Machine learning enhancement of a digital twin for wavelength division multiplexing network performance prediction leveraging quality of transmission parameter refinement

Digital twins capable of quality of transmission (QoT) estimation and prediction are powerful tools for efficient design and operation of optical networks. In this paper, we employ machine learning techniques to enhance both the QoT estimation and prediction capabilities of an optical network digital twin. By leveraging a method to infer or refine the unknown lumped loss distributions and amplifier gain spectra for accurate characterization of the current optical network state, the accuracy of estimation and prediction is substantially improved. For QoT prediction, we further develop a novel neural-network architecture for erbium-doped fiber amplifiers that, after factory training on a single device and with fully loaded configurations only, generalizes to partially loaded configurations seen after deployment and to different gain and tilt settings and other physical devices of the same type. We combine refined parameters and a novel method that predicts the difference in per-channel power when individual services are added or removed or multiple services are lost due to a fiber cut. The impact of error amplification due to cascading of individual components’ models is shown to be reduced, yielding predictions that are substantially more accurate than simply cascaded predictions. As the network ages, the digital twin can be updated by retraining while using only information available in the current network state.