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

• URL: http://arxiv.org/abs/2507.07067v1
• Date: Wed, 09 Jul 2025 17:27:51 GMT
• Title: How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks
• Authors: Clement Ruah, Houssem Sifaou, Osvaldo Simeone, Bashir M. Al-Hashimi,
• Abstract summary: Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data.<n>Real data collection is expensive, and available datasets often fail to capture the unique operational conditions and contextual variability of the network environment.<n>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.
• Score: 30.858857240474077
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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