy, differential privacy is applied by injecting some Gaussian into the parameters of local GCL before sharing it with the global coordinator. Proof of concept simulations has demonstrated that GH-Twin can precisely predict flow-level QoS and recognize anomalous links and nodes under different network topologies.