Transfer Learning from Unbiased Training Data Sets for QoT Estimation in WDM Networks

摘要

We assess the benefits of transfer learning based on artificial neural networks (ANN) using unbiased training data sets for Qo T-estimation of unestablished light paths. This study considers transfer learning from the CONUS topology and from an unbiased training data set to the Germany50 topology.