When high PAPR reduction meets CNN: A PRD framework

One of the most important factors limiting the performance of OFDM (Orthogonal Frequency Division Multiplexing) system is high PAPR (Peak to Average Power Ratio). Great efforts have been made in suppressing PAPR, but their implementation often requires pre-processing all input signals, leading to ex...

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Autores principales:	Yaoqi Yang, Xianglin Wei, Renhui Xu, Laixian Peng
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	convolutional neural network orthogonal frequency division multiplexing peak to average power ratio Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/8ce80e5396bf407db3f4c27e3e0adf14
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id	oai:doaj.org-article:8ce80e5396bf407db3f4c27e3e0adf14
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spelling	oai:doaj.org-article:8ce80e5396bf407db3f4c27e3e0adf142021-11-09T02:04:06ZWhen high PAPR reduction meets CNN: A PRD framework10.3934/mbe.20212691551-0018https://doaj.org/article/8ce80e5396bf407db3f4c27e3e0adf142021-06-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021269?viewType=HTMLhttps://doaj.org/toc/1551-0018One of the most important factors limiting the performance of OFDM (Orthogonal Frequency Division Multiplexing) system is high PAPR (Peak to Average Power Ratio). Great efforts have been made in suppressing PAPR, but their implementation often requires pre-processing all input signals, leading to excessive calculation overhead. When the transmission speed is high, much more time will be taken to process the input signal with the traditional methods, which will reduce the performance of the system. In this background, this paper firstly presents an algorithm, called PRD, to identify the high PAPR sequence without IFFT (Inverse Fast Fourier Transform) operations, in which a CNN (Convolutional Neural Network) for identifying PAPR sequences is trained first before applying further PAPR reduction schemes. Experimental results show that the proposed algorithm can identify the high PAPR sequences with 92.3% accuracy and reduce PAPR with extremely low calculations.Yaoqi YangXianglin WeiRenhui XuLaixian PengAIMS Pressarticleconvolutional neural networkorthogonal frequency division multiplexingpeak to average power ratioBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 5309-5320 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	convolutional neural network orthogonal frequency division multiplexing peak to average power ratio Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	convolutional neural network orthogonal frequency division multiplexing peak to average power ratio Biotechnology TP248.13-248.65 Mathematics QA1-939 Yaoqi Yang Xianglin Wei Renhui Xu Laixian Peng When high PAPR reduction meets CNN: A PRD framework
description	One of the most important factors limiting the performance of OFDM (Orthogonal Frequency Division Multiplexing) system is high PAPR (Peak to Average Power Ratio). Great efforts have been made in suppressing PAPR, but their implementation often requires pre-processing all input signals, leading to excessive calculation overhead. When the transmission speed is high, much more time will be taken to process the input signal with the traditional methods, which will reduce the performance of the system. In this background, this paper firstly presents an algorithm, called PRD, to identify the high PAPR sequence without IFFT (Inverse Fast Fourier Transform) operations, in which a CNN (Convolutional Neural Network) for identifying PAPR sequences is trained first before applying further PAPR reduction schemes. Experimental results show that the proposed algorithm can identify the high PAPR sequences with 92.3% accuracy and reduce PAPR with extremely low calculations.
format	article
author	Yaoqi Yang Xianglin Wei Renhui Xu Laixian Peng
author_facet	Yaoqi Yang Xianglin Wei Renhui Xu Laixian Peng
author_sort	Yaoqi Yang
title	When high PAPR reduction meets CNN: A PRD framework
title_short	When high PAPR reduction meets CNN: A PRD framework
title_full	When high PAPR reduction meets CNN: A PRD framework
title_fullStr	When high PAPR reduction meets CNN: A PRD framework
title_full_unstemmed	When high PAPR reduction meets CNN: A PRD framework
title_sort	when high papr reduction meets cnn: a prd framework
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/8ce80e5396bf407db3f4c27e3e0adf14
work_keys_str_mv	AT yaoqiyang whenhighpaprreductionmeetscnnaprdframework AT xianglinwei whenhighpaprreductionmeetscnnaprdframework AT renhuixu whenhighpaprreductionmeetscnnaprdframework AT laixianpeng whenhighpaprreductionmeetscnnaprdframework
_version_	1718441400736940032

When high PAPR reduction meets CNN: A PRD framework

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