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

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

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URIhttp://purl.tuc.gr/dl/dias/626B2C9E-2C7D-4CD6-AE25-7FD1E551DBE4-
Identifierhttps://doi.org/10.1109/78.765134-
Languageen-
Extent10 pagesen
TitleRobustness of least-squares and subspace methods for blind channel identification/equalization with respect to effective channel undermodeling/overmodelingen
CreatorLiavas Athanasiosen
CreatorΛιαβας Αθανασιοςel
CreatorDelmas, Jacquesen
CreatorRegalia, Phillip A., 1962-en
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryThe least-squares and the subspace methods are two 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 practically inevitable cases in which the channel possesses long tails of “small” impulse response terms. We study the performance of the mth-order least-squares and subspace methods using a perturbation analysis approach. We partition the true impulse response into the mth-order significant part and the tails. We show that the mth-order least-squares or subspace methods estimate an impulse response that is “close” to the mth-order significant part. The closeness depends on the diversity of the mth-order significant part and the size of the tails. Furthermore, we show that if we try to model not only the “large” terms but also some “small” ones, then the quality of our estimate may degrade dramatically; thus, we should avoid modeling “small” terms. Finally, we present simulations using measured microwave radio channels, highlighting potential advantages and shortcomings of the least-squares and subspace methodsen
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-08-
Date of Publication1999-
SubjectMethod of least squaresen
SubjectSquares, Leasten
Subjectleast squaresen
Subjectmethod of least squaresen
Subjectsquares leasten
Bibliographic CitationA. P. Liavas, P. A. Regalia ,J-P. Delmas,“Robustness of the least squares and subspace methods for blind channel identification/equalization with respect to effective channel undermodeling/overmodeling,” IEEE Trans. Signal Proc., vol. 47, no. 6, pp. 1636–1645, Ju. 1999.doi : 10.1109/78.765134en

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