# How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks

arXiv: https://arxiv.org/abs/2507.07067 · HTML: https://mlsanity.com/paper/2507.07067/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 9 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [Prediction-Powered Inference](https://mlsanity.com/paper/2301.09633/) (2023)
- [Cross-Prediction-Powered Inference](https://mlsanity.com/paper/2309.16598/) (2023)
- [Learning Radio Environments by Differentiable Ray Tracing](https://mlsanity.com/paper/2311.18558/) (2023)
- [Transfer Learning for Future Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2102.07572/) (2021)
- [A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems](https://mlsanity.com/paper/2212.01351/) (2022)
- [Doubly Robust Self-Training](https://mlsanity.com/paper/2306.00265/) (2023)
- [Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimates](https://mlsanity.com/paper/2312.12625/) (2023)
- [Edge Continual Learning for Dynamic Digital Twins over Wireless Networks](https://mlsanity.com/paper/2204.04795/) (2022)
- [Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems](https://mlsanity.com/paper/2210.02515/) (2022)
- [Semi-Supervised Learning via Cross-Prediction-Powered Inference for Wireless Systems](https://mlsanity.com/paper/2405.15415/) (2024)
- [Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications](https://mlsanity.com/paper/2207.00300/) (2022)
- [Colosseum: The Open RAN Digital Twin](https://mlsanity.com/paper/2404.17317/) (2024)
- [Toward Digital Network Twins: Integrating Sionna RT in ns-3 for 6G Multi-RAT Networks Simulations](https://mlsanity.com/paper/2501.00372/) (2024)
- [Context-Aware Doubly-Robust Semi-Supervised Learning](https://mlsanity.com/paper/2502.15577/) (2025)

## Built on by (most cited first, 6 of 6)

- [Great X: A Unified Multi-Modal Simulator Bridging the Sim2Real Gap for 6G](https://mlsanity.com/paper/2507.08716/) (2025)
- [Uncertainty Quantification and Data Efficiency in AI: An Information-Theoretic Perspective](https://mlsanity.com/paper/2512.05267/) (2025)
- [SEAL: An Open, Auditable, and Fair Data Generation Framework for AI-Native 6G Networks](https://mlsanity.com/paper/2604.02128/) (2026)
- [Should I Use This Synthetic Dataset for Training? How to Test with Minimal Real Data](https://mlsanity.com/paper/2608.27996/) (2026)
- [Conformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control](https://mlsanity.com/paper/2608.28179/) (2026)
- [KODAMA: Multimodal Digital Twin Reconstruction for Urban RF Propagation Modelling](https://mlsanity.com/paper/2609.07298/) (2026)

## Abstract (arXiv)

Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data. Real data collection is expensive, and available datasets often fail to capture the unique operational conditions and contextual variability of the network environment. Digital twinning provides a potential solution to this problem, as simulators tailored to the current network deployment can generate site-specific data to augment the available training datasets. However, there is a need to develop solutions to bridge the inherent simulation-to-reality (sim-to-real) gap between synthetic and real-world data. This paper reviews recent advances on two complementary strategies: 1) the calibration of digital twins (DTs) through real-world measurements, and 2) the use of sim-to-real gap-aware training strategies to robustly handle residual discrepancies between digital twin-generated and real data. For the latter, we evaluate two conceptually distinct methods that model the sim-to-real gap either at the level of the environment via Bayesian learning or at the level of the training loss via prediction-powered inference.