Deep Learning Aided Signal Detection for SPAD-Based Underwater Optical Wireless Communications

In underwater optical wireless communication (UOWC) systems, using single photon avalanche photondiode (SPAD) as the detector can improve the transmission distance. However, the signal detection for SPAD-based systems is greatly challenged by the complex optical channel characteristics and SPAD nonl...

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Autores principales:	Rui Jiang, Caiming Sun, Long Zhang, Xinke Tang, Hongjie Wang, Aidong Zhang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Underwater optical wireless communication nonlinear distortion deep learning multilayer perception signal detection Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/2c58612fced94510873b7607ba9f9195
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id	oai:doaj.org-article:2c58612fced94510873b7607ba9f9195
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spelling	oai:doaj.org-article:2c58612fced94510873b7607ba9f91952021-11-20T00:00:30ZDeep Learning Aided Signal Detection for SPAD-Based Underwater Optical Wireless Communications2169-353610.1109/ACCESS.2020.2967461https://doaj.org/article/2c58612fced94510873b7607ba9f91952020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/8962099/https://doaj.org/toc/2169-3536In underwater optical wireless communication (UOWC) systems, using single photon avalanche photondiode (SPAD) as the detector can improve the transmission distance. However, the signal detection for SPAD-based systems is greatly challenged by the complex optical channel characteristics and SPAD nonlinear distortion. To address this issue, a novel deep learning aided signal detection scheme is proposed in this paper. By exploiting the physical mechanism and prior expert knowledge of the signal processing, a two-connected multilayer perception (MLP) network is integrated into the receiver. The first subnetwork is regarded as a channel compensation block while the second one works as a demodulator. With sophisticated numerical optical channel model and SPAD non-Poisson model, large amounts of training data are utilized to train the proposed model offline. Afterwards, the online data are recovered with the trained network. Simulation results verify that significant bit error ratio (BER) improvement can be achieved with the proposed scheme.Rui JiangCaiming SunLong ZhangXinke TangHongjie WangAidong ZhangIEEEarticleUnderwater optical wireless communicationnonlinear distortiondeep learningmultilayer perceptionsignal detectionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 20363-20374 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Underwater optical wireless communication nonlinear distortion deep learning multilayer perception signal detection Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Underwater optical wireless communication nonlinear distortion deep learning multilayer perception signal detection Electrical engineering. Electronics. Nuclear engineering TK1-9971 Rui Jiang Caiming Sun Long Zhang Xinke Tang Hongjie Wang Aidong Zhang Deep Learning Aided Signal Detection for SPAD-Based Underwater Optical Wireless Communications
description	In underwater optical wireless communication (UOWC) systems, using single photon avalanche photondiode (SPAD) as the detector can improve the transmission distance. However, the signal detection for SPAD-based systems is greatly challenged by the complex optical channel characteristics and SPAD nonlinear distortion. To address this issue, a novel deep learning aided signal detection scheme is proposed in this paper. By exploiting the physical mechanism and prior expert knowledge of the signal processing, a two-connected multilayer perception (MLP) network is integrated into the receiver. The first subnetwork is regarded as a channel compensation block while the second one works as a demodulator. With sophisticated numerical optical channel model and SPAD non-Poisson model, large amounts of training data are utilized to train the proposed model offline. Afterwards, the online data are recovered with the trained network. Simulation results verify that significant bit error ratio (BER) improvement can be achieved with the proposed scheme.
format	article
author	Rui Jiang Caiming Sun Long Zhang Xinke Tang Hongjie Wang Aidong Zhang
author_facet	Rui Jiang Caiming Sun Long Zhang Xinke Tang Hongjie Wang Aidong Zhang
author_sort	Rui Jiang
title	Deep Learning Aided Signal Detection for SPAD-Based Underwater Optical Wireless Communications
title_short	Deep Learning Aided Signal Detection for SPAD-Based Underwater Optical Wireless Communications
title_full	Deep Learning Aided Signal Detection for SPAD-Based Underwater Optical Wireless Communications
title_fullStr	Deep Learning Aided Signal Detection for SPAD-Based Underwater Optical Wireless Communications
title_full_unstemmed	Deep Learning Aided Signal Detection for SPAD-Based Underwater Optical Wireless Communications
title_sort	deep learning aided signal detection for spad-based underwater optical wireless communications
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/2c58612fced94510873b7607ba9f9195
work_keys_str_mv	AT ruijiang deeplearningaidedsignaldetectionforspadbasedunderwateropticalwirelesscommunications AT caimingsun deeplearningaidedsignaldetectionforspadbasedunderwateropticalwirelesscommunications AT longzhang deeplearningaidedsignaldetectionforspadbasedunderwateropticalwirelesscommunications AT xinketang deeplearningaidedsignaldetectionforspadbasedunderwateropticalwirelesscommunications AT hongjiewang deeplearningaidedsignaldetectionforspadbasedunderwateropticalwirelesscommunications AT aidongzhang deeplearningaidedsignaldetectionforspadbasedunderwateropticalwirelesscommunications
_version_	1718419825626185728

Deep Learning Aided Signal Detection for SPAD-Based Underwater Optical Wireless Communications

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