Machine learning for DCO-OFDM based LiFi.

Light fidelity (LiFi) uses different forms of orthogonal frequency division multiplexing (OFDM), including DC biased optical OFDM (DCO-OFDM). In DCO-OFDM, the use of a large DC bias causes optical power inefficiency, while a small bias leads to higher clipping noise. Hence, finding an appropriate DC...

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Autores principales:	Krishna Saha Purnita, M Rubaiyat Hossain Mondal
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/baa7cb46d13948cfabb8faab3ba404dd
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spelling	oai:doaj.org-article:baa7cb46d13948cfabb8faab3ba404dd2021-12-02T20:16:14ZMachine learning for DCO-OFDM based LiFi.1932-620310.1371/journal.pone.0259955https://doaj.org/article/baa7cb46d13948cfabb8faab3ba404dd2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259955https://doaj.org/toc/1932-6203Light fidelity (LiFi) uses different forms of orthogonal frequency division multiplexing (OFDM), including DC biased optical OFDM (DCO-OFDM). In DCO-OFDM, the use of a large DC bias causes optical power inefficiency, while a small bias leads to higher clipping noise. Hence, finding an appropriate DC bias level for DCO-OFDM is important. This paper applies machine learning (ML) algorithms to find optimum DC-bias value for DCO-OFDM based LiFi systems. For this, a dataset is generated for DCO-OFDM using MATLAB tool. Next, ML algorithms are applied using Python programming language. ML is used to find the important attributes of DCO-OFDM that influence the optimum DC bias. It is shown here that the optimum DC bias is a function of several factors including, the minimum, the standard deviation, and the maximum value of the bipolar OFDM signal, and the constellation size. Next, linear and polynomial regression algorithms are successfully applied to predict the optimum DC bias value. Results show that polynomial regression of order 2 can predict the optimum DC bias value with a coefficient of determination of 96.77% which confirms the effectiveness of the prediction.Krishna Saha PurnitaM Rubaiyat Hossain MondalPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0259955 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Krishna Saha Purnita M Rubaiyat Hossain Mondal Machine learning for DCO-OFDM based LiFi.
description	Light fidelity (LiFi) uses different forms of orthogonal frequency division multiplexing (OFDM), including DC biased optical OFDM (DCO-OFDM). In DCO-OFDM, the use of a large DC bias causes optical power inefficiency, while a small bias leads to higher clipping noise. Hence, finding an appropriate DC bias level for DCO-OFDM is important. This paper applies machine learning (ML) algorithms to find optimum DC-bias value for DCO-OFDM based LiFi systems. For this, a dataset is generated for DCO-OFDM using MATLAB tool. Next, ML algorithms are applied using Python programming language. ML is used to find the important attributes of DCO-OFDM that influence the optimum DC bias. It is shown here that the optimum DC bias is a function of several factors including, the minimum, the standard deviation, and the maximum value of the bipolar OFDM signal, and the constellation size. Next, linear and polynomial regression algorithms are successfully applied to predict the optimum DC bias value. Results show that polynomial regression of order 2 can predict the optimum DC bias value with a coefficient of determination of 96.77% which confirms the effectiveness of the prediction.
format	article
author	Krishna Saha Purnita M Rubaiyat Hossain Mondal
author_facet	Krishna Saha Purnita M Rubaiyat Hossain Mondal
author_sort	Krishna Saha Purnita
title	Machine learning for DCO-OFDM based LiFi.
title_short	Machine learning for DCO-OFDM based LiFi.
title_full	Machine learning for DCO-OFDM based LiFi.
title_fullStr	Machine learning for DCO-OFDM based LiFi.
title_full_unstemmed	Machine learning for DCO-OFDM based LiFi.
title_sort	machine learning for dco-ofdm based lifi.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/baa7cb46d13948cfabb8faab3ba404dd
work_keys_str_mv	AT krishnasahapurnita machinelearningfordcoofdmbasedlifi AT mrubaiyathossainmondal machinelearningfordcoofdmbasedlifi
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Machine learning for DCO-OFDM based LiFi.

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