On the Robustness of a ML-based Method for QoT Tool Parameter Refinement in Partially Loaded Networks

Nathalie Morette; Ivan Fernandez de Jauregui Ruiz; Hartmut Hafermann; Yvan Pointurier

doi:10.1364/OFC.2022.M3F.1

Optical Fiber Communication Conference (OFC) 2022
Technical Digest Series (Optica Publishing Group, 2022),
paper M3F.1
•https://doi.org/10.1364/OFC.2022.M3F.1

On the Robustness of a ML-based Method for QoT Tool Parameter Refinement in Partially Loaded Networks

Nathalie Morette, Ivan Fernandez de Jauregui Ruiz, Hartmut Hafermann, and Yvan Pointurier

Optical Fiber Communication Conference 2022

San Diego, California United States
6–10 March 2022
ISBN: 978-1-55752-466-9

From the session
Machine Learning for Network Operation (M3F)

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N. Morette, I. F. de Jauregui Ruiz, H. Hafermann, and Y. Pointurier, "On the Robustness of a ML-based Method for QoT Tool Parameter Refinement in Partially Loaded Networks," in Optical Fiber Communication Conference (OFC) 2022, S. Matsuo, D. Plant, J. Shan Wey, C. Fludger, R. Ryf, and D. Simeonidou, eds., Technical Digest Series (Optica Publishing Group, 2022), paper M3F.1.

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Abstract

The robustness of a ML-based QoT input parameters refinement technique in partially loaded networks (both static and dynamic) is assessed using experimental data. SNR prediction error is reduced by up to 1dB over >40000 services.

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