Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning
Based on orbital angular momentum (OAM) properties of Laguerre–Gaussian beams LG(<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo>,</mo><mo>ℓ</mo></mrow&...
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MDPI AG
2023-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/5/2755 |
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| author | Erick Lamilla Christian Sacarelo Manuel S. Alvarez-Alvarado Arturo Pazmino Peter Iza |
| author_facet | Erick Lamilla Christian Sacarelo Manuel S. Alvarez-Alvarado Arturo Pazmino Peter Iza |
| author_sort | Erick Lamilla |
| collection | DOAJ |
| description | Based on orbital angular momentum (OAM) properties of Laguerre–Gaussian beams LG(<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo>,</mo><mo>ℓ</mo></mrow></semantics></math></inline-formula>), 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 <i>p</i> and <i>ℓ</i> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>=</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>9</mn></mrow></msup></mrow></semantics></math></inline-formula> for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>10.2</mn></mrow></semantics></math></inline-formula> dB of signal-to-noise ratio in one of the SVM models. |
| first_indexed | 2024-03-11T07:10:42Z |
| format | Article |
| id | doaj.art-a9f27863b8f741fe9c5657083d7d5f08 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T07:10:42Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a9f27863b8f741fe9c5657083d7d5f082023-11-17T08:39:23ZengMDPI AGSensors1424-82202023-03-01235275510.3390/s23052755Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine LearningErick Lamilla0Christian Sacarelo1Manuel S. Alvarez-Alvarado2Arturo Pazmino3Peter Iza4Escuela Superior Politécnica del Litoral, ESPOL, Departamento de Física, Campus Gustavo Galindo, Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil 090150, EcuadorEscuela Superior Politécnica del Litoral, ESPOL, Departamento de Física, Campus Gustavo Galindo, Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil 090150, EcuadorEscuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación(FIEC), Campus Gustavo Galindo, Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil 090150, EcuadorEscuela Superior Politécnica del Litoral, ESPOL, Departamento de Física, Campus Gustavo Galindo, Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil 090150, EcuadorEscuela Superior Politécnica del Litoral, ESPOL, Departamento de Física, Campus Gustavo Galindo, Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil 090150, EcuadorBased on orbital angular momentum (OAM) properties of Laguerre–Gaussian beams LG(<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo>,</mo><mo>ℓ</mo></mrow></semantics></math></inline-formula>), 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 <i>p</i> and <i>ℓ</i> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>=</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>9</mn></mrow></msup></mrow></semantics></math></inline-formula> for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>10.2</mn></mrow></semantics></math></inline-formula> dB of signal-to-noise ratio in one of the SVM models.https://www.mdpi.com/1424-8220/23/5/2755machine learningLG-beamsOAM-beamsoptical encoding model |
| spellingShingle | Erick Lamilla Christian Sacarelo Manuel S. Alvarez-Alvarado Arturo Pazmino Peter Iza Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning Sensors machine learning LG-beams OAM-beams optical encoding model |
| 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 | machine learning LG-beams OAM-beams optical encoding model |
| url | https://www.mdpi.com/1424-8220/23/5/2755 |
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