Machine learning techniques for quality of transmission estimation in optical networks

被引：71

作者：

Pointurier, Yvan ^{[1
]}

机构：

[1] Huawei Technol France, 20 Quai Point Jour, F-92100 Boulogne Billancourt, France

来源：

JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING | 2021年 / 13卷 / 04期

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

页码：B60 / B71

页数：12

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