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Blind Interleaver Parameter Estimation From Scant Data

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dc.contributor.authorJang, Mingyu-
dc.contributor.authorKim, Geunbae-
dc.contributor.authorKim, Dongyeong-
dc.contributor.authorYoon, Dongweon-
dc.date.accessioned2021-08-02T08:28:11Z-
dc.date.available2021-08-02T08:28:11Z-
dc.date.created2021-05-12-
dc.date.issued2020-12-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/8127-
dc.description.abstractA method for blind estimation of interleaver parameter was recently reported which made additional data from a limited amount of received data. However, the process of making additional data creates undesirable linearity which degrades estimation performance. Promise for improved estimation therefore lies in enhancing blind estimation of interleaver parameter without making additional data. In this paper, we propose an improved method to blindly estimate interleaver parameter under the condition of scant data. We first generate a matrix by using the received data. From this matrix we then make square submatrices and obtain their rank deficiency distribution. Finally, we estimate the interleaver parameter by comparing the rank deficiency distribution of the square submatrices and that of random binary matrices. Through computer simulations, we validate the proposed method in terms of detection probability and the number of false alarms. Simulation results show that the proposed method works better than the conventional method given scarce received data.-
dc.language영어-
dc.language.isoen-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleBlind Interleaver Parameter Estimation From Scant Data-
dc.typeArticle-
dc.contributor.affiliatedAuthorYoon, Dongweon-
dc.identifier.doi10.1109/ACCESS.2020.3041795-
dc.identifier.scopusid2-s2.0-85097398267-
dc.identifier.wosid000598238000001-
dc.identifier.bibliographicCitationIEEE ACCESS, v.8, pp.217282 - 217289-
dc.relation.isPartOfIEEE ACCESS-
dc.citation.titleIEEE ACCESS-
dc.citation.volume8-
dc.citation.startPage217282-
dc.citation.endPage217289-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaTelecommunications-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryTelecommunications-
dc.subject.keywordPlusERROR-CORRECTING CODES-
dc.subject.keywordPlusMODULATION CLASSIFICATION-
dc.subject.keywordPlusESTIMATION ALGORITHM-
dc.subject.keywordPlusSEQUENCE ESTIMATION-
dc.subject.keywordPlusCYCLIC CODES-
dc.subject.keywordPlusRECONSTRUCTION-
dc.subject.keywordPlusIDENTIFICATION-
dc.subject.keywordAuthorEstimation-
dc.subject.keywordAuthorLinearity-
dc.subject.keywordAuthorChannel estimation-
dc.subject.keywordAuthorReceivers-
dc.subject.keywordAuthorData models-
dc.subject.keywordAuthorConvolutional codes-
dc.subject.keywordAuthorConvolution-
dc.subject.keywordAuthorBlind detection-
dc.subject.keywordAuthornon-cooperative context-
dc.subject.keywordAuthorremote sensing-
dc.subject.keywordAuthorspectrum surveillance-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/9274324-
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