Linear MMSE-optimal turbo equalization using context trees

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dc.citation.epage3055en_US
dc.citation.issueNumber12en_US
dc.citation.spage3041en_US
dc.citation.volumeNumber61en_US
dc.contributor.authorKim, K.en_US
dc.contributor.authorKalantarova, N.en_US
dc.contributor.authorKozat, S. S.en_US
dc.contributor.authorSinger, A. C.en_US
dc.date.accessioned2016-02-08T09:38:14Z
dc.date.available2016-02-08T09:38:14Z
dc.date.issued2013en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractFormulations of the turbo equalization approach to iterative equalization and decoding vary greatly when channel knowledge is either partially or completely unknown. Maximum aposteriori probability (MAP) and minimum mean-square error (MMSE) approaches leverage channel knowledge to make explicit use of soft information (priors over the transmitted data bits) in a manner that is distinctly nonlinear, appearing either in a trellis formulation (MAP) or inside an inverted matrix (MMSE). To date, nearly all adaptive turbo equalization methods either estimate the channel or use a direct adaptation equalizer in which estimates of the transmitted data are formed from an expressly linear function of the received data and soft information, with this latter formulation being most common. We study a class of direct adaptation turbo equalizers that are both adaptive and nonlinear functions of the soft information from the decoder. We introduce piecewise linear models based on context trees that can adaptively approximate the nonlinear dependence of the equalizer on the soft information such that it can choose both the partition regions as well as the locally linear equalizer coefficients in each region independently, with computational complexity that remains of the order of a traditional direct adaptive linear equalizer. This approach is guaranteed to asymptotically achieve the performance of the best piecewise linear equalizer, and we quantify the MSE performance of the resulting algorithm and the convergence of its MSE to that of the linear minimum MSE estimator as the depth of the context tree and the data length increase.en_US
dc.identifier.doi10.1109/TSP.2013.2256899en_US
dc.identifier.issn1053-587X
dc.identifier.urihttp://hdl.handle.net/11693/20934
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TSP.2013.2256899en_US
dc.source.titleIEEE Transactions on Signal Processingen_US
dc.subjectContext treeen_US
dc.subjectContext treeen_US
dc.subjectDecision feedbacken_US
dc.subjectNonlinear equalizationen_US
dc.subjectPiecewise linearen_US
dc.subjectTurbo equalizationsen_US
dc.subjectEqualizersen_US
dc.subjectEstimationen_US
dc.subjectIterative decodingen_US
dc.subjectKnowledge managementen_US
dc.subjectNonlinear feedbacken_US
dc.subjectPiecewise linear techniquesen_US
dc.subjectTrees (mathematics)en_US
dc.titleLinear MMSE-optimal turbo equalization using context treesen_US
dc.typeArticleen_US
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