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Reseach Article

SIMO-OFDM Channel Estimation based on Nonlinear Complex LS-SVM

by Anis Charrada, Abdelaziz Samet
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 3
Year of Publication: 2012
Authors: Anis Charrada, Abdelaziz Samet
10.5120/5670-6823

Anis Charrada, Abdelaziz Samet . SIMO-OFDM Channel Estimation based on Nonlinear Complex LS-SVM. International Journal of Computer Applications. 42, 3 ( March 2012), 1-7. DOI=10.5120/5670-6823

@article{ 10.5120/5670-6823,
author = { Anis Charrada, Abdelaziz Samet },
title = { SIMO-OFDM Channel Estimation based on Nonlinear Complex LS-SVM },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 3 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number3/5670-6823/ },
doi = { 10.5120/5670-6823 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:30:30.311238+05:30
%A Anis Charrada
%A Abdelaziz Samet
%T SIMO-OFDM Channel Estimation based on Nonlinear Complex LS-SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 3
%P 1-7
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this contribution, we propose a robust highly selective nonlinear channel estimator for Single -Input Multiple-Output (SIMO) Orthogonal Frequency Division Multiplexing (OFDM) system using complex Least Squares Support Vector Machines (LS-SVM) and applied to Long Term Evolution (LTE) downlink under high mobility conditions . The new method uses the information provided by the pilot signals to estimate the total frequency response of the channel in two phases: learning phase and estimation phase. The estimation algorithm makes use of the reference signals to estimate the total frequency response of the highly selective multipath channel in the presence of non-Gaussian impulse noise interfering with pilot signals. Thus, the algorithm maps trained data into a high dimensional feature space and uses the Structural Risk Minimization (SRM) principle to carry out the regression estimation for the frequency response function of the highly selective channel. The simulations show the effectiveness of the proposed method which has good performance and high precision to track the variations of the fading channels compared to the conventional LS method and it is robust at high speed mobility.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Ls-svm Simo Mrc Impulse Noise Ofdm Lte