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.

本文提出了一个基于学习的网络数字孪生用于网络模拟器，它可以综合表示无线网络中的信息流，并通过将网络配置映射到关键绩效指标来进行快速实验和优化。通过在多种情景下全面测试，结果表明该方法比传统的基于模拟的方法具有更高的精确性和鲁棒性，同时实现了比模拟器更高的计算效率。