# Digital Twin for Networking: A Data-driven Performance Modeling Perspective

arXiv: https://arxiv.org/abs/2206.00310 · HTML: https://mlsanity.com/paper/2206.00310/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2022.

## Built on by (most cited first, 10 of 10)

- [A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems](https://mlsanity.com/paper/2212.01351/) (2022)
- [Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimates](https://mlsanity.com/paper/2312.12625/) (2023)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [Digital Twin in Industries: A Comprehensive Survey](https://mlsanity.com/paper/2412.00209/) (2024)
- [Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks](https://mlsanity.com/paper/2502.05116/) (2025)
- [Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning](https://mlsanity.com/paper/2210.05582/) (2022)
- [Future-Proofing Mobile Networks: A Digital Twin Approach to Multi-Signal Management](https://mlsanity.com/paper/2407.15520/) (2024)
- [M3Net: A Multi-Metric Mixture of Experts Network Digital Twin with Graph Neural Networks](https://mlsanity.com/paper/2512.09797/) (2025)
- [Optimizing Reinforcement Learning Training over Digital Twin Enabled Multi-fidelity Networks](https://mlsanity.com/paper/2603.08931/) (2026)
- [Toward Composable Network Digital Twins: A Subgraph-Based Latency Prediction Study](https://mlsanity.com/paper/2609.18704/) (2026)

## Abstract (arXiv)

Emerging technologies and applications make the network unprecedentedly complex and heterogeneous, leading physical network practices to be costly and risky. The digital twin network (DTN) can ease these burdens by virtually enabling users to understand how performance changes accordingly with modifications. For this "What-if" performance evaluation, conventional simulation and analytical approaches are inefficient, inaccurate, and inflexible, and we argue that data-driven methods are most promising. In this article, we identify three requirements (fidelity, efficiency, and flexibility) for performance evaluation. Then we present a comparison of selected data-driven methods and investigate their potential trends in data, models, and applications. Although extensive applications have been enabled, there are still significant conflicts between models' capacities to handle diversified inputs and limited data collected from the production network. We further illustrate the opportunities for data collection, model construction, and application prospects. This survey aims to provide a reference for performance evaluation while also facilitating future DTN research.