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

2025-07-09

AI Summary

To address the weak generalization of AI models in telecom networks caused by scarce real-world data, this paper proposes a dual-strategy framework to bridge the sim-to-real gap between digital twin simulations and physical deployments. Methodologically: (1) Bayesian learning is introduced at the environment level to enable dynamic calibration of the digital twin; (2) a prediction-augmented inference mechanism is designed at the loss level to explicitly model and suppress simulation biases. Together, these components establish a robust training paradigm that actively perceives and mitigates simulation–reality discrepancies. Experiments demonstrate that the framework significantly improves both deployment performance and cross-scenario generalization of AI models trained solely on synthetic data. It thus provides a transferable, high-fidelity modeling and training pathway for digital-twin-driven communication intelligence.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Bridging sim-to-real gap in telecom AI training
• Calibrating digital twins with real-world measurements
• Handling discrepancies via sim-to-real-aware training

Innovation

• Calibrate digital twins using real-world measurements
• Apply sim-to-real gap-aware training strategies
• Model gap via Bayesian learning or inference