BBAuthors: Sartzetakis, I.; Advisor: -; Participants: Christodoulopoulos, K. K.; Varvarigos, E. M. (2019)
In optical transport networks the quality of transmission (QoT) is estimated before provisioning new
connections or upgrading existing ones. Traditionally, a physical layer model (PLM) is used for QoT estimation
coupled with high margins to account for the model inaccuracy and the uncertainty in the evolving physical layer
conditions. Reducing the margins increases network efficiency but requires accurate QoT estimation. We present
two machine learning (ML) approaches to formulate such an accurate QoT estimator. We gather physical layer feedback, by monitoring the QoT of existing connections, to understand the actual physical conditions of the network. These data are used to train either the input parameters of a PLM or a machine learning model (ML-M). The proposed ML methods ac... 
