Cited 0 time in
Novel blind interleaver parameters estimation based on Hamming weight distribution of linear codes
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Wee, Seungwoo | - |
| dc.contributor.author | Choi, Changryoul | - |
| dc.contributor.author | Jeong, Jechang | - |
| dc.date.accessioned | 2022-07-06T12:09:33Z | - |
| dc.date.available | 2022-07-06T12:09:33Z | - |
| dc.date.created | 2021-11-22 | - |
| dc.date.issued | 2021-10 | - |
| dc.identifier.issn | 1051-2004 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140908 | - |
| dc.description.abstract | Interleaving 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.language | 영어 | - |
| dc.language.iso | en | - |
| dc.publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE | - |
| dc.title | Novel blind interleaver parameters estimation based on Hamming weight distribution of linear codes | - |
| dc.type | Article | - |
| dc.contributor.affiliatedAuthor | Jeong, Jechang | - |
| dc.identifier.doi | 10.1016/j.dsp.2021.103190 | - |
| dc.identifier.scopusid | 2-s2.0-85111799204 | - |
| dc.identifier.wosid | 000696725200016 | - |
| dc.identifier.bibliographicCitation | Digital Signal Processing, v.117, pp.1 - 8 | - |
| dc.relation.isPartOf | Digital Signal Processing | - |
| dc.citation.title | Digital Signal Processing | - |
| dc.citation.volume | 117 | - |
| dc.citation.startPage | 1 | - |
| dc.citation.endPage | 8 | - |
| dc.type.rims | ART | - |
| dc.type.docType | Article | - |
| dc.description.journalClass | 1 | - |
| dc.description.isOpenAccess | Y | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Engineering | - |
| dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
| dc.subject.keywordPlus | Blind equalization | - |
| dc.subject.keywordPlus | Codes (symbols) | - |
| dc.subject.keywordPlus | Error correction | - |
| dc.subject.keywordPlus | Binomial distribution | - |
| dc.subject.keywordPlus | Conventional methods | - |
| dc.subject.keywordPlus | Gaussian elimination | - |
| dc.subject.keywordPlus | Interleaving technique | - |
| dc.subject.keywordPlus | Parameter estimation method | - |
| dc.subject.keywordPlus | Parameters estimation | - |
| dc.subject.keywordPlus | Probability of errors | - |
| dc.subject.keywordPlus | Rank distributions | - |
| dc.subject.keywordPlus | Parameter estimation | - |
| dc.subject.keywordAuthor | Interleaver | - |
| dc.subject.keywordAuthor | Blind interleaver parameters estimation | - |
| dc.subject.keywordAuthor | Channel code | - |
| dc.subject.keywordAuthor | Hamming weight distribution | - |
| dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S1051200421002293?via%3Dihub | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1366
COPYRIGHT © 2024 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.
