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Nonlinear turbo equalization using context trees

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dc.contributor.coauthorKim, Kyeongyeon
dc.contributor.coauthorSinger, Andrew C.
dc.contributor.departmentDepartment of Electrical and Electronics Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorKozat, Süleyman Serdar
dc.contributor.kuauthorKalantarova, Nargiz
dc.contributor.kuprofileFaculty Member
dc.contributor.kuprofilePhD Student
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.contributor.yokid177972
dc.contributor.yokidN/A
dc.date.accessioned2024-11-09T22:52:57Z
dc.date.issued2011
dc.description.abstractIn this paper, we study adaptive nonlinear turbo equalization to model the nonlinear dependency of a linear minimum mean square error (MMSE) equalizer on soft information from the decoder. To accomplish this, we introduce piecewise linear models based on context trees that can adaptively choose both the partition regions as well as the equalizer coefficients in each region independently, with the computational complexity of a single adaptive linear equalizer. This approach is guaranteed to asymptotically achieve the performance of the best piecewise linear equalizer that can choose both its regions as well as its filter parameters based on observing the whole data sequence in advance.
dc.description.indexedbyScopus
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.identifier.doi10.1109/ITA.2011.5743608
dc.identifier.isbn9781-4577-0361-4
dc.identifier.linkhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-79955781810anddoi=10.1109%2fITA.2011.5743608andpartnerID=40andmd5=4eed77d6e7265646d7376ff4452a9756
dc.identifier.quartileN/A
dc.identifier.scopus2-s2.0-79955781810
dc.identifier.urihttp://dx.doi.org/10.1109/ITA.2011.5743608
dc.identifier.urihttps://hdl.handle.net/20.500.14288/7112
dc.keywordsTurbo equalization
dc.keywordsContext trees
dc.keywordsData sequences
dc.keywordsDecision feedback
dc.keywordsEqualizer coefficients
dc.keywordsFilter parameter
dc.keywordsLinear equalizer
dc.keywordsLinear minimum mean square errors
dc.keywordsNonlinear dependencies
dc.keywordsNonlinear equalization
dc.keywordsPiecewise linear
dc.keywordsPiecewise linear models
dc.keywordsSoft information
dc.keywordsTurbo equalizations
dc.keywordsComputational complexity
dc.keywordsInformation theory
dc.keywordsPiecewise linear techniques
dc.keywordsPlant extracts
dc.keywordsSignal receivers
dc.keywordsDecision feedback equalizers
dc.languageEnglish
dc.publisherIEEE
dc.source2011 Information Theory and Applications Workshop, ITA 2011 - Conference Proceedings
dc.subjectEngineering
dc.subjectElectrical and electronics engineering
dc.titleNonlinear turbo equalization using context trees
dc.typeConference proceeding
dspace.entity.typePublication
local.contributor.authorid0000-0002-6488-3848
local.contributor.authoridN/A
local.contributor.kuauthorKozat, Süleyman Serdar
local.contributor.kuauthorKalantarova, Nargiz
local.publication.orgunit1College of Engineering
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit2Department of Electrical and Electronics Engineering
local.publication.orgunit2Graduate School of Sciences and Engineering
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