用数字孪生生成数据，再通过实测校准和智能训练，提升通信网络AI模型的实用性能。
How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks
- 通过真实测量校准数字孪生，使仿真数据更贴近实际环境。
- 采用贝叶斯建模或预测增强损失函数，有效缓解仿真与现实的偏差。
- 适合通信系统研发人员及数字孪生应用工程师参考。
为电信网络训练高效的AI模型面临挑战，因部署特定数据稀缺。真实数据采集成本高，现有数据集难以捕捉网络运行的独特条件与上下文变化。数字孪生提供潜在解决方案：针对当前网络部署定制的模拟器可生成站点特异性数据，补充训练数据集。然而，需解决仿真与现实（sim-to-real）之间的固有差距。本文综述了两种互补策略的最新进展：1）通过真实测量校准数字孪生（DT），2）采用考虑sim-to-real差距的训练策略，以稳健处理数字孪生生成数据与真实数据间的残余差异。后者评估了两种概念上不同的方法：一种在环境层面通过贝叶斯学习建模差距，另一种在训练损失层面通过预测驱动推断建模差距。
原文摘要 · Abstract (English)
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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