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.

翻译：基于人工智能（AI）的新兴数据驱动方法为车载应用中的智能、灵活和自适应网络管理铺平了道路。为提升网络管理以迈向网络自动化，本文提出了一种数字孪生（DT）辅助的两层学习框架，该框架促进了基于机器学习的智能网络管理功能（INMFs）的自动化生命周期管理。具体而言，在高层中，采用元学习来捕获非平稳网络条件下INMFs的不同层次通用特征；在低层中，基于快速模型自适应为局部网络定制个性化学习模型。分层DT部署在边缘服务器和云端服务器上，通过与物理网络域的闭环交互来辅助上述两层学习过程。最后，通过案例研究展示了元学习相较于基准方案具有快速且精准的模型自适应能力。