一种面向网络规划应用的 EGN 辅助机器学习 QoT 估计方法
网络与互联网体系结构
2021-12-09 v1
摘要
本文提出了一种基于预计算每通道自信道干扰（SCI）的机器学习（ML）模型。由于该模型相比闭式高斯噪声（GN）模型具有更高的精度，在端到端链路优化中显示出平均 1.1 dB 的信噪比（SNR）增益，并在网络规划场景中，满足流量请求所需的光路数量减少了 40%。
引用
@article{arxiv.2112.04039,
title = {A QoT Estimation Method using EGN-assisted Machine Learning for Network Planning Applications},
author = {Jasper Müller and Sai Kireet Patri and Tobias Fehenberger and Carmen Mas-Machuca and Helmut Griesser and Jörg-Peter Elbers},
journal= {arXiv preprint arXiv:2112.04039},
year = {2021}
}
备注
This work has been performed in the framework of the CELTIC-NEXT project AI-NET-PROTECT (Project ID C2019/3-4), and it is partly funded by the German Federal Ministry of Education and Research (FKZ16KIS1279K)