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

TL;DR

This paper reviews recent methods to bridge the simulation-to-reality gap in digital twin-based telecommunications, focusing on calibration and gap-aware training strategies to improve AI model robustness.

Contribution

It introduces and evaluates two novel approaches for handling the sim-to-real gap: Bayesian environment modeling and prediction-powered inference.

Findings

Calibration of digital twins improves data accuracy for training.

Gap-aware training strategies enhance model robustness to discrepancies.

Bayesian and inference-based methods effectively model and mitigate the sim-to-real gap.

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.