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dc.contributor.authorBerger, Christian R.  Concept link
dc.contributor.authorZhou, Shengli  Concept link
dc.contributor.authorPreisig, James C.  Concept link
dc.contributor.authorWillett, Peter  Concept link
dc.date.accessioned2010-03-04T15:51:58Z
dc.date.available2010-03-04T15:51:58Z
dc.date.issued2009-10-29
dc.identifier.citationIEEE Transactions on Signal Processing 58 (2010): 1708-1721en_US
dc.identifier.urihttps://hdl.handle.net/1912/3190
dc.descriptionAuthor Posting. © IEEE, 2009. This article is posted here by permission of IEEE for personal use, not for redistribution. The definitive version was published in IEEE Transactions on Signal Processing 58 (2010): 1708-1721, doi:10.1109/TSP.2009.2038424.en_US
dc.description.abstractIn this paper, we investigate various channel estimators that exploit channel sparsity in the time and/or Doppler domain for a multicarrier underwater acoustic system. We use a path-based channel model, where the channel is described by a limited number of paths, each characterized by a delay, Doppler scale, and attenuation factor, and derive the exact inter-carrierinterference (ICI) pattern. For channels that have limited Doppler spread we show that subspace algorithms from the array processing literature, namely Root-MUSIC and ESPRIT, can be applied for channel estimation. For channels with Doppler spread, we adopt a compressed sensing approach, in form of Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP) algorithms, and utilize overcomplete dictionaries with an increased path delay resolution. Numerical simulation and experimental data of an OFDM block-by-block receiver are used to evaluate the proposed algorithms in comparison to the conventional least-squares (LS) channel estimator.We observe that subspace methods can tolerate small to moderate Doppler effects, and outperform the LS approach when the channel is indeed sparse. On the other hand, compressed sensing algorithms uniformly outperform the LS and subspace methods. Coupled with a channel equalizer mitigating ICI, the compressed sensing algorithms can effectively handle channels with significant Doppler spread.en_US
dc.description.sponsorshipC. Berger, S. Zhou, and P. Willett are supported by ONR grants N00014-09-10613, N00014-07-1-0805, and N00014-09-1-0704.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.urihttps://doi.org/10.1109/TSP.2009.2038424
dc.subjectBasis Pursuiten_US
dc.subjectDoppler spreaden_US
dc.subjectESPRITen_US
dc.subjectICIen_US
dc.subjectMUSICen_US
dc.subjectOFDMen_US
dc.subjectOrthogonal Matching Pursuiten_US
dc.titleSparse channel estimation for multicarrier underwater acoustic communication : from subspace methods to compressed sensingen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TSP.2009.2038424


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