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Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning

Based on orbital angular momentum (OAM) properties of Laguerre–Gaussian beams LG([Formula: see text]), a robust optical encoding model for efficient data transmission applications is designed. This paper presents an optical encoding model based on an intensity profile generated by a coherent superpo...

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Autores principales: Lamilla, Erick, Sacarelo, Christian, Alvarez-Alvarado, Manuel S., Pazmino, Arturo, Iza, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007020/
https://www.ncbi.nlm.nih.gov/pubmed/36904967
http://dx.doi.org/10.3390/s23052755
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author Lamilla, Erick
Sacarelo, Christian
Alvarez-Alvarado, Manuel S.
Pazmino, Arturo
Iza, Peter
author_facet Lamilla, Erick
Sacarelo, Christian
Alvarez-Alvarado, Manuel S.
Pazmino, Arturo
Iza, Peter
author_sort Lamilla, Erick
collection PubMed
description Based on orbital angular momentum (OAM) properties of Laguerre–Gaussian beams LG([Formula: see text]), a robust optical encoding model for efficient data transmission applications is designed. This paper presents an optical encoding model based on an intensity profile generated by a coherent superposition of two OAM-carrying Laguerre–Gaussian modes and a machine learning detection method. In the encoding process, the intensity profile for data encoding is generated based on the selection of p and ℓ indices, while the decoding process is performed using a support vector machine (SVM) algorithm. Two different decoding models based on an SVM algorithm are tested to verify the robustness of the optical encoding model, finding a BER [Formula: see text] for [Formula: see text] dB of signal-to-noise ratio in one of the SVM models.
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spelling pubmed-100070202023-03-12 Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning Lamilla, Erick Sacarelo, Christian Alvarez-Alvarado, Manuel S. Pazmino, Arturo Iza, Peter Sensors (Basel) Communication Based on orbital angular momentum (OAM) properties of Laguerre–Gaussian beams LG([Formula: see text]), a robust optical encoding model for efficient data transmission applications is designed. This paper presents an optical encoding model based on an intensity profile generated by a coherent superposition of two OAM-carrying Laguerre–Gaussian modes and a machine learning detection method. In the encoding process, the intensity profile for data encoding is generated based on the selection of p and ℓ indices, while the decoding process is performed using a support vector machine (SVM) algorithm. Two different decoding models based on an SVM algorithm are tested to verify the robustness of the optical encoding model, finding a BER [Formula: see text] for [Formula: see text] dB of signal-to-noise ratio in one of the SVM models. MDPI 2023-03-02 /pmc/articles/PMC10007020/ /pubmed/36904967 http://dx.doi.org/10.3390/s23052755 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Lamilla, Erick
Sacarelo, Christian
Alvarez-Alvarado, Manuel S.
Pazmino, Arturo
Iza, Peter
Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning
title Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning
title_full Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning
title_fullStr Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning
title_full_unstemmed Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning
title_short Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning
title_sort optical encoding model based on orbital angular momentum powered by machine learning
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007020/
https://www.ncbi.nlm.nih.gov/pubmed/36904967
http://dx.doi.org/10.3390/s23052755
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