.png){kind=icon}
返回
Inverse System Design Using Machine Learning: The Raman Amplifier Case
.png){kind=icon}
OA
AI
DOI：10.1109/JLT.2019.2952179.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A wide range of highly-relevant problems in programmable and integrated photonics, optical amplification, and communication deal with inverse system design. Typically, a desired output (usually a gain profile, a noise profile, a transfer function or a similar continuous function) is given and the goal is to determine the corresponding set of input parameters (usually a set of input voltages, currents, powers, and wavelengths). We present a novel method for inverse system design using machine learning and apply it to Raman amplifier design. Inverse system design for Raman amplifiers consists of selecting pump powers and wavelengths that would result in a targeted gain profile. This is a challenging task due to highly-complex interaction between pumps and Raman gain. Using the proposed framework, highly-accurate predictions of the pumping setup for arbitrary Raman gain profiles are demonstrated numerically in C and C+L-band, as well as experimentally in C band, for the first time. A low mean (0.46 and 0.35 dB) and standard deviation (0.20 and 0.17 dB) of the maximum error are obtained for numerical (C+L-band) and experimental (C-band) results, respectively, when employing 4 pumps and 100 km span length. The presented framework is general and can be applied to other inverse problems in optical communication and photonics in general.
Keyword:
Gain
Optimization
Neural networks
System analysis and design
Machine learning
Pumps
Optical pumping
Inverse system design
machine learning
optical amplification
optical communication
optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.8
论文数:
1.7W
被引数:
3.8W
机构
引用论文
.png){kind=icon}
Machine Learning for Network Automation: Overview, Architecture, and Applications [Invited Tutorial]网络自动化的机器学习: 概述、架构和应用 [特邀教程]OAAI
OAAI分享收藏分享收藏
查看更多内容