Forecasting SNR margins has low-complexity

Firouzeh Golaghazadeh; Firouzeh Golaghazadeh; Petar Djukic; Christine Tremblay; Christian Desrosiers

doi:10.1364/NETWORKS.2020.NeW1B.4

OSA Advanced Photonics Congress (AP) 2020 (IPR, NP, NOMA, Networks, PVLED, PSC, SPPCom, SOF)
OSA Technical Digest (Optica Publishing Group, 2020),
paper NeW1B.4
•https://doi.org/10.1364/NETWORKS.2020.NeW1B.4

Forecasting SNR margins has low-complexity

Firouzeh Golaghazadeh, Petar Djukic, Christine Tremblay, and Christian Desrosiers

Photonic Networks and Devices 2020

Washington, DC United States
13–16 July 2020
ISBN: 978-1-943580-79-8

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Machine Learning for Optical Networks II (NeW1B)

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F. Golaghazadeh, P. Djukic, C. Tremblay, and C. Desrosiers, "Forecasting SNR margins has low-complexity," in OSA Advanced Photonics Congress (AP) 2020 (IPR, NP, NOMA, Networks, PVLED, PSC, SPPCom, SOF), L. Caspani, A. Tauke-Pedretti, F. Leo, and B. Yang, eds., OSA Technical Digest (Optica Publishing Group, 2020), paper NeW1B.4.

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Abstract

We use an extensive dataset from a production network to forecast signal-to-noise ratio (SNR) margin and show that a na¨ıve technique is able to forecast SNR margin better than a deep neural network (DNN).

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