Advancing Network Digital Twin Framework for Generating Realistic Datasets
This work provides a scalable, open-source tool for generating realistic wireless network datasets, lowering the entry barrier for research in virtualized and intelligent communication systems.
The paper presents an open-source Network Digital Twin (NDT) framework integrating vehicular mobility, ray tracing, and network simulation to generate realistic datasets for wireless networks, enabling reproducible experimentation and benchmarking of ML-based network algorithms.
The integration of accurate and reproducible wireless network simulations is a key enabler for research on open, virtualized, and intelligent communication systems. Network Digital Twins (NDTs) provide a scalable alternative to costly and time-consuming measurement campaigns, while enabling controlled experimentation and data generation for data-driven network design. In this paper, we present an open and user-friendly NDT framework that integrates controllable vehicular mobility with the site-specific ray tracer Sionna and the discrete-event ns-3 network simulator, enabling virtualized end-to-end modeling of wireless networks across the radio, network, and application layers. The proposed framework is particularly well-suited for dynamic vehicular networks and urban deployments, supporting realistic mobility, traffic dynamics, and the extraction of cross-layer metrics. To promote open-source initiatives, we release both the NDT implementation and a representative dataset generated from realistic vehicular and urban scenarios. The framework and dataset facilitate reproducible experimentation and benchmarking of machine learning-based quality of service prediction, network optimization, and intelligent network management algorithms, lowering the entry barrier for research on virtual and open wireless network services.