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Classification of Single- and Multi-carrier Signals Using CNN Based Deep Learning

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dc.contributor.authorAn, Sungbae-
dc.contributor.authorJang, Mingyu-
dc.contributor.authorYoon, Dongweon-
dc.date.accessioned2022-07-06T10:38:15Z-
dc.date.available2022-07-06T10:38:15Z-
dc.date.created2022-03-07-
dc.date.issued2022-01-
dc.identifier.issn0000-0000-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/139792-
dc.description.abstractIn a non-cooperative context, to recover data from the received signal, the receiver must estimate the communication parameters used in the transmitter. In this paper, we propose an algorithm for classifying single-carrier and multi-carrier signals by using convolutional neural network based deep learning and analyze classification performance. Simulation results show that the proposed algorithm outperforms the conventional methods in an additive white Gaussian noise channel and Rician fading channel. © 2021 IEEE.-
dc.language영어-
dc.language.isoen-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleClassification of Single- and Multi-carrier Signals Using CNN Based Deep Learning-
dc.typeArticle-
dc.contributor.affiliatedAuthorYoon, Dongweon-
dc.identifier.doi10.1109/IC-NIDC54101.2021.9660515-
dc.identifier.scopusid2-s2.0-85124794883-
dc.identifier.bibliographicCitationProceedings of 2021 7th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2021, pp.196 - 199-
dc.relation.isPartOfProceedings of 2021 7th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2021-
dc.citation.titleProceedings of 2021 7th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2021-
dc.citation.startPage196-
dc.citation.endPage199-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusConvolutional neural networks-
dc.subject.keywordPlusCooperative communication-
dc.subject.keywordPlusDecoding-
dc.subject.keywordPlusDeep learning-
dc.subject.keywordPlusFading channels-
dc.subject.keywordPlusGaussian noise (electronic)-
dc.subject.keywordPlusOrthogonal frequency division multiplexing-
dc.subject.keywordPlusSignal receivers-
dc.subject.keywordPlusTurbo codes-
dc.subject.keywordPlusWhite noise-
dc.subject.keywordPlusClassification performance-
dc.subject.keywordPlusCommunication parameters-
dc.subject.keywordPlusConvolutional neural network-
dc.subject.keywordPlusDeep learning-
dc.subject.keywordPlusMulticarrier signal-
dc.subject.keywordPlusNetwork-based-
dc.subject.keywordPlusNon-cooperative-
dc.subject.keywordPlusOrthogonal frequency-division multiplexing-
dc.subject.keywordPlusReceived signals-
dc.subject.keywordPlusSingle carrier-
dc.subject.keywordPlusConvolution-
dc.subject.keywordAuthorclassification-
dc.subject.keywordAuthorconvolutional neural network-
dc.subject.keywordAuthordeep learning-
dc.subject.keywordAuthororthogonal frequency division multiplexing-
dc.subject.keywordAuthorsingle-carrier-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/9660515-
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