Digital Twin Assisted Intelligent Network Management for Vehicular Applications

TL;DR

The paper tackles nonstationary spatio-temporal dynamics in vehicular networks that undermine static ML-based INMFs. It proposes a digital twin–assisted two-tier learning framework where cloud-hosted hierarchical meta models provide generalized knowledge and edge-hosted DTs enable fast, local customization through meta adaptation. Key contributions include the design of hierarchical meta models, hierarchical DT architecture, and offline/online interaction protocols that support automated life-cycle management of INMFs, demonstrated via a case study on RL-based cooperative perception. The approach promises faster, more robust network automation in dynamic 6G vehicular environments and offers a path toward broader space–air–ground integration in future networks.

Abstract

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.

Figures (6)

• Figure 1: A three-layer network architecture for vehicular networks.
• Figure 2: Stages in the life-cycle of a machine learning model.
• Figure 3: A digital-twin assisted two-tier learning framework for vehicular networks.
• Figure 4: An illustrative example of hierarchical meta models for different network categories based on time, city, road attributes.
• Figure 5: Interactions among cloud DT, edge DT, and PLVN. (a) for meta training. (b) for meta adaptation.