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

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 locationspecific 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 evergrowing database is utilized to analyze the KPI requirements of selected vehicular applications. For an example ToD target zone, it is shown that a multiMobile 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.