Constellation-based identification of linear and nonlinear OSNR using machine learning: a study of link-agnostic performance-Reference-Cited by-同舟云学术

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Constellation-based identification of linear and nonlinear OSNR using machine learning: a study of link-agnostic performance

Published:2022-01-12 Issue:2 Volume:30 Page:2693
ISSN:1094-4087
Container-title:Optics Express
language:en
Short-container-title:Opt. Express

Author:

Cho Hyung Joon,Lippiatt Daniel,Thomas Varghese A.,Varughese Siddharth,Searcy Steven,Richter Thomas,Tibuleac Sorin,Ralph Stephen E.

Funder

Georgia Electronic Design Center

L3Harris Technologies, Inc.

ADVA Optical Networking, Inc.

Publisher

The Optical Society

Subject

Atomic and Molecular Physics, and Optics

Reference23 articles.

1. Capacity Limits of Optical Fiber Networks

2. Design, Acceptance and Capacity of Subsea Open Cables

3. Optical performance monitoring using digital coherent receivers and convolutional neural networks

4. Nonlinear Interference Mitigation: Methods and Potential Gain

5. Inter-Channel Crosstalk Cancellation for Nyquist-WDM Superchannel Applications

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1. Linear and Nonlinear SNR Estimation using Spectral and Temporal Correlations;2024 International Conference on Optical Network Design and Modeling (ONDM);2024-05-06

2. Experimental Validation of Machine Learning-Based Joint Failure Management and Quality of Transmission Estimation;IEEE Photonics Journal;2023-12

3. Experimental Investigation of Spectral Data Enhanced QoT Estimation;Journal of Lightwave Technology;2023-09-15

4. Joint nonlinear optical signal-to-noise ratio estimation and modulation format identification based on constellation-points trajectory information and multitask 1DCNN for WDM systems;Applied Optics;2022-12-14

5. Nonlinear SNR estimation based on the data augmentation-assisted DNN with a small-scale dataset;Optics Express;2022-10-11

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