English

Estimating Quality of Transmission in a Live Production Network using Machine Learning

Networking and Internet Architecture 2021-12-09 v1
Authors: Jasper Müller , Tobias Fehenberger , Sai Kireet Patri , Kaida Kaeval , Helmut Griesser , Marko Tikas , Jörg-Peter Elbers
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Abstract

We demonstrate QoT estimation in a live network utilizing neural networks trained on synthetic data spanning a large parameter space. The ML-model predicts the measured lightpath performance with <0.5dB SNR error over a wide configuration range.

Keywords

quality of transmission optical network machine learning neural network synthetic data signal-to-noise ratio

Cite

@article{arxiv.2112.04031,
  title  = {Estimating Quality of Transmission in a Live Production Network using Machine Learning},
  author = {Jasper Müller and Tobias Fehenberger and Sai Kireet Patri and Kaida Kaeval and Helmut Griesser and Marko Tikas and Jörg-Peter Elbers},
  journal= {arXiv preprint arXiv:2112.04031},
  year   = {2021}
}

Comments

The work has been partially funded by the German Ministry of Education and Research in the project OptiCON (contract #16KIS0989K)

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