A QoT Estimation Method using EGN-assisted Machine Learning for Network Planning Applications

Jasper Müller; Sai Kireet Patri; Tobias Fehenberger; Carmen Mas-Machuca; Helmut Griesser; Jörg-Peter Elbers

doi:10.1109/ECOC52684.2021.9606064

A QoT Estimation Method using EGN-assisted Machine Learning for Network Planning Applications

Networking and Internet Architecture 2021-12-09 v1

Authors: Jasper Müller , Sai Kireet Patri , Tobias Fehenberger , Carmen Mas-Machuca , Helmut Griesser , Jörg-Peter Elbers

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Abstract

An ML model based on precomputed per-channel SCI is proposed. Due to its superior accuracy over closed-form GN, an average SNR gain of 1.1 dB in an end-to-end link optimization and a 40% reduction in required lightpaths to meet traffic requests in a network planning scenario are shown.

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@article{arxiv.2112.04039,
  title  = {A QoT Estimation Method using EGN-assisted Machine Learning for Network Planning Applications},
  author = {Jasper Müller and Sai Kireet Patri and Tobias Fehenberger and Carmen Mas-Machuca and Helmut Griesser and Jörg-Peter Elbers},
  journal= {arXiv preprint arXiv:2112.04039},
  year   = {2021}
}

Comments

This work has been performed in the framework of the CELTIC-NEXT project AI-NET-PROTECT (Project ID C2019/3-4), and it is partly funded by the German Federal Ministry of Education and Research (FKZ16KIS1279K)

A QoT Estimation Method using EGN-assisted Machine Learning for Network Planning Applications

Abstract

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