Optical networks are evolving toward ultrawide bandwidth and autonomous operation. In this scenario, it is crucial to accurately model and control optical power evolutions (OPEs) through optical amplifiers (OAs), as they directly affect the signal-to-noise ratio and fiber nonlinearities. However, a fundamental contradiction arises between the complex physical phenomena in optical transmission and the required precision in network control. Traditional theoretical methods underperform due to ideal assumptions, while data-driven approaches entail exorbitant costs associated with acquiring massive amounts of data to achieve the desired level of accuracy. In this work, we propose a Bayesian inference framework (BIF) to construct the digital twin of OAs and control OPE in a data-efficient manner. Only the informative data are collected to balance the exploration and exploitation of the data space, thus enabling efficient autonomous-driving optical networks (ADONs). Simulations and experiments demonstrate that the BIF can reduce the data size for modeling erbium-doped fiber amplifiers by 80% and Raman amplifiers by 60%. Within 30 iterations, the optimal controlling performance can be achieved to realize target signal/gain profiles in links with different types of OAs. The results show that the BIF paves the way to accurately model and control OPE for future ADONs.

为了应对在传输容量、光谱利用率、能量效率、体积和系统复杂度方面日益增长的需求，波分复用(WDM)光纤通信系统需要比目前使用的传统激光模块更先进的激光源。由集成片上微腔产生的克尔光频梳是下一代波分复用激光源中的一个十分具有前途的方案，其优势包括宽光谱、大量的梳齿、与波分复用信道相匹配的频率间隔、高度稳定的频率、低的相位噪声、与芯片集成的兼容性以及能够低成本地批量生产。回顾了克尔光频梳的基本原理，并介绍了各种克尔光频梳器件的制造方法。此外，还讨论了克尔光频梳的特点，如高光谱纯度和能够兼容芯片集成以及其能在长距离相干传输和数据中心互连中推动波分复用光通信发展。