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Novel blind interleaver parameters estimation based on Hamming weight distribution of linear codes

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dc.contributor.authorWee, Seungwoo-
dc.contributor.authorChoi, Changryoul-
dc.contributor.authorJeong, Jechang-
dc.date.accessioned2022-07-06T12:09:33Z-
dc.date.available2022-07-06T12:09:33Z-
dc.date.created2021-11-22-
dc.date.issued2021-10-
dc.identifier.issn1051-2004-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140908-
dc.description.abstractInterleaving techniques are used to improve the probability of error correction in communication systems. In a non-cooperative context, interleaver parameters must be determined first so that the received data can be decoded into relevant information. This paper proposes blind interleaver parameter estimation method based on the Hamming weight distribution. Conventional methods based on rank distributions suffer from the high computational complexity of Gaussian elimination. In this study, we exploit the fact that the Hamming weight distributions of linear codes differ from those of random sequences owing to the linear dependence of linear codes. By exploiting this property, the proposed algorithm can estimate the interleaver period without a rank calculation. The values of the chi(2) test are used to estimate the interleaver period by constructing the Hamming weight distribution that differs the most from the binomial distribution. The experimental results indicate that the proposed algorithm outperforms conventional methods.-
dc.language영어-
dc.language.isoen-
dc.publisherACADEMIC PRESS INC ELSEVIER SCIENCE-
dc.titleNovel blind interleaver parameters estimation based on Hamming weight distribution of linear codes-
dc.typeArticle-
dc.contributor.affiliatedAuthorJeong, Jechang-
dc.identifier.doi10.1016/j.dsp.2021.103190-
dc.identifier.scopusid2-s2.0-85111799204-
dc.identifier.wosid000696725200016-
dc.identifier.bibliographicCitationDigital Signal Processing, v.117, pp.1 - 8-
dc.relation.isPartOfDigital Signal Processing-
dc.citation.titleDigital Signal Processing-
dc.citation.volume117-
dc.citation.startPage1-
dc.citation.endPage8-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.subject.keywordPlusBlind equalization-
dc.subject.keywordPlusCodes (symbols)-
dc.subject.keywordPlusError correction-
dc.subject.keywordPlusBinomial distribution-
dc.subject.keywordPlusConventional methods-
dc.subject.keywordPlusGaussian elimination-
dc.subject.keywordPlusInterleaving technique-
dc.subject.keywordPlusParameter estimation method-
dc.subject.keywordPlusParameters estimation-
dc.subject.keywordPlusProbability of errors-
dc.subject.keywordPlusRank distributions-
dc.subject.keywordPlusParameter estimation-
dc.subject.keywordAuthorInterleaver-
dc.subject.keywordAuthorBlind interleaver parameters estimation-
dc.subject.keywordAuthorChannel code-
dc.subject.keywordAuthorHamming weight distribution-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S1051200421002293?via%3Dihub-
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