用于未见光网络状态传输质量估计的机器学习方法
网络与互联网体系结构
2020-08-04 v1 信号处理
摘要
我们应用深度图卷积神经网络对未见网络状态进行传输质量（Quality-of-Transmission）估计，除其他重要损伤外，该估计还捕获了在采用多芯光纤的光网络中显著的核心间串扰。
引用
@article{arxiv.1812.07254,
title = {Machine Learning for QoT Estimation of Unseen Optical Network States},
author = {Tania Panayiotou and Giannis Savva and Behnam Shariati and Ioannis Tomkos and Georgios Ellinas},
journal= {arXiv preprint arXiv:1812.07254},
year = {2020}
}
备注
accepted for publication in the Optical Networking and Communication Conference & Exhibition (OFC), 2019