Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning

Nonlinear effects provide inherent limitations in fiber optical communications. Here, the authors experimentally demonstrate improved digital back propagation with machine learning and use the results to reveal insights in the optimization of digital signal processing.

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Bibliographic Details
Main Authors:	Qirui Fan, Gai Zhou, Tao Gui, Chao Lu, Alan Pak Tao Lau
Format:	article
Language:	EN
Published:	Nature Portfolio 2020
Subjects:	Science Q
Online Access:	https://doaj.org/article/50f8d967bc4946a0b867e0de9bb7a6ad
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id	oai:doaj.org-article:50f8d967bc4946a0b867e0de9bb7a6ad
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spelling	oai:doaj.org-article:50f8d967bc4946a0b867e0de9bb7a6ad2021-12-02T16:26:32ZAdvancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning10.1038/s41467-020-17516-72041-1723https://doaj.org/article/50f8d967bc4946a0b867e0de9bb7a6ad2020-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17516-7https://doaj.org/toc/2041-1723Nonlinear effects provide inherent limitations in fiber optical communications. Here, the authors experimentally demonstrate improved digital back propagation with machine learning and use the results to reveal insights in the optimization of digital signal processing.Qirui FanGai ZhouTao GuiChao LuAlan Pak Tao LauNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Qirui Fan Gai Zhou Tao Gui Chao Lu Alan Pak Tao Lau Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning
description	Nonlinear effects provide inherent limitations in fiber optical communications. Here, the authors experimentally demonstrate improved digital back propagation with machine learning and use the results to reveal insights in the optimization of digital signal processing.
format	article
author	Qirui Fan Gai Zhou Tao Gui Chao Lu Alan Pak Tao Lau
author_facet	Qirui Fan Gai Zhou Tao Gui Chao Lu Alan Pak Tao Lau
author_sort	Qirui Fan
title	Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning
title_short	Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning
title_full	Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning
title_fullStr	Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning
title_full_unstemmed	Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning
title_sort	advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/50f8d967bc4946a0b867e0de9bb7a6ad
work_keys_str_mv	AT qiruifan advancingtheoreticalunderstandingandpracticalperformanceofsignalprocessingfornonlinearopticalcommunicationsthroughmachinelearning AT gaizhou advancingtheoreticalunderstandingandpracticalperformanceofsignalprocessingfornonlinearopticalcommunicationsthroughmachinelearning AT taogui advancingtheoreticalunderstandingandpracticalperformanceofsignalprocessingfornonlinearopticalcommunicationsthroughmachinelearning AT chaolu advancingtheoreticalunderstandingandpracticalperformanceofsignalprocessingfornonlinearopticalcommunicationsthroughmachinelearning AT alanpaktaolau advancingtheoreticalunderstandingandpracticalperformanceofsignalprocessingfornonlinearopticalcommunicationsthroughmachinelearning
_version_	1718383990434430976

Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning

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