Future vehicular applications like Tele-Operated Driving (ToD) and Communication-Based Train Control (CBTC) pose demanding requirements on mobile communication networks. Despite continuous 5G technology upgrades and expansion strategies, mobile networks cannot provide a full-coverage service guarantee for the required mission-critical Key Performance Indicators (KPIs). However, application and location-specific Quality of Service (QoS) predictions are crucial to reliably meet the highest QoS compliance of emerging future smart city services.Therefore, this paper proposes a digital twin capable of merging connectivity data with arbitrary application domains to derive KPI predictions for mission-critical applications. The potential of the proposed approach is illustrated based on a case study in the urban area of Dortmund, Germany, considering data rate and latency predictions for mobile applications. In this context, a continuous data flow for the multi-dimensional mobile network twin is acquired using a massive, multimodal measurement campaign enabled by road and rail-based vehicles. This ever-growing database is utilized to analyze the KPI requirements of selected vehicular applications.For an example ToD target zone, it is shown that a multi-Mobile Network Operator (MNO) approach increases the KPI fulfillment of direct control ToD from approximately 70% to 90% compared to a single MNO. By further restricting the ToD zone and combining two MNOs, a ToD-ready zone with 100% fulfillment of the KPIs is reached.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Data-Driven Digital Mobile Network Twin Enabling Mission-Critical Vehicular Applications


    Contributors:


    Publication date :

    2023-06-01


    Size :

    11507057 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Digital Twin Enabled Data-Driven Approach for Traffic Efficiency and Software-Defined Vehicular Network Optimization

    Shahriar, Mohammad Sajid / Subramaniam, Suresh / Matsuura, Motoharu et al. | IEEE | 2024


    A Data Synchronization Incentive Scheme in Vehicular Digital Twin Network with Stackelberg Game

    Tan, Jingru / Yang, Xiaoqing / Zheng, Jinkai et al. | IEEE | 2024


    Enabling vehicular safety applications over LTE networks

    Kato, Seiya / Hiltunen, Matti / Joshi, Kaustubh et al. | IEEE | 2013


    Blockchain Assisted Intra-Twin and Inter-Twin Authentication Scheme for Vehicular Digital Twin System

    Gautam, Deepika / Thakur, Garima / Kumar, Pankaj et al. | IEEE | 2024


    Digital Twin-Driven Vehicular Task Offloading and IRS Configuration in the Internet of Vehicles

    Yuan, Xiaoming / Chen, Jiahui / Zhang, Ning et al. | IEEE | 2022