计算机科学
先验与后验
频道(广播)
光放大器
最大后验估计
人工神经网络
传输(电信)
均方误差
人工智能
电子工程
工程类
电信
统计
数学
光学
物理
最大似然
激光器
哲学
认识论
作者
Jiakai Yu,Shengxiang Zhu,Craig Gutterman,Gil Zussman,Daniel C. Kilper
出处
期刊:Journal of Optical Communications and Networking
[The Optical Society]
日期:2021-02-12
卷期号:13 (4): B83-B83
被引量:21
摘要
Optical transmission systems with high spectral efficiency require accurate quality of transmission estimation for optical channel provisioning. However, the wavelength-dependent gain effects of erbium-doped fiber amplifiers (EDFAs) complicate precise optical channel power prediction and low-margin operation. In this work, we examine supervised machine learning methods using multiple artificial neural networks (ANNs) to build models for gain spectra prediction of optical transmission line EDFAs under different operating conditions. Channel-loading configurations and channel input power spectra are used as an a posteriori knowledge data feature for model training. In a hybrid learning approach, estimated gain spectra calculated by an analytical model are added as an a priori input data feature to further improve the EDFA ANN model performance in terms of prediction accuracy, training time, and quantity of training data. Using these methods, the root mean square error and maximum absolute error of the predicted channel output power can be as low as 0.144 dB and 1.6 dB, respectively.
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