DIGITAL TWIN–DRIVEN 6G CAMPUS NETWORKS: INTELLIGENT FAULT RECOVERY AND ENERGY OPTIMIZATION

Abstract

This paper puts forward a framework for Digital Twin–driven 6G campus networks that are both private and autonomous and are supported by the convergence of agentic AI and Service Management and Orchestration (SMO) models to facilitate self-managing operations. The new network system uses the concept of digital twins for the purposes of intelligently forecasting faults and autonomously taking corrective actions thereby raising the reliability of the network to a very high level while the downtime is kept to a minimum. It moreover includes energy-environment-friendly self-optimization that is the dynamic scaling of resources to the extent that supersedes performance and efficiency. In addition, the framework allows for planning of the strategic “what-if” scenarios enabling the operators of the network to simulate the maintenance strategies, capacity increases, and traffic changes before putting them into practice. The findings of the simulation indicate that the method not only significantly enhances network resilience but also energy efficiency and operational foresight thus marking digital twin–backed 6G private networks as an important factor in the development of next-generation campus connectivity.