Machine learning techniques for quality of transmission estimation in optical networks

被引:62
|
作者
Pointurier, Yvan [1 ]
机构
[1] Huawei Technol France, 20 Quai Point Jour, F-92100 Boulogne Billancourt, France
关键词
DIGITAL COHERENT RECEIVERS; DEEP NEURAL-NETWORK; QOT ESTIMATION; NONLINEAR NOISE; JOINT OSNR; PREDICTION; LONG; PARAMETERS; MODEL;
D O I
10.1364/JOCN.417434
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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. (C) 2021 Optical Society of America
引用
收藏
页码:B60 / B71
页数:12
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