A cost function level analysis of Auto-correlation Minimization based blind adaptive channel shorteners

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dc.contributor.author Ciira, Wa Maina
dc.contributor.author Walsh, John MacLaren
dc.date.accessioned 2019-03-11T09:10:15Z
dc.date.available 2019-03-11T09:10:15Z
dc.date.issued 2008
dc.identifier.issn 1558-7916
dc.identifier.uri http://41.89.227.156:8080/xmlui/handle/123456789/839
dc.description.abstract This paper considers a cost function level analysis of the Sum-squared Autocorrelation Minimization (SAM) channel shortening algorithm. We point out that the actual cost the blind adaptive stochastic gradient descent algorithm is minimizing is only indirectly related to the sum squared autocorrelation. We study the asymptotic regimes under which the actual cost yields a reliable surrogate for the sum squared autocorrelation. We investigate the relationship between the minima of the actual cost and sum squared autocorrelation. We also study the upper bound of the approximate cost as a function of the window size used in the approximate autocorrelation calculation en_US
dc.language.iso en en_US
dc.publisher IEEE TRANSACTIONS ON INFORMATION THEORY en_US
dc.title A cost function level analysis of Auto-correlation Minimization based blind adaptive channel shorteners en_US
dc.type Article en_US


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