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：

欧洲研究理事会; 欧盟地平线“2020”;

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)

引用

页数：3

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