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dc.contributor.authorPladdy, Christopher
dc.contributor.authorNerayanuru, Sreenivasa M.
dc.contributor.authorFimoff, Mark
dc.contributor.authorÖzen, Serdar
dc.contributor.authorZoltowski, Michael
dc.date.accessioned2016-06-01T10:26:21Z
dc.date.available2016-06-01T10:26:21Z
dc.date.issued2004
dc.identifier.citationPladdy, C., Nerayanuru, S. M., Fimoff, M., Özen, S., and Zoltowski, M. (2004). Taylor series approximation for low complexity semi-blind best linear unbiased channel estimates for the general linear model with applications to DTV. Conference Record - Asilomar Conference on Signals, Systems and Computers, 2, 2208-2212. doi:10.1109/ACSSC.2004.1399559en_US
dc.identifier.issn1058-6393
dc.identifier.urihttp://doi.org/10.1109/ACSSC.2004.1399559
dc.identifier.urihttp://hdl.handle.net/11147/4701
dc.descriptionConference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers; Pacific Grove, CA; United States; 7 November 2004 through 10 November 2004en_US
dc.description.abstractWe present a low complexity approximate method for semi-blind best linear unbiased estimation (BLUE) of a channel impulse response vector (CIR) for a communication system which utilizes a periodically transmitted training sequence, within a continuous stream of information symbols. The algorithm achieves slightly degraded results at a much lower complexity than directly computing the BLUE CIR estimate. In addition, the inverse matrix required to invert the weighted normal equations to solve the general least squares problem may be precomputed and stored at the receiver. The BLUE estimate is obtained by solving the general linear model, y = Ah + w + n, for h, where w is correlated noise and the vector n is an AWGN process, which is uncorrelated with w. The solution is given by the Gauss-Markoff Theorem as h = (A TC(h) -1A) -1 A TC(h) -1y. In the present work we propose a Taylor series approximation for the function F(h) = (A TC(h) -1A) -1 A TC(h) -1y where, F: R L → R L for each fixed vector of received symbols, y, and each fixed convolution matrix of known transmitted training symbols, A. We describe the full Taylor formula for this function, F (h) = F (h id + ∑ |α|≥1(h - h id) α (∂/∂h) α F(h id) and describe algorithms using, respectively, first, second and third order approximations. The algorithms give better performance than correlation channel estimates and previous approximations used at only a slight increase in complexity. The linearization procedure used is similar to that used in the linearization to obtain the extended Kalman filter, and the higher order approximations are similar to those used in obtaining higher order Kalman filter approximations,en_US
dc.language.isoengen_US
dc.publisherIEEE Computer Societyen_US
dc.relation.isversionof10.1109/ACSSC.2004.1399559en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBest linear unbiased estimationen_US
dc.subjectChannel capacityen_US
dc.subjectGauss Markoff Theoremen_US
dc.subjectLinearizationen_US
dc.subjectGeneral linear modelen_US
dc.titleTaylor series approximation for low complexity semi-blind best linear unbiased channel estimates for the general linear model with applications to DTVen_US
dc.typeconferenceObjecten_US
dc.contributor.iztechauthorÖzen, Serdar
dc.relation.journalConference Record - Asilomar Conference on Signals, Systems and Computersen_US
dc.contributor.departmentIzmir Institute of Technology. Electronics and Communication Engineeringen_US
dc.identifier.volume2en_US
dc.identifier.startpage2208en_US
dc.identifier.endpage2212en_US
dc.identifier.scopusSCOPUS:2-s2.0-21644469527
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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