Autonomous Network Management in Multi-Domain 6G Networks based on Graph Neural Networks

Sixth-generation (6G) networks propose integrating multiple networks and domains while improving network performance. Hence, today’s networks are becoming increasingly larger and more complex. Traditional methods to manage networks are facing significant challenges as the topology sizes, traffic patterns, and network domains are changing.This paper presents the state-of-the-art in literature for network management and proposes a research plan for an autonomous network management framework fueled by the Digital Twin (DT) paradigm. Unlike the existing methods such as Queuing Theory (QT) or network simulation studies, the proposed framework relies on state-of-the-art Graph Neural Networks (GNNs) for network performance analysis. We argue that seamless integration of networks while improving performance guarantees can be achieved via autonomous management of networks and present a research plan in this paper.