Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks

Structured light can be encoded to carry information for free-space optical communications with an extended degree of freedom to increase the capacity, however, the accuracy issue along with capacity increase is one of the biggest challenges that prevent practical applications. To achieve high accur...

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Main Authors: Zhang, Zilong, He, Wei, Zhao, Suyi, Gao, Yuan, Wang, Xin, Li, Xiaotian, Wang, Yuqi, Ma, Yunfei, Hu, Yetong, Shen, Yijie, Zhao, Changming
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/177942
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1779422024-06-03T06:15:01Z Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks Zhang, Zilong He, Wei Zhao, Suyi Gao, Yuan Wang, Xin Li, Xiaotian Wang, Yuqi Ma, Yunfei Hu, Yetong Shen, Yijie Zhao, Changming School of Electrical and Electronic Engineering School of Physical and Mathematical Sciences Centre for Disruptive Photonic Technologies (CDPT) Engineering Deep learning Information transmission Structured light can be encoded to carry information for free-space optical communications with an extended degree of freedom to increase the capacity, however, the accuracy issue along with capacity increase is one of the biggest challenges that prevent practical applications. To achieve high accuracy with high capacity by a simple method, they propose the spatial nonlinear conversion of structured light into a communication network, especially, realizing an ultra-high-accuracy point-to-multipoint (PtoMP) information transmission link. A series of coherently superposed spatial modes and their spatial nonlinear conversion states are used as information carriers to replace the prior orbital angular momentum beams and greatly expand channel capacity within quite low spatial mode order. Through the spatial nonlinear conversion of simple dual-mode superposition and a very basic neural network for machine learning-based recognition, as high as 99.5% accuracy for more than 500 modes is obtained. By a combination of diffuse reflection screens and multiple CCDs, the large observation angle PtoMP information transmission is also proved to be feasible. This work paves the way for practical large-scale multi-party information networks using structured light. Ministry of Education (MOE) Nanyang Technological University This study was sup-ported by The National Natural Science Foundation of China (NSFC) grant funded by the Chinses government (Grant No. 62375015). Y.S. thanks the support of a start grant from Nanyang Technological University and Singapore Ministry of Education (MOE) AcRF Tier 1 grant (RG157/23). 2024-06-03T06:15:01Z 2024-06-03T06:15:01Z 2024 Journal Article Zhang, Z., He, W., Zhao, S., Gao, Y., Wang, X., Li, X., Wang, Y., Ma, Y., Hu, Y., Shen, Y. & Zhao, C. (2024). Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks. Laser and Photonics Reviews, 2301225-. https://dx.doi.org/10.1002/lpor.202301225 1863-8880 https://hdl.handle.net/10356/177942 10.1002/lpor.202301225 2-s2.0-85185333362 2301225 en RG157/23 NTU SUG Laser and Photonics Reviews © 2024 Wiley-VCH GmbH. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Deep learning
Information transmission
spellingShingle Engineering
Deep learning
Information transmission
Zhang, Zilong
He, Wei
Zhao, Suyi
Gao, Yuan
Wang, Xin
Li, Xiaotian
Wang, Yuqi
Ma, Yunfei
Hu, Yetong
Shen, Yijie
Zhao, Changming
Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks
description Structured light can be encoded to carry information for free-space optical communications with an extended degree of freedom to increase the capacity, however, the accuracy issue along with capacity increase is one of the biggest challenges that prevent practical applications. To achieve high accuracy with high capacity by a simple method, they propose the spatial nonlinear conversion of structured light into a communication network, especially, realizing an ultra-high-accuracy point-to-multipoint (PtoMP) information transmission link. A series of coherently superposed spatial modes and their spatial nonlinear conversion states are used as information carriers to replace the prior orbital angular momentum beams and greatly expand channel capacity within quite low spatial mode order. Through the spatial nonlinear conversion of simple dual-mode superposition and a very basic neural network for machine learning-based recognition, as high as 99.5% accuracy for more than 500 modes is obtained. By a combination of diffuse reflection screens and multiple CCDs, the large observation angle PtoMP information transmission is also proved to be feasible. This work paves the way for practical large-scale multi-party information networks using structured light.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Zhang, Zilong
He, Wei
Zhao, Suyi
Gao, Yuan
Wang, Xin
Li, Xiaotian
Wang, Yuqi
Ma, Yunfei
Hu, Yetong
Shen, Yijie
Zhao, Changming
format Article
author Zhang, Zilong
He, Wei
Zhao, Suyi
Gao, Yuan
Wang, Xin
Li, Xiaotian
Wang, Yuqi
Ma, Yunfei
Hu, Yetong
Shen, Yijie
Zhao, Changming
author_sort Zhang, Zilong
title Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks
title_short Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks
title_full Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks
title_fullStr Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks
title_full_unstemmed Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks
title_sort spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks
publishDate 2024
url https://hdl.handle.net/10356/177942
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