Network digital twins (NDTs) facilitate the estimation of key performance indicators (KPIs) before physically implementing a network, thereby enabling efficient optimization of the network configuration. In this paper, we propose a learning-based NDT for network simulators. The proposed method offers a holistic representation of information flow in a wireless network by integrating node, edge, and path embeddings. Through this approach, the model is trained to map the network configuration to KPIs in a single forward pass. Hence, it offers a more efficient alternative to traditional simulation-based methods, thus allowing for rapid experimentation and optimization. Our proposed method has been extensively tested through comprehensive experimentation in various scenarios, including wired and wireless networks. Results show that it outperforms baseline learning models in terms of accuracy and robustness. Moreover, our approach achieves comparable performance to simulators but with significantly higher computational efficiency.

翻译：网络数字孪生（NDTs）能够在物理网络部署之前对关键性能指标（KPIs）进行估计，从而实现对网络配置的高效优化。本文提出一种基于学习的网络数字孪生方法，用于网络模拟器。该方法通过整合节点、边和路径嵌入，对无线网络中的信息流进行整体表征。通过这一方式，模型被训练为能在单次前向传播中将网络配置映射至KPIs。因此，该方法相较于传统基于仿真的方法提供了更高效的替代方案，可实现快速实验与优化。我们通过在包括有线和无线网络在内的多种场景下进行广泛实验，对所提方法进行了全面测试。结果表明，该方法在准确性和鲁棒性上均优于基线学习模型。此外，我们的方法达到了与模拟器相当的性能，但计算效率显著更高。