Machine Learning for QoT Estimation of Unseen Optical Network States

Tania Panayiotou, Giannis Savva, Behnam Shariati, Ioannis Tomkos, Georgios Ellinas

Open source

DOI: 10.1364/ofc.2019.tu2e.2
Published: 2019
Container: Optical Fiber Communication Conference (OFC) 2019
Publisher: OSA
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1364/ofc.2019.tu2e.2,
  title = {Machine Learning for QoT Estimation of Unseen Optical Network States},
  author = {Tania Panayiotou and Giannis Savva and Behnam Shariati and Ioannis Tomkos and Georgios Ellinas},
  year = {2019},
  journal = {Optical Fiber Communication Conference (OFC) 2019},
  doi = {10.1364/ofc.2019.tu2e.2},
  url = {https://doi.org/10.1364/ofc.2019.tu2e.2}
}

RIS

TY  - JOUR
TI  - Machine Learning for QoT Estimation of Unseen Optical Network States
AU  - Tania Panayiotou
AU  - Giannis Savva
AU  - Behnam Shariati
AU  - Ioannis Tomkos
AU  - Georgios Ellinas
PY  - 2019
JO  - Optical Fiber Communication Conference (OFC) 2019
DO  - 10.1364/ofc.2019.tu2e.2
UR  - https://doi.org/10.1364/ofc.2019.tu2e.2
ER  -

APA

Panayiotou, T., Savva, G., Shariati, B., Tomkos, I., & Ellinas, G. (2019). Machine Learning for QoT Estimation of Unseen Optical Network States. Optical Fiber Communication Conference (OFC) 2019. https://doi.org/10.1364/ofc.2019.tu2e.2

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crossref · retrieved 2026-09-25T22:49:04.449Z