QoT Estimation with Margin-Driven Transfer Learning in Time-Varying Optical Networks

Estimating transmission quality in an optical network is critical for resource efficiency but challenging due to the infrastructure time-varying state. We propose a transfer learning solution to adapt a data-driven model to network changes.