Frequency tracking by method of least squares combined with channel estimation for OFDM over mobile wireless channels

doi:10.1186/1687-1499-2012-192

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Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/83000

Title:	Frequency tracking by method of least squares combined with channel estimation for OFDM over mobile wireless channels
Authors:	Yen, Rainfield Y;Liu, Hong-Yu;Tsai, Chia-Sheng
Contributors:	淡江大學電機工程學系
Keywords:	orthogonal frequency division multiplexing (OFDM);least squares (LS) estimation;maximum-likelihood (ML) estimation;carrier frequency synchronization;channel estimation
Date:	2012-06
Issue Date:	2013-03-13 03:33:34 (UTC+8)
Publisher:	Heidelberg: SpringerOpen
Abstract:	To track frequency offset and time-varying channel in orthogonal frequency division multiplexing (OFDM) systems over mobile wireless channels, a common technique is, based on one OFDM training block sample, to apply the maximum-likelihood (ML) algorithm to perform joint frequency tracking and channel estimation employing some adaptive iteration processes. The major drawback of such joint estimation techniques is the local extrema problem arising from the highly nonlinear nature of the log-likelihood function. This makes the joint estimation process very difficult and complicated, and many a time the results are not very satisfactory if the algorithm is not well designed. In this study, rather than using the ML algorithm, we shall apply the method of least squares (LS) for frequency tracking utilizing repeated OFDM training blocks. As will be seen, by using such an LS approach, the frequency offset estimation requires no channel knowledge. The channel state can be estimated separately after the LS frequency offset correction. This not only circumvents the local extrema complication, but also obviates the need for the lengthy adaptive iteration process of joint estimation thus greatly simplifies the entire estimation process. Most importantly, our technique can achieve excellent estimation performance as compared to the usual ML algorithms.
Relation:	EURASIP Journal on Wireless Communications and Networking 2012:192, 13pages
DOI:	10.1186/1687-1499-2012-192
Appears in Collections:	[Graduate Institute & Department of Electrical Engineering] Journal Article

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