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Network digital twins (NDTs) facilitate the estimation of key performanceindicators (KPIs) before physically implementing a network, thereby enablingefficient optimization of the network configuration. In this paper, we proposea learning-based NDT for network simulators. The proposed method offers aholistic representation of information flow in a wireless network byintegrating node, edge, and path embeddings. Through this approach, the modelis trained to map the network configuration to KPIs in a single forward pass.Hence, it offers a more efficient alternative to traditional simulation-basedmethods, thus allowing for rapid experimentation and optimization. Our proposedmethod has been extensively tested through comprehensive experimentation invarious scenarios, including wired and wireless networks. Results show that itoutperforms baseline learning models in terms of accuracy and robustness.Moreover, our approach achieves comparable performance to simulators but withsignificantly higher computational efficiency.

Learnable Digital Twin for Efficient Wireless Network Evaluation