When Digital Twin Meets Generative AI: Intelligent Closed-Loop Network Management
Xinyu Huang, Haojun Yang, Conghao Zhou, Mingcheng He, Xuemin Shen, Weihua Zhuang
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.