Efficient Recognition of the Propagated Orbital Angular Momentum Modes in Turbulences With the Convolutional Neural Network
The vortex beam carrying orbital angular momentum (OAM) has attracted great attentions in optical communication field, which can extend the channel capacity of communication system due to the orthogonality between different OAM modes. Generally, atmospheric turbulence can distort the helical phase f...
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| Format: | Article |
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IEEE
2019-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/8712532/ |
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| author | Zikun Wang Maxime Irene Dedo Kai Guo Keya Zhou Fei Shen Yongxuan Sun Shutian Liu Zhongyi Guo |
| author_facet | Zikun Wang Maxime Irene Dedo Kai Guo Keya Zhou Fei Shen Yongxuan Sun Shutian Liu Zhongyi Guo |
| author_sort | Zikun Wang |
| collection | DOAJ |
| description | The vortex beam carrying orbital angular momentum (OAM) has attracted great attentions in optical communication field, which can extend the channel capacity of communication system due to the orthogonality between different OAM modes. Generally, atmospheric turbulence can distort the helical phase fronts of OAM beams, which presents a critical challenge to the effective recognition of OAM modes. Recently, convolutional neural network (CNN), as a model of deep learning, has been widely applied to machine vision. In this paper, based on the CNN theory, we make a tradeoff between the computational complexity of the system and the efficiency of recognition by establishing a specially designed six-layer CNN structure in CPU station to efficiently achieve the recognition of OAM mode in turbulent environment through the feature extraction of the received Laguerre–Gaussian beam's intensity distributions. Furthermore, we examine the performances of our designed CNN with respect to various turbulence levels, transmission distances, mode spacings, and we have also compared the performances of recognizing single OAM mode with multiplexed OAM modes. The numerical simulation shows that basing on CNN method, the coaxial multiplexed OAM modes can obtain higher recognizing accuracy about 96.25% even under long transmission distance with strong turbulence. It is anticipated that the results might be helpful for future implementation of high-capacity OAM-based optical communication technology. |
| format | Article |
| id | doaj-art-16161645b4534bdc8c3b44d7cfc19bd6 |
| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-16161645b4534bdc8c3b44d7cfc19bd62025-08-20T03:32:54ZengIEEEIEEE Photonics Journal1943-06552019-01-0111311410.1109/JPHOT.2019.29162078712532Efficient Recognition of the Propagated Orbital Angular Momentum Modes in Turbulences With the Convolutional Neural NetworkZikun Wang0Maxime Irene Dedo1https://orcid.org/0000-0001-8803-7921Kai Guo2Keya Zhou3Fei Shen4Yongxuan Sun5Shutian Liu6Zhongyi Guo7https://orcid.org/0000-0001-7282-2503School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei, ChinaDepartment of Physics, Harbin Institute of Technology, Harbin, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei, ChinaDepartment of Physics, Harbin Institute of Technology, Harbin, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei, ChinaThe vortex beam carrying orbital angular momentum (OAM) has attracted great attentions in optical communication field, which can extend the channel capacity of communication system due to the orthogonality between different OAM modes. Generally, atmospheric turbulence can distort the helical phase fronts of OAM beams, which presents a critical challenge to the effective recognition of OAM modes. Recently, convolutional neural network (CNN), as a model of deep learning, has been widely applied to machine vision. In this paper, based on the CNN theory, we make a tradeoff between the computational complexity of the system and the efficiency of recognition by establishing a specially designed six-layer CNN structure in CPU station to efficiently achieve the recognition of OAM mode in turbulent environment through the feature extraction of the received Laguerre–Gaussian beam's intensity distributions. Furthermore, we examine the performances of our designed CNN with respect to various turbulence levels, transmission distances, mode spacings, and we have also compared the performances of recognizing single OAM mode with multiplexed OAM modes. The numerical simulation shows that basing on CNN method, the coaxial multiplexed OAM modes can obtain higher recognizing accuracy about 96.25% even under long transmission distance with strong turbulence. It is anticipated that the results might be helpful for future implementation of high-capacity OAM-based optical communication technology.https://ieeexplore.ieee.org/document/8712532/Pattern recognitionmachine visionoptical vorticesfree-space optical communicationatmospheric turbulence |
| spellingShingle | Zikun Wang Maxime Irene Dedo Kai Guo Keya Zhou Fei Shen Yongxuan Sun Shutian Liu Zhongyi Guo Efficient Recognition of the Propagated Orbital Angular Momentum Modes in Turbulences With the Convolutional Neural Network IEEE Photonics Journal Pattern recognition machine vision optical vortices free-space optical communication atmospheric turbulence |
| title | Efficient Recognition of the Propagated Orbital Angular Momentum Modes in Turbulences With the Convolutional Neural Network |
| title_full | Efficient Recognition of the Propagated Orbital Angular Momentum Modes in Turbulences With the Convolutional Neural Network |
| title_fullStr | Efficient Recognition of the Propagated Orbital Angular Momentum Modes in Turbulences With the Convolutional Neural Network |
| title_full_unstemmed | Efficient Recognition of the Propagated Orbital Angular Momentum Modes in Turbulences With the Convolutional Neural Network |
| title_short | Efficient Recognition of the Propagated Orbital Angular Momentum Modes in Turbulences With the Convolutional Neural Network |
| title_sort | efficient recognition of the propagated orbital angular momentum modes in turbulences with the convolutional neural network |
| topic | Pattern recognition machine vision optical vortices free-space optical communication atmospheric turbulence |
| url | https://ieeexplore.ieee.org/document/8712532/ |
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