A MIMO Detector With Deep-Neural-Network for Faster-Than-Nyquist Optical Wireless Communications
Conventional multiple input multiple output (MIMO) detection algorithms face challenges related to computational complexity and limited performance when handling high-dimensional inputs and complex channel conditions. In order to enhance signal recovery accuracy in atmospheric turbulence channels fo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Photonics Journal |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10462083/ |
| _version_ | 1797243278594146304 |
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| author | Minghua Cao Ruifang Yao Qinxue Sun Yue Zhang Qing Yang Huiqin Wang |
| author_facet | Minghua Cao Ruifang Yao Qinxue Sun Yue Zhang Qing Yang Huiqin Wang |
| author_sort | Minghua Cao |
| collection | DOAJ |
| description | Conventional multiple input multiple output (MIMO) detection algorithms face challenges related to computational complexity and limited performance when handling high-dimensional inputs and complex channel conditions. In order to enhance signal recovery accuracy in atmospheric turbulence channels for faster-than-Nyquist (FTN) optical wireless communication (OWC) systems, a deep learning (DL) based MIMO detector is proposed. By leveraging a deep neural network (DNN), it becomes possible to learn nonlinear mappings within MIMO systems, resulting in improved detection performance while reducing computational overheads. Simulation results validate that our proposed DNN detector achieves comparable performance to the maximum likelihood (ML) method, while reducing complexity by 40%. |
| first_indexed | 2024-04-24T18:52:35Z |
| format | Article |
| id | doaj.art-42132268d84d4bf985851023eab7d1c5 |
| institution | Directory Open Access Journal |
| issn | 1943-0655 |
| language | English |
| last_indexed | 2024-04-24T18:52:35Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj.art-42132268d84d4bf985851023eab7d1c52024-03-26T17:48:29ZengIEEEIEEE Photonics Journal1943-06552024-01-011621910.1109/JPHOT.2024.337300210462083A MIMO Detector With Deep-Neural-Network for Faster-Than-Nyquist Optical Wireless CommunicationsMinghua Cao0https://orcid.org/0000-0002-1610-2007Ruifang Yao1https://orcid.org/0009-0004-6280-3789Qinxue Sun2https://orcid.org/0009-0002-9966-9718Yue Zhang3https://orcid.org/0000-0001-5318-2475Qing Yang4https://orcid.org/0009-0007-1271-3883Huiqin Wang5https://orcid.org/0000-0001-9026-3290School of Computer and Communication, Lanzhou University of Technology, Lanzhou, ChinaSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou, ChinaSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou, ChinaSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou, ChinaSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou, ChinaSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou, ChinaConventional multiple input multiple output (MIMO) detection algorithms face challenges related to computational complexity and limited performance when handling high-dimensional inputs and complex channel conditions. In order to enhance signal recovery accuracy in atmospheric turbulence channels for faster-than-Nyquist (FTN) optical wireless communication (OWC) systems, a deep learning (DL) based MIMO detector is proposed. By leveraging a deep neural network (DNN), it becomes possible to learn nonlinear mappings within MIMO systems, resulting in improved detection performance while reducing computational overheads. Simulation results validate that our proposed DNN detector achieves comparable performance to the maximum likelihood (ML) method, while reducing complexity by 40%.https://ieeexplore.ieee.org/document/10462083/Deep neural networkfaster-than-nyquistmultiple input multiple outputoptical wireless communication |
| spellingShingle | Minghua Cao Ruifang Yao Qinxue Sun Yue Zhang Qing Yang Huiqin Wang A MIMO Detector With Deep-Neural-Network for Faster-Than-Nyquist Optical Wireless Communications IEEE Photonics Journal Deep neural network faster-than-nyquist multiple input multiple output optical wireless communication |
| title | A MIMO Detector With Deep-Neural-Network for Faster-Than-Nyquist Optical Wireless Communications |
| title_full | A MIMO Detector With Deep-Neural-Network for Faster-Than-Nyquist Optical Wireless Communications |
| title_fullStr | A MIMO Detector With Deep-Neural-Network for Faster-Than-Nyquist Optical Wireless Communications |
| title_full_unstemmed | A MIMO Detector With Deep-Neural-Network for Faster-Than-Nyquist Optical Wireless Communications |
| title_short | A MIMO Detector With Deep-Neural-Network for Faster-Than-Nyquist Optical Wireless Communications |
| title_sort | mimo detector with deep neural network for faster than nyquist optical wireless communications |
| topic | Deep neural network faster-than-nyquist multiple input multiple output optical wireless communication |
| url | https://ieeexplore.ieee.org/document/10462083/ |
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