Fast and accurate nonlinear interference in-band spectrum prediction for sparse channel allocation

被引：0

作者：

Andrenacci, Isaia ^{[1
]}

Lonardi, Matteo ^{[1
]}

Ramantanis, Petros ^{[1
]}

Awwad, Elie ^{[2
]}

Irurozki, Ekhine ^{[2
]}

Clemencon, Stephan ^{[2
]}

机构：

[1] Telecom Paris, Phys Opt Networks, LTCI, Nokia Bell Labs, Nozay, France

[2] Telecom Paris, LTCI, Palaiseau, France

来源：

2023 INTERNATIONAL CONFERENCE ON OPTICAL NETWORK DESIGN AND MODELING, ONDM | 2023年

关键词：

optical network; machine learning; fiber nonlinearity; nonlinear interference power spectral densityd;

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 propose and numerically evaluate a machine-learning-based nonlinear interference spectrum estimator for a coherent optical network. The solution shows a root-mean-squared error of about 0.13 dB compared with split-step Fourier simulation when estimating the nonlinear interference variance.

引用

页数：5

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