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16QAM Turbo Decoding with Equal Probability Quantization Value

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dc.contributor.authorNam, Dae-ho-
dc.contributor.authorHongik University, Department of Electronic and Electrical Engineering-
dc.contributor.authorLee, Ho Kyeong-
dc.contributor.authorHongik University, Department of Electronic and Electrical Engineering-
dc.date.available2021-03-17T07:47:25Z-
dc.date.created2021-02-26-
dc.date.issued2020-
dc.identifier.issn0000-0000-
dc.identifier.urihttps://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/12486-
dc.description.abstractIn this paper, we propose the Log-MAP decoding algorithm of the 16QAM turbo code considering the changed channel through the quantization process, assuming that the probability distribution of the received signal is known. For the performance analysis of the proposed decoding algorithm, we analyzed the performance of 16QAM turbo code using uniform quantizer which is generally used and equal probability quantizer considering quantized channel to maximize the entropy of received signal probability distribution. As a result, when the quantization bit is 4 bits, the equal probability quantizer considering the quantized channel has a code gain of about 0.13dB at BER 10-5 than the uniform quantizer. © 2020 IEEE.-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.title16QAM Turbo Decoding with Equal Probability Quantization Value-
dc.typeArticle-
dc.contributor.affiliatedAuthorLee, Ho Kyeong-
dc.identifier.doi10.1109/ICAIIC48513.2020.9065266-
dc.identifier.scopusid2-s2.0-85084053142-
dc.identifier.bibliographicCitation2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, pp.575 - 577-
dc.relation.isPartOf2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020-
dc.citation.title2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020-
dc.citation.startPage575-
dc.citation.endPage577-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordAuthorChannel Code-
dc.subject.keywordAuthorLog-MAP Algorithm-
dc.subject.keywordAuthorQAM-
dc.subject.keywordAuthorQuantization-
dc.subject.keywordAuthorTurbo Code-
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