Performance Improvement of Neural Network Based RLS Channel Estimators in MIMO-OFDM Systems

The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division...

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Autor principal:	Alaa Abdulameer Hassan
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2011
Materias:	MIMO-OFDM RLS NN BER SNR channel estimation. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/276e47a09e8a42a9976269efc6e4ecc4
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id	oai:doaj.org-article:276e47a09e8a42a9976269efc6e4ecc4
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spelling	oai:doaj.org-article:276e47a09e8a42a9976269efc6e4ecc42021-12-02T02:42:17ZPerformance Improvement of Neural Network Based RLS Channel Estimators in MIMO-OFDM Systems1818-1171https://doaj.org/article/276e47a09e8a42a9976269efc6e4ecc42011-01-01T00:00:00Zhttp://www.iasj.net/iasj?func=fulltext&aId=2324https://doaj.org/toc/1818-1171The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust at high speed mobility.Alaa Abdulameer HassanAl-Khwarizmi College of Engineering – University of BaghdadarticleMIMO-OFDMRLSNNBERSNRchannelestimation.Chemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 7, Iss 2, Pp 36-46 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	MIMO-OFDM RLS NN BER SNR channel estimation. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	MIMO-OFDM RLS NN BER SNR channel estimation. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Alaa Abdulameer Hassan Performance Improvement of Neural Network Based RLS Channel Estimators in MIMO-OFDM Systems
description	The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust at high speed mobility.
format	article
author	Alaa Abdulameer Hassan
author_facet	Alaa Abdulameer Hassan
author_sort	Alaa Abdulameer Hassan
title	Performance Improvement of Neural Network Based RLS Channel Estimators in MIMO-OFDM Systems
title_short	Performance Improvement of Neural Network Based RLS Channel Estimators in MIMO-OFDM Systems
title_full	Performance Improvement of Neural Network Based RLS Channel Estimators in MIMO-OFDM Systems
title_fullStr	Performance Improvement of Neural Network Based RLS Channel Estimators in MIMO-OFDM Systems
title_full_unstemmed	Performance Improvement of Neural Network Based RLS Channel Estimators in MIMO-OFDM Systems
title_sort	performance improvement of neural network based rls channel estimators in mimo-ofdm systems
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2011
url	https://doaj.org/article/276e47a09e8a42a9976269efc6e4ecc4
work_keys_str_mv	AT alaaabdulameerhassan performanceimprovementofneuralnetworkbasedrlschannelestimatorsinmimoofdmsystems
_version_	1718402207094669312

Performance Improvement of Neural Network Based RLS Channel Estimators in MIMO-OFDM Systems

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