Machine learning techniques for quality of transmission estimation in optical networks

The estimation of the quality of transmission (QoT) in optical systems with machine learning (ML) has recently been the focus of a large body of research. We discuss the sources of inaccuracy in QoT estimation in general; we propose a taxonomy for ML-aided QoT estimation; we briefly review ML-aided...

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Bibliographic Details
Published inJournal of optical communications and networking Vol. 13; no. 4; pp. B60 - B71
Main Author Pointurier, Yvan
Format Journal Article
LanguageEnglish
Published Piscataway Optica Publishing Group 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Machine learning
Optical communication
Taxonomy
Online AccessGet full text
ISSN1943-0620
1943-0639
DOI10.1364/JOCN.417434

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Summary:The estimation of the quality of transmission (QoT) in optical systems with machine learning (ML) has recently been the focus of a large body of research. We discuss the sources of inaccuracy in QoT estimation in general; we propose a taxonomy for ML-aided QoT estimation; we briefly review ML-aided optical performance monitoring, a tightly related topic; and we review and compare all recently published ML-aided QoT articles.
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ISSN:1943-0620
1943-0639
DOI:10.1364/JOCN.417434