# Learnable Digital Twin for Efficient Wireless Network Evaluation

arXiv: https://arxiv.org/abs/2306.06574 · HTML: https://mlsanity.com/paper/2306.06574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 11 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN](https://mlsanity.com/paper/1910.01508/) (2019)
- [Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities](https://mlsanity.com/paper/2202.02559/) (2022)
- [RouteNet-Fermi: Network Modeling with Graph Neural Networks](https://mlsanity.com/paper/2212.12070/) (2022)
- [RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation](https://mlsanity.com/paper/2202.13956/) (2022)

## Built on by (most cited first, 3 of 3)

- [Biased Backpressure Routing Using Link Features and Graph Neural Networks](https://mlsanity.com/paper/2407.09753/) (2024)
- [A Differentiable Digital Twin of Distributed Link Scheduling for Contention-Aware Networking](https://mlsanity.com/paper/2512.10874/) (2025)
- [Toward Composable Network Digital Twins: A Subgraph-Based Latency Prediction Study](https://mlsanity.com/paper/2609.18704/) (2026)

## Abstract (arXiv)

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.