Analysis of smoothing techniques for subspace estimation with application to channel estimation

Niels Lovmand Pedersen, Morten Lomholt Jakobsen, Christian Rom, Bernard Henri Fleury

Research output: Contribution to journalConference article in JournalResearchpeer-review

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Abstract

In this paper, we present an investigation on the impact of spatial smoothing and forward-backward averaging techniques for subspace-based channel estimation. The spatial smoothing technique requires the selection of a window size, which, if not chosen properly, leads to dramatic performance breakdown of subspace-based methods. We provide an explanation of the performance drop for certain window sizes and subsequently an understanding of a proper window size selection. In particular, we describe the behavior of the magnitude of the least signal eigenvalue as a function of the used window size. Through simulations we show that the magnitude of this eigenvalue is of particular importance for estimating the signal subspace and the entailing performance of the channel estimator.
Original languageEnglish
Book seriesI E E E International Conference on Communications
Pages (from-to)1-6
ISSN1550-3607
DOIs
Publication statusPublished - 2011
EventIEEE International Conference on Communications (ICC) - Kyoto, Japan
Duration: 5 Jun 20119 Jun 2011

Conference

ConferenceIEEE International Conference on Communications (ICC)
CountryJapan
CityKyoto
Period05/06/201109/06/2011

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Channel estimation

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title = "Analysis of smoothing techniques for subspace estimation with application to channel estimation",
abstract = "In this paper, we present an investigation on the impact of spatial smoothing and forward-backward averaging techniques for subspace-based channel estimation. The spatial smoothing technique requires the selection of a window size, which, if not chosen properly, leads to dramatic performance breakdown of subspace-based methods. We provide an explanation of the performance drop for certain window sizes and subsequently an understanding of a proper window size selection. In particular, we describe the behavior of the magnitude of the least signal eigenvalue as a function of the used window size. Through simulations we show that the magnitude of this eigenvalue is of particular importance for estimating the signal subspace and the entailing performance of the channel estimator.",
author = "Pedersen, {Niels Lovmand} and Jakobsen, {Morten Lomholt} and Christian Rom and Fleury, {Bernard Henri}",
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journal = "I E E E International Conference on Communications",
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Analysis of smoothing techniques for subspace estimation with application to channel estimation. / Pedersen, Niels Lovmand; Jakobsen, Morten Lomholt; Rom, Christian; Fleury, Bernard Henri.

In: I E E E International Conference on Communications, 2011, p. 1-6.

Research output: Contribution to journalConference article in JournalResearchpeer-review

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T1 - Analysis of smoothing techniques for subspace estimation with application to channel estimation

AU - Pedersen, Niels Lovmand

AU - Jakobsen, Morten Lomholt

AU - Rom, Christian

AU - Fleury, Bernard Henri

N1 - E-ISBN : 978-1-61284-231-8 Print ISBN: 978-1-61284-232-5

PY - 2011

Y1 - 2011

N2 - In this paper, we present an investigation on the impact of spatial smoothing and forward-backward averaging techniques for subspace-based channel estimation. The spatial smoothing technique requires the selection of a window size, which, if not chosen properly, leads to dramatic performance breakdown of subspace-based methods. We provide an explanation of the performance drop for certain window sizes and subsequently an understanding of a proper window size selection. In particular, we describe the behavior of the magnitude of the least signal eigenvalue as a function of the used window size. Through simulations we show that the magnitude of this eigenvalue is of particular importance for estimating the signal subspace and the entailing performance of the channel estimator.

AB - In this paper, we present an investigation on the impact of spatial smoothing and forward-backward averaging techniques for subspace-based channel estimation. The spatial smoothing technique requires the selection of a window size, which, if not chosen properly, leads to dramatic performance breakdown of subspace-based methods. We provide an explanation of the performance drop for certain window sizes and subsequently an understanding of a proper window size selection. In particular, we describe the behavior of the magnitude of the least signal eigenvalue as a function of the used window size. Through simulations we show that the magnitude of this eigenvalue is of particular importance for estimating the signal subspace and the entailing performance of the channel estimator.

U2 - 10.1109/icc.2011.5962560

DO - 10.1109/icc.2011.5962560

M3 - Conference article in Journal

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JO - I E E E International Conference on Communications

JF - I E E E International Conference on Communications

SN - 1550-3607

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