When Digital Twin Meets Generative AI: Intelligent Closed-Loop Network Management

TL;DR

This paper introduces a novel GAI-driven digital twin architecture for intelligent, closed-loop network management, enhancing performance through innovative data processing and adaptive strategies.

Contribution

It proposes a new GAI-integrated digital twin framework with methods for model light-weighting, adaptive selection, and data-driven management for improved network control.

Findings

Effective GAI-based status emulation and decision-making

Enhanced network management through adaptive model strategies

Identification of open research challenges in GDT networks

Abstract

Generative artificial intelligence (GAI) and digital twin (DT) are advanced data processing and virtualization technologies to revolutionize communication networks. Thanks to the powerful data processing capabilities of GAI, integrating it into DT is a potential approach to construct an intelligent holistic virtualized network for better network management performance. To this end, we propose a GAI-driven DT (GDT) network architecture to enable intelligent closed-loop network management. In the architecture, various GAI models can empower DT status emulation, feature abstraction, and network decision-making. The interaction between GAI-based and model-based data processing can facilitate intelligent external and internal closed-loop network management. To further enhance network management performance, three potential approaches are proposed, i.e., model light-weighting, adaptive model selection, and data-model-driven network management. We present a case study pertaining to data-model-driven network management for the GDT network, followed by some open research issues.