An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications
By modulating the optical power of the light-emitting diode (LED) in accordance with the electrical source and using a photodetector to convert the corresponding optical variation back into electrical signals, visible light communication (VLC) has been developed to achieve lighting and communication...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/8/948 |
| _version_ | 1797537475818684416 |
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| author | Jenn-Kaie Lain Yan-He Chen |
| author_facet | Jenn-Kaie Lain Yan-He Chen |
| author_sort | Jenn-Kaie Lain |
| collection | DOAJ |
| description | By modulating the optical power of the light-emitting diode (LED) in accordance with the electrical source and using a photodetector to convert the corresponding optical variation back into electrical signals, visible light communication (VLC) has been developed to achieve lighting and communications simultaneously, and is now considered one of the promising technologies for handling the continuing increases in data demands, especially indoors, for next generation wireless broadband systems. During the process of electrical-to-optical conversion using LEDs in VLC, however, signal distortion occurs due to LED nonlinearity, resulting in VLC system performance degradation. Artificial neural networks (ANNs) are thought to be capable of achieving universal function approximation, which was the motivation for introducing ANN predistortion to compensate for LED nonlinearity in this paper. Without using additional training sequences, the related parameters in the proposed ANN predistorter can be adaptively updated, using a feedback replica of the original electrical source, to track the LED time-variant characteristics due to temperature variation and aging. Computer simulations and experimental implementation were carried out to evaluate and validate the performance of the proposed ANN predistorter against existing adaptive predistorter schemes, such as the normalized least mean square predistorter and the Chebyshev polynomial predistorter. |
| first_indexed | 2024-03-10T12:16:42Z |
| format | Article |
| id | doaj.art-a9a0bbb5985747d89230dedf07ce754c |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T12:16:42Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a9a0bbb5985747d89230dedf07ce754c2023-11-21T15:49:59ZengMDPI AGElectronics2079-92922021-04-0110894810.3390/electronics10080948An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light CommunicationsJenn-Kaie Lain0Yan-He Chen1Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu 64002, TaiwanDepartment of Electronic Engineering, National Yunlin University of Science and Technology, Douliu 64002, TaiwanBy modulating the optical power of the light-emitting diode (LED) in accordance with the electrical source and using a photodetector to convert the corresponding optical variation back into electrical signals, visible light communication (VLC) has been developed to achieve lighting and communications simultaneously, and is now considered one of the promising technologies for handling the continuing increases in data demands, especially indoors, for next generation wireless broadband systems. During the process of electrical-to-optical conversion using LEDs in VLC, however, signal distortion occurs due to LED nonlinearity, resulting in VLC system performance degradation. Artificial neural networks (ANNs) are thought to be capable of achieving universal function approximation, which was the motivation for introducing ANN predistortion to compensate for LED nonlinearity in this paper. Without using additional training sequences, the related parameters in the proposed ANN predistorter can be adaptively updated, using a feedback replica of the original electrical source, to track the LED time-variant characteristics due to temperature variation and aging. Computer simulations and experimental implementation were carried out to evaluate and validate the performance of the proposed ANN predistorter against existing adaptive predistorter schemes, such as the normalized least mean square predistorter and the Chebyshev polynomial predistorter.https://www.mdpi.com/2079-9292/10/8/948artificial neural networksdigital predistorterLED nonlinearityvisible light communications |
| spellingShingle | Jenn-Kaie Lain Yan-He Chen An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications Electronics artificial neural networks digital predistorter LED nonlinearity visible light communications |
| title | An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications |
| title_full | An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications |
| title_fullStr | An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications |
| title_full_unstemmed | An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications |
| title_short | An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications |
| title_sort | ann based adaptive predistorter for led nonlinearity in indoor visible light communications |
| topic | artificial neural networks digital predistorter LED nonlinearity visible light communications |
| url | https://www.mdpi.com/2079-9292/10/8/948 |
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