Structured Light Transmission under Free Space Jamming: An Enhanced Mode Identification and Signal-to-Jamming Ratio Estimation Using Machine Learning
In this paper, we develop new classification and estimation algorithms in the context of free space optics (FSO) transmission. Firstly, a new classification algorithm is proposed to address efficiently the problem of identifying structured light modes under jamming effect. The proposed method exploi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
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| Series: | Photonics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-6732/9/3/200 |
| _version_ | 1827626560891387904 |
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| author | Ahmed B. Ibrahim Amr M. Ragheb Waddah S. Saif Saleh A. Alshebeili |
| author_facet | Ahmed B. Ibrahim Amr M. Ragheb Waddah S. Saif Saleh A. Alshebeili |
| author_sort | Ahmed B. Ibrahim |
| collection | DOAJ |
| description | In this paper, we develop new classification and estimation algorithms in the context of free space optics (FSO) transmission. Firstly, a new classification algorithm is proposed to address efficiently the problem of identifying structured light modes under jamming effect. The proposed method exploits support vector machine (SVM) and the histogram of oriented gradients algorithm for the classification task within a specific range of signal-to-jamming ratio (SJR). The SVM model is trained and tested using experimental data generated using different modes of the structured light beam, including the 8-ary Laguerre Gaussian (LG), 8-ary superposition-LG, and 16-ary Hermite Gaussian (HG) formats. Secondly, a new algorithm is proposed using neural networks for the sake of predicting the value of SJR with promising results within the investigated range of values between −5 dB and 3 dB. |
| first_indexed | 2024-03-09T12:57:22Z |
| format | Article |
| id | doaj.art-ea662304139f4b5bae5c28a5f72210c4 |
| institution | Directory Open Access Journal |
| issn | 2304-6732 |
| language | English |
| last_indexed | 2024-03-09T12:57:22Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj.art-ea662304139f4b5bae5c28a5f72210c42023-11-30T21:59:36ZengMDPI AGPhotonics2304-67322022-03-019320010.3390/photonics9030200Structured Light Transmission under Free Space Jamming: An Enhanced Mode Identification and Signal-to-Jamming Ratio Estimation Using Machine LearningAhmed B. Ibrahim0Amr M. Ragheb1Waddah S. Saif2Saleh A. Alshebeili3Electrical Engineering Department, King Saud University, Riyadh 11421, Saudi ArabiaElectrical Engineering Department, King Saud University, Riyadh 11421, Saudi ArabiaElectrical Engineering Department, King Saud University, Riyadh 11421, Saudi ArabiaElectrical Engineering Department, King Saud University, Riyadh 11421, Saudi ArabiaIn this paper, we develop new classification and estimation algorithms in the context of free space optics (FSO) transmission. Firstly, a new classification algorithm is proposed to address efficiently the problem of identifying structured light modes under jamming effect. The proposed method exploits support vector machine (SVM) and the histogram of oriented gradients algorithm for the classification task within a specific range of signal-to-jamming ratio (SJR). The SVM model is trained and tested using experimental data generated using different modes of the structured light beam, including the 8-ary Laguerre Gaussian (LG), 8-ary superposition-LG, and 16-ary Hermite Gaussian (HG) formats. Secondly, a new algorithm is proposed using neural networks for the sake of predicting the value of SJR with promising results within the investigated range of values between −5 dB and 3 dB.https://www.mdpi.com/2304-6732/9/3/200free space opticsstructured light beam modes classificationsupport vector machinehistogram of oriented gradientsneural networksimage projection |
| spellingShingle | Ahmed B. Ibrahim Amr M. Ragheb Waddah S. Saif Saleh A. Alshebeili Structured Light Transmission under Free Space Jamming: An Enhanced Mode Identification and Signal-to-Jamming Ratio Estimation Using Machine Learning Photonics free space optics structured light beam modes classification support vector machine histogram of oriented gradients neural networks image projection |
| title | Structured Light Transmission under Free Space Jamming: An Enhanced Mode Identification and Signal-to-Jamming Ratio Estimation Using Machine Learning |
| title_full | Structured Light Transmission under Free Space Jamming: An Enhanced Mode Identification and Signal-to-Jamming Ratio Estimation Using Machine Learning |
| title_fullStr | Structured Light Transmission under Free Space Jamming: An Enhanced Mode Identification and Signal-to-Jamming Ratio Estimation Using Machine Learning |
| title_full_unstemmed | Structured Light Transmission under Free Space Jamming: An Enhanced Mode Identification and Signal-to-Jamming Ratio Estimation Using Machine Learning |
| title_short | Structured Light Transmission under Free Space Jamming: An Enhanced Mode Identification and Signal-to-Jamming Ratio Estimation Using Machine Learning |
| title_sort | structured light transmission under free space jamming an enhanced mode identification and signal to jamming ratio estimation using machine learning |
| topic | free space optics structured light beam modes classification support vector machine histogram of oriented gradients neural networks image projection |
| url | https://www.mdpi.com/2304-6732/9/3/200 |
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