Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

The Perks of Using Machine Learning for QoT Estimation with Uncertain Network Parameters

Not Accessible

Your library or personal account may give you access


We compare the resilience to network parameters uncertainty between a GN-model based QoT tool and ML-based ones. We study the mean square error of the estimated G-OSNR and a novel metric based on the overestimation probability.

© 2020 The Author(s)

PDF Article
More Like This
How Uncertainty on the Fiber Span Lengths Influences QoT Estimation Using Machine Learning in WDM Networks

J. Pesic, M. Lonardi, N. Rossi, T. Zami, E. Seve, and Y. Pointurier
Th3D.5 Optical Fiber Communication Conference (OFC) 2020

Modeling Filtering Penalties in ROADM-based Networks with Machine Learning for QoT Estimation

Ankush Mahajan, Kostas Christodoulopoulos, Ricardo Martinez, Salvatore Spadaro, and Raul Munoz
Th3D.4 Optical Fiber Communication Conference (OFC) 2020

Experimental Comparisons between Machine Learning and Analytical Models for QoT Estimations in WDM Systems

Qirui Fan, Jianing Lu, Gai Zhou, Derek Zeng, Changjian Guo, Linyue Lu, Jianqiang Li, Chongjin Xie, Chao Lu, Faisal Nadeem Khan, and Alan Pak Tao Lau
M2J.2 Optical Fiber Communication Conference (OFC) 2020


You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
Login to access Optica Member Subscription

Select as filters

Select Topics Cancel
© Copyright 2022 | Optica Publishing Group. All Rights Reserved