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.

翻译：精确且可复现的无线网络仿真的集成是开放式、虚拟化及智能化通信系统研究的关键使能技术。网络数字孪生（NDTs）为昂贵且耗时的实际测量活动提供了一种可扩展的替代方案，同时能够实现受控实验与数据驱动网络设计所需的数据生成。本文提出一个开源且用户友好的NDT框架，该框架将可控车辆移动性与基于特定场景的光线追踪器Sionna及离散事件网络模拟器ns-3相结合，实现了跨无线层、网络层及应用层的虚拟化端到端无线网络建模。所提框架尤其适用于动态车载网络与城市部署场景，支持真实移动性建模、动态流量模拟及跨层指标提取。为促进开源计划，我们发布了NDT实现代码及基于真实车载与城市场景生成的代表性数据集。该框架与数据集有助于推动基于机器学习的服务质量预测、网络优化及智能网络管理算法的可复现实验与基准测试，从而降低虚拟及开放无线网络服务研究的技术门槛。