Generalization Properties of Machine Learning-based Raman Models

被引：0
|
作者：
de Moura, U. C. [1 ]
Zibar, D. [1 ]
Brusin, A. M. Rosa [2 ]
Carena, A. [2 ]
Da Ros, F. [1 ]
机构：
[1] Tech Univ Denmark, DTU Foton, DK-2800 Lyngby, Denmark
[2] Politecn Torino, DET, Corso Duca Abruzzi 24, I-10129 Turin, Italy
来源：
2021 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXPOSITION (OFC) | 2021年
基金：
欧盟地平线“2020”; 欧洲研究理事会;
关键词：
NETWORKS;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We investigate the generalization capabilities of neural network-based Raman amplifier models. The new proposed model architecture, including fiber parameters as inputs, can predict Raman gains of fiber types unseen during training, unlike previous fiber-specific models. (C) 2021 The Author(s)
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页数：3
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