$Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications$
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Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications

Abstract Structured light with spatial degrees of freedom (DoF) is considered a potential solution to address the unprecedented demand for data traffic, but there is a limit to effectively improving the communication capacity by its integer quantization. We propose a data transmission system using f...

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Autores principales:	Youngbin Na, Do-Kyeong Ko
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/31b497c7dac14df7969ac14e011bc194
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id	oai:doaj.org-article:31b497c7dac14df7969ac14e011bc194
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spelling	oai:doaj.org-article:31b497c7dac14df7969ac14e011bc1942021-12-02T10:48:02ZDeep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications10.1038/s41598-021-82239-82045-2322https://doaj.org/article/31b497c7dac14df7969ac14e011bc1942021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82239-8https://doaj.org/toc/2045-2322Abstract Structured light with spatial degrees of freedom (DoF) is considered a potential solution to address the unprecedented demand for data traffic, but there is a limit to effectively improving the communication capacity by its integer quantization. We propose a data transmission system using fractional mode encoding and deep-learning decoding. Spatial modes of Bessel-Gaussian beams separated by fractional intervals are employed to represent 8-bit symbols. Data encoded by switching phase holograms is efficiently decoded by a deep-learning classifier that only requires the intensity profile of transmitted modes. Our results show that the trained model can simultaneously recognize two independent DoF without any mode sorter and precisely detect small differences between fractional modes. Moreover, the proposed scheme successfully achieves image transmission despite its densely packed mode space. This research will present a new approach to realizing higher data rates for advanced optical communication systems.Youngbin NaDo-Kyeong KoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Youngbin Na Do-Kyeong Ko Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications
description	Abstract Structured light with spatial degrees of freedom (DoF) is considered a potential solution to address the unprecedented demand for data traffic, but there is a limit to effectively improving the communication capacity by its integer quantization. We propose a data transmission system using fractional mode encoding and deep-learning decoding. Spatial modes of Bessel-Gaussian beams separated by fractional intervals are employed to represent 8-bit symbols. Data encoded by switching phase holograms is efficiently decoded by a deep-learning classifier that only requires the intensity profile of transmitted modes. Our results show that the trained model can simultaneously recognize two independent DoF without any mode sorter and precisely detect small differences between fractional modes. Moreover, the proposed scheme successfully achieves image transmission despite its densely packed mode space. This research will present a new approach to realizing higher data rates for advanced optical communication systems.
format	article
author	Youngbin Na Do-Kyeong Ko
author_facet	Youngbin Na Do-Kyeong Ko
author_sort	Youngbin Na
title	Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications
title_short	Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications
title_full	Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications
title_fullStr	Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications
title_full_unstemmed	Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications
title_sort	deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/31b497c7dac14df7969ac14e011bc194
work_keys_str_mv	AT youngbinna deeplearningbasedhighresolutionrecognitionoffractionalspatialmodeencodeddataforfreespaceopticalcommunications AT dokyeongko deeplearningbasedhighresolutionrecognitionoffractionalspatialmodeencodeddataforfreespaceopticalcommunications
_version_	1718396700913041408

Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications

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