Machine Learning Techniques for Estimating the Quality of Transmission of Lightpaths

被引：0
|
作者：
Tremblay, Christine [1 ]
Aladin, Sandra [1 ]
机构：
[1] Ecole Technol Super, Elect Engn Dept, Network Technol Lab, Montreal, PQ, Canada
来源：
2018 IEEE PHOTONICS SOCIETY SUMMER TOPICAL MEETING SERIES (SUM) | 2018年
基金：
加拿大自然科学与工程研究理事会;
关键词：
Communications technology; optical fiber networks; WDM networks; machine learning; supervised learning; support vector machines; cognitive systems; AWGN; quality of transmission;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We discuss the use of machine learning (ML) techniques for estimating the quality of transmission (QoT) of lightpaths in coherent uncompensated WDM links and show the potential benefits of cognitive QoT estimation tools through a comparative performance analysis of ML-based classifiers using synthetic BER data.
引用
收藏
页码：237 / 238
页数：2
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