Advancing Network Digital Twin Framework for Generating Realistic Datasets

2026-04-14

AI Summary

This work addresses the lack of reproducibility and high-fidelity modeling in existing wireless network simulation platforms, which hinders research on open, virtualized, and intelligent communication systems. The authors propose an open-source, user-friendly network digital twin framework that, for the first time, deeply integrates the Sionna ray tracer with the ns-3 discrete-event simulator, augmented by a controllable vehicular mobility model to enable end-to-end, cross-layer fidelity from the physical to the application layer. The framework supports dynamic vehicular networks and urban scenarios, facilitating cross-layer metric extraction, and is accompanied by publicly released code and datasets. By providing a realistic and reproducible experimental environment, this framework significantly lowers the barrier to developing and evaluating machine learning algorithms for QoS prediction, network optimization, and intelligent management, thereby advancing benchmarking and reproducible research in the field.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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.

Problem

• Network Digital Twin
• realistic dataset generation
• vehicular networks
• wireless network simulation
• cross-layer metrics

Innovation

• Network Digital Twin
• ray tracing
• ns-3
• vehicular mobility
• cross-layer modeling