Modeling filtering penalties in ROADM-based networks with machine learning for QoT estimation

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Monitoring 3dB bandwidth and other spectrum related parameters at ROADMs provides information about quality of their filters. We propose a machine-learning model to estimate end-to-end filtering penalty for more accurate QoT estimation of future connections.Authors would like to thank Karsten Schuh and Camille Delezoide of Nokia Bell Labs for technical discussionsonfilter modelling. This work is a part ofH2020-MSCA, ONFIRE project supported by EU, grant agreement No. 765275.Peer ReviewedPostprint (author's final draft