  The emerging data-driven methods based on artificial intelligence (AI) have
paved the way for intelligent, flexible, and adaptive network management in
vehicular applications. To enhance network management towards network
automation, this article presents a digital twin (DT) assisted two-tier
learning framework, which facilitates the automated life-cycle management of
machine learning based intelligent network management functions (INMFs).
Specifically, at a high tier, meta learning is employed to capture different
levels of general features for the INMFs under nonstationary network
conditions. At a low tier, individual learning models are customized for local
networks based on fast model adaptation. Hierarchical DTs are deployed at the
edge and cloud servers to assist the two-tier learning process, through
closed-loop interactions with the physical network domain. Finally, a case
study demonstrates the fast and accurate model adaptation ability of meta
learning in comparison with benchmark schemes.
