Channel estimation and symbol detection for block transmission using data-dependent superimposed training

Abstract

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. The superimposed training consists of the sum of a known sequence and a data-dependent sequence that is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to significantly outperform existing methods based on superimposed training (ST).

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Item Type:	Article
Authors/Creators:	Ghogho, M. McLernon, D. Alameda-Hernandez, E. Swami, A.
Copyright, Publisher and Additional Information:	Copyright © 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Dates:	Published: March 2005
Institution:	The University of Leeds
Academic Units:	The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds)
Depositing User:	Sherpa Assistant
Date Deposited:	30 Sep 2005
Last Modified:	25 Oct 2016 14:16
Published Version:	http://dx.doi.org/10.1109/LSP.2004.842283
Status:	Published
Refereed:	Yes
Identification Number:	10.1109/LSP.2004.842283
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:692

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Channel estimation and symbol detection for block transmission using data-dependent superimposed training

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