  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.
