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- W4380552007 abstract "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." @default.
- W4380552007 created "2023-06-14" @default.
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- W4380552007 date "2023-06-10" @default.
- W4380552007 modified "2023-10-18" @default.
- W4380552007 title "Learnable Digital Twin for Efficient Wireless Network Evaluation" @default.
- W4380552007 doi "https://doi.org/10.48550/arxiv.2306.06574" @default.
- W4380552007 hasPublicationYear "2023" @default.
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