保证
光通信
计算机科学
电信网络
通信系统
电信
多波长光网络
光学(聚焦)
透视图(图形)
光网络
信号处理
物理层
传输(电信)
电子工程
人工智能
光纤
工程类
波分复用
无线
光纤分路器
经济
物理
光学
金融经济学
光电子学
雷达
光纤传感器
波长
作者
Faisal Nadeem Khan,Qirui Fan,Chao Lu,Alan Pak Tao Lau
标识
DOI:10.1109/jlt.2019.2897313
摘要
Machine learning (ML) has disrupted a wide range of science and engineering disciplines in recent years. ML applications in optical communications and networking are also gaining more attention, particularly in the areas of nonlinear transmission systems, optical performance monitoring, and cross-layer network optimizations for software-defined networks. However, the extent to which ML techniques can benefit optical communications and networking is not clear and this is partly due to an insufficient understanding of the nature of ML concepts. This paper aims to describe the mathematical foundations of basic ML techniques from communication theory and signal processing perspectives, which in turn will shed light on the types of problems in optical communications and networking that naturally warrant ML use. This will be followed by an overview of ongoing ML research in optical communications and networking with a focus on physical layer issues.
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