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Robustness of least-squares and subspace methods for blind channel identification/equalization algorithms with respect to channel undermodeling

Liavas Athanasios, Delmas, Fernand, Regalia, Phillip A., 1962-

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URIhttp://purl.tuc.gr/dl/dias/A35861DE-3D72-4F98-AB7C-08B54B679544-
Identifierhttp://faculty.cua.edu/regalia/regalia-perso_files/eusipco-98a.pdf-
Languageen-
Extent4 pagesen
TitleRobustness of least-squares and subspace methods for blind channel identification/equalization algorithms with respect to channel undermodelingen
CreatorLiavas Athanasiosen
CreatorΛιαβας Αθανασιοςel
CreatorDelmas, Fernanden
CreatorRegalia, Phillip A., 1962-en
Content SummaryThe least-squares and the subspace methods are well known approaches for blind channel identification/equalization. When the order of the channel is known, the algorithms are able to identify the channel, under the so-called length and zero conditions. Furthermore, in the noiseless case, the channel can be perfectly equalized. Less is known about the performance of these algorithms in the cases in which the channel order is underestimated. We partition the true impulse response into the significant part and the tails. We show that the m-th order least-squares or subspace methods estimate an impulse response which is “close” to the m-th order significant part of the true impulse response. The closeness depends on the diversity of the m-th order significant part and the size of the “unmodeled” parten
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-09-
Date of Publication1998-
Bibliographic CitationA. P. Liavas, P. A. Regalia and J-P. Delmas.(1998).Robustness of least-squares and subspace methods for blind channel identification/equalization algorithms with respect to channel undermodeling.Presented at European Signal Processing Conference.[online].Available:http://faculty.cua.edu/regalia/regalia-perso_files/eusipco-98a.pdfen

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