Research on Orbital Angular Momentum Recognition Technology Based on a Convolutional Neural Network
In underwater wireless optical communication (UWOC), a vortex beam carrying orbital angular momentum has a spatial spiral phase distribution, which provides spatial freedom for UWOC and, as a new information modulation dimension resource, it can greatly improve channel capacity and spectral efficien...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/2/971 |
| _version_ | 1797437188792647680 |
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| author | Xiaoji Li Leiming Sun Jiemei Huang Fanze Zeng |
| author_facet | Xiaoji Li Leiming Sun Jiemei Huang Fanze Zeng |
| author_sort | Xiaoji Li |
| collection | DOAJ |
| description | In underwater wireless optical communication (UWOC), a vortex beam carrying orbital angular momentum has a spatial spiral phase distribution, which provides spatial freedom for UWOC and, as a new information modulation dimension resource, it can greatly improve channel capacity and spectral efficiency. In a case of the disturbance of a vortex beam by ocean turbulence, where a Laguerre–Gaussian (<i>LG</i>) beam carrying orbital angular momentum (OAM) is damaged by turbulence and distortion, which affects OAM pattern recognition, and the phase feature of the phase map not only has spiral wavefront but also phase singularity feature, the convolutional neural network (CNN) model can effectively extract the information of the distorted OAM phase map to realize the recognition of dual-mode OAM and single-mode OAM. The phase map of the Laguerre–Gaussian beam passing through ocean turbulence was used as a dataset to simulate and analyze the OAM recognition effect during turbulence caused by different temperature ratios and salinity. The results showed that, during strong turbulence <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>C</mi><mi>n</mi><mn>2</mn></msubsup><mo>=</mo><mn>1.0</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>13</mn></mrow></msup><msup><mi mathvariant="normal">K</mi><mn>2</mn></msup><msup><mi mathvariant="normal">m</mi><mrow><mo>−</mo><mn>2</mn><mo>/</mo><mn>3</mn></mrow></msup></mrow></semantics></math></inline-formula>, when different <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ω</mi></semantics></math></inline-formula> = −1.75, the recognition rate of dual-mode OAM (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>ℓ</mo></semantics></math></inline-formula> = ±1~±5, ±1~±6, ±1~±7, ±1~±8, ±1~±9, ±1~±10) had higher recognition rates of 100%, 100%, 100%, 100%, 98.89%, and 98.67% and single-mode OAM (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>ℓ</mo></semantics></math></inline-formula> = 1~5, 1~6, 1~7, 1~8, 1~9, 1~10) had higher recognition rates of 93.33%, 92.77%, 92.33%, 90%, 87.78%, and 84%, respectively. With the increase in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ω</mi></semantics></math></inline-formula>, the recognition accuracy of the CNN model will gradually decrease, and in a fixed case, the dual-mode OAM has stronger anti-interference ability than single-mode OAM. These results may provide a reference for optical communication technologies that implement high-capacity OAM. |
| first_indexed | 2024-03-09T11:16:12Z |
| format | Article |
| id | doaj.art-b77e7bd1cd3b45a9b9cab126894227fb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:16:12Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b77e7bd1cd3b45a9b9cab126894227fb2023-12-01T00:30:40ZengMDPI AGSensors1424-82202023-01-0123297110.3390/s23020971Research on Orbital Angular Momentum Recognition Technology Based on a Convolutional Neural NetworkXiaoji Li0Leiming Sun1Jiemei Huang2Fanze Zeng3Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education, Guilin University of Electronic Technology, Guilin 541004, ChinaKey Laboratory of Cognitive Radio and Information Processing, Ministry of Education, Guilin University of Electronic Technology, Guilin 541004, ChinaKey Laboratory of Cognitive Radio and Information Processing, Ministry of Education, Guilin University of Electronic Technology, Guilin 541004, ChinaKey Laboratory of Cognitive Radio and Information Processing, Ministry of Education, Guilin University of Electronic Technology, Guilin 541004, ChinaIn underwater wireless optical communication (UWOC), a vortex beam carrying orbital angular momentum has a spatial spiral phase distribution, which provides spatial freedom for UWOC and, as a new information modulation dimension resource, it can greatly improve channel capacity and spectral efficiency. In a case of the disturbance of a vortex beam by ocean turbulence, where a Laguerre–Gaussian (<i>LG</i>) beam carrying orbital angular momentum (OAM) is damaged by turbulence and distortion, which affects OAM pattern recognition, and the phase feature of the phase map not only has spiral wavefront but also phase singularity feature, the convolutional neural network (CNN) model can effectively extract the information of the distorted OAM phase map to realize the recognition of dual-mode OAM and single-mode OAM. The phase map of the Laguerre–Gaussian beam passing through ocean turbulence was used as a dataset to simulate and analyze the OAM recognition effect during turbulence caused by different temperature ratios and salinity. The results showed that, during strong turbulence <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>C</mi><mi>n</mi><mn>2</mn></msubsup><mo>=</mo><mn>1.0</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>13</mn></mrow></msup><msup><mi mathvariant="normal">K</mi><mn>2</mn></msup><msup><mi mathvariant="normal">m</mi><mrow><mo>−</mo><mn>2</mn><mo>/</mo><mn>3</mn></mrow></msup></mrow></semantics></math></inline-formula>, when different <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ω</mi></semantics></math></inline-formula> = −1.75, the recognition rate of dual-mode OAM (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>ℓ</mo></semantics></math></inline-formula> = ±1~±5, ±1~±6, ±1~±7, ±1~±8, ±1~±9, ±1~±10) had higher recognition rates of 100%, 100%, 100%, 100%, 98.89%, and 98.67% and single-mode OAM (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>ℓ</mo></semantics></math></inline-formula> = 1~5, 1~6, 1~7, 1~8, 1~9, 1~10) had higher recognition rates of 93.33%, 92.77%, 92.33%, 90%, 87.78%, and 84%, respectively. With the increase in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ω</mi></semantics></math></inline-formula>, the recognition accuracy of the CNN model will gradually decrease, and in a fixed case, the dual-mode OAM has stronger anti-interference ability than single-mode OAM. These results may provide a reference for optical communication technologies that implement high-capacity OAM.https://www.mdpi.com/1424-8220/23/2/971orbital angular momentumocean turbulenceconvolutional neural networks |
| spellingShingle | Xiaoji Li Leiming Sun Jiemei Huang Fanze Zeng Research on Orbital Angular Momentum Recognition Technology Based on a Convolutional Neural Network Sensors orbital angular momentum ocean turbulence convolutional neural networks |
| title | Research on Orbital Angular Momentum Recognition Technology Based on a Convolutional Neural Network |
| title_full | Research on Orbital Angular Momentum Recognition Technology Based on a Convolutional Neural Network |
| title_fullStr | Research on Orbital Angular Momentum Recognition Technology Based on a Convolutional Neural Network |
| title_full_unstemmed | Research on Orbital Angular Momentum Recognition Technology Based on a Convolutional Neural Network |
| title_short | Research on Orbital Angular Momentum Recognition Technology Based on a Convolutional Neural Network |
| title_sort | research on orbital angular momentum recognition technology based on a convolutional neural network |
| topic | orbital angular momentum ocean turbulence convolutional neural networks |
| url | https://www.mdpi.com/1424-8220/23/2/971 |
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