Beyond-5G data collection towards Network Digital Twins

The emergence of 6G networks introduces new challenges, including the planning of ultra-dense networks that can be optimized through Network Digital Twins (NDTs). In smart cities, NDTs are especially valuable, as they integrate network and mobility data to enable adaptive and intelligent management. This work proposes a NDT designed to analyze the impact of urban mobility on 5G networks. The NDT environment integrates real and simulated urban mobility with emulated 5G base stations, emulating real vehicles in the road accessing 5G network services while on the move. The proposed system leverages metric collection for beyond-5G networks with real-time and historical capabilities, to support the envisioned simulations. The system combines real-world data collection with large-scale network emulation using open-source projects, such as SUMO framework, UERANSIM, and Kubernetes to simulate multiple V2X connections between vehicles and 5G base stations. The proposed NDT bridges urban mobility and network behavior, providing a foundation for planning and emulating beyond-5G network management solutions in mobile and dynamic environments. Several scenarios have been tested, such as random, a marathon with road blocks, and lane closure. The results show a high aggregation of vehicles and data traffic in one of the 5G antennas when blocking roundabouts on fast roads in comparison to the random scenario; moreover, the closure of a double entry lane to the roundabout at the location of the 5G gNodeB translates to much lower peak utilization and a better balance of antenna resources during the running scenarios.