A Machine Learning Approach for Dynamic Optical Channel Add/Drop Strategies that Minimize EDFA Power Excursions

摘要

We demonstrate a machine learning approach to characterize channel dependence of power excursions in mulit-span EDFA networks. This technique can determine accurate recommendations for channel add/drop with minimal excursions and is applicate to different network design.