Enhanced Performance of PAM7 MISO Underwater VLC System Utilizing Machine Learning Algorithm Based on DBSCAN

After visible light communication drawing increasing attention, underwater visible light communication (UVLC) has attracted more interest in the research community nowadays. As multiple input single output (MISO) is getting increasingly widely used to improve the transmission speed in UVLC system, t...

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Autores principales:	Meng Shi, Yiheng Zhao, Weixiang Yu, Yuchong Chen, Nan Chi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2019
Materias:	MISO DBSCAN underwater visible light communication. Applied optics. Photonics TA1501-1820 Optics. Light QC350-467
Acceso en línea:	https://doaj.org/article/7d1968783ac04c4f881b56e2d72f2ca4
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id	oai:doaj.org-article:7d1968783ac04c4f881b56e2d72f2ca4
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spelling	oai:doaj.org-article:7d1968783ac04c4f881b56e2d72f2ca42021-11-30T00:00:09ZEnhanced Performance of PAM7 MISO Underwater VLC System Utilizing Machine Learning Algorithm Based on DBSCAN1943-065510.1109/JPHOT.2019.2928827https://doaj.org/article/7d1968783ac04c4f881b56e2d72f2ca42019-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/8764014/https://doaj.org/toc/1943-0655After visible light communication drawing increasing attention, underwater visible light communication (UVLC) has attracted more interest in the research community nowadays. As multiple input single output (MISO) is getting increasingly widely used to improve the transmission speed in UVLC system, the unbalance between multiple transmitters’ power is still a common phenomenon, which leads to the unequal spacing between each adjacent level and damages the system performance. In this paper, we study and analyze the unbalance between the two transmitters. Compared to a traditional hard decision, a density-based spatial clustering of applications with noise (DBSCAN) of a machine learning method is employed to get the actual center of each cluster and distinguish each level of PAM7 signals. In this way, a new decision curve substitutes traditional standard straight line as a constellation discrimination method. The experimental results show that up to 1.22 Gb/s over 1.2 m underwater visible light transmission can be achieved by using DBSCAN for PAM7 MISO signals. The measured bit error rate is well under the hard decision-forward error correction threshold of 3.8 × 10<sup>−3</sup>.Meng ShiYiheng ZhaoWeixiang YuYuchong ChenNan ChiIEEEarticleMISODBSCANunderwater visible light communication.Applied optics. PhotonicsTA1501-1820Optics. LightQC350-467ENIEEE Photonics Journal, Vol 11, Iss 4, Pp 1-13 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	MISO DBSCAN underwater visible light communication. Applied optics. Photonics TA1501-1820 Optics. Light QC350-467
spellingShingle	MISO DBSCAN underwater visible light communication. Applied optics. Photonics TA1501-1820 Optics. Light QC350-467 Meng Shi Yiheng Zhao Weixiang Yu Yuchong Chen Nan Chi Enhanced Performance of PAM7 MISO Underwater VLC System Utilizing Machine Learning Algorithm Based on DBSCAN
description	After visible light communication drawing increasing attention, underwater visible light communication (UVLC) has attracted more interest in the research community nowadays. As multiple input single output (MISO) is getting increasingly widely used to improve the transmission speed in UVLC system, the unbalance between multiple transmitters’ power is still a common phenomenon, which leads to the unequal spacing between each adjacent level and damages the system performance. In this paper, we study and analyze the unbalance between the two transmitters. Compared to a traditional hard decision, a density-based spatial clustering of applications with noise (DBSCAN) of a machine learning method is employed to get the actual center of each cluster and distinguish each level of PAM7 signals. In this way, a new decision curve substitutes traditional standard straight line as a constellation discrimination method. The experimental results show that up to 1.22 Gb/s over 1.2 m underwater visible light transmission can be achieved by using DBSCAN for PAM7 MISO signals. The measured bit error rate is well under the hard decision-forward error correction threshold of 3.8 × 10<sup>−3</sup>.
format	article
author	Meng Shi Yiheng Zhao Weixiang Yu Yuchong Chen Nan Chi
author_facet	Meng Shi Yiheng Zhao Weixiang Yu Yuchong Chen Nan Chi
author_sort	Meng Shi
title	Enhanced Performance of PAM7 MISO Underwater VLC System Utilizing Machine Learning Algorithm Based on DBSCAN
title_short	Enhanced Performance of PAM7 MISO Underwater VLC System Utilizing Machine Learning Algorithm Based on DBSCAN
title_full	Enhanced Performance of PAM7 MISO Underwater VLC System Utilizing Machine Learning Algorithm Based on DBSCAN
title_fullStr	Enhanced Performance of PAM7 MISO Underwater VLC System Utilizing Machine Learning Algorithm Based on DBSCAN
title_full_unstemmed	Enhanced Performance of PAM7 MISO Underwater VLC System Utilizing Machine Learning Algorithm Based on DBSCAN
title_sort	enhanced performance of pam7 miso underwater vlc system utilizing machine learning algorithm based on dbscan
publisher	IEEE
publishDate	2019
url	https://doaj.org/article/7d1968783ac04c4f881b56e2d72f2ca4
work_keys_str_mv	AT mengshi enhancedperformanceofpam7misounderwatervlcsystemutilizingmachinelearningalgorithmbasedondbscan AT yihengzhao enhancedperformanceofpam7misounderwatervlcsystemutilizingmachinelearningalgorithmbasedondbscan AT weixiangyu enhancedperformanceofpam7misounderwatervlcsystemutilizingmachinelearningalgorithmbasedondbscan AT yuchongchen enhancedperformanceofpam7misounderwatervlcsystemutilizingmachinelearningalgorithmbasedondbscan AT nanchi enhancedperformanceofpam7misounderwatervlcsystemutilizingmachinelearningalgorithmbasedondbscan
_version_	1718406872349802496

Enhanced Performance of PAM7 MISO Underwater VLC System Utilizing Machine Learning Algorithm Based on DBSCAN

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