2024/03/24 by Kaige Qu, Weihua Zhuang, Qu, Kaige +1 · 1 citation
Computer Science · Engineering · #Cognitive Computing and Networks #Digital Transformation in Industry #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI)
paper · pdf · doi:10.48550/arxiv.2403.16021
openalex publication_date 2024/03/24 · openalex created_date 2024/03/27 · openalex updated_date 2026/07/28
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.