Maximum Weight Basis Decoding of Convolutional Codes

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dc.citation.conferenceDate2000en_US
dc.citation.conferenceNameIEEE Global Telecommunications Conference (Globecom)en_US
dc.citation.firstpage835
dc.citation.lastpage841
dc.citation.locationSan Francisco, CAen_US
dc.contributor.authorDas, Suman
dc.contributor.authorErkip, Elza
dc.contributor.authorCavallaro, Joseph R.
dc.contributor.authorAazhang, Behnaam
dc.contributor.orgCenter for Multimedia Communicationen_US
dc.date.accessioned2012-06-29T18:42:24Z
dc.date.available2012-06-29T18:42:24Z
dc.date.issued2002-11-01eng
dc.description.abstractIn this paper we describe a new suboptimal decoding technique for linear codes based on the calculation of maximum weight basis of the code. The idea is based on estimating the maximum number locations in a codeword which have least probability of estimation error without violating the codeword structure. In this paper we discuss the details of the algorithm for a convolutional code. The error correcting capability of the convolutional code increases with the constraint length of the code. Unfortunately the decoding complexity of Viterbi algorithm grows exponentially with the constraint length. We also augment the maximal weight basis algorithm by incorporating the ideas of list decoding technique. The complexity of the algorithm grows only quadratically with the constraint length and the performance of the algorithm is comparable to the optimal Viterbi decoding method.en_US
dc.identifier.citationS. Das, E. Erkip, J. R. Cavallaro and B. Aazhang, "Maximum Weight Basis Decoding of Convolutional Codes," 2002.*
dc.identifier.doihttp://dx.doi.org/10.1109/GLOCOM.2000.891256en_US
dc.identifier.otherhttp://scholar.google.com/scholar?cluster=13785228708626545187&hl=en&as_sdt=0,44
dc.identifier.urihttps://hdl.handle.net/1911/64354
dc.language.isoengen
dc.publisherIEEEen_US
dc.subjectViterbi algorithmen_US
dc.subjectConvolutional codeen_US
dc.subjectDecoding techniqueen_US
dc.subjectMaximum weighten_US
dc.titleMaximum Weight Basis Decoding of Convolutional Codesen_US
dc.typeConference paperen_US
dc.type.dcmiTexten
dc.type.dcmiTexten_US
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