DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Minhoe | ko |
dc.contributor.author | Kim, Nam-I | ko |
dc.contributor.author | Lee, Woongsup | ko |
dc.contributor.author | Cho, Dong-Ho | ko |
dc.date.accessioned | 2018-05-24T01:32:36Z | - |
dc.date.available | 2018-05-24T01:32:36Z | - |
dc.date.created | 2018-04-30 | - |
dc.date.created | 2018-04-30 | - |
dc.date.created | 2018-04-30 | - |
dc.date.created | 2018-04-30 | - |
dc.date.issued | 2018-04 | - |
dc.identifier.citation | IEEE COMMUNICATIONS LETTERS, v.22, no.4, pp.720 - 723 | - |
dc.identifier.issn | 1089-7798 | - |
dc.identifier.uri | http://hdl.handle.net/10203/242181 | - |
dc.description.abstract | Sparse code multiple access (SCMA) is a promising code-based non-orthogonal multiple-access technique that can provide improved spectral efficiency and massive connectivity meeting the requirements of 5G wireless communication systems. We propose a deep learning-aided SCMA (D-SCMA) in which the codebook that minimizes the bit error rate (BER) is adaptively constructed, and a decoding strategy is learned using a deep neural network-based encoder and decoder. One benefit of D-SCMA is that the construction of an efficient codebook can be achieved in an automated manner, which is generally difficult due to the non-orthogonality and multi-dimensional traits of SCMA. We use simulations to show that our proposed scheme provides a lower BER with a smaller computation time than conventional schemes. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Deep Learning-Aided SCMA | - |
dc.type | Article | - |
dc.identifier.wosid | 000429676700016 | - |
dc.identifier.scopusid | 2-s2.0-85041219177 | - |
dc.type.rims | ART | - |
dc.citation.volume | 22 | - |
dc.citation.issue | 4 | - |
dc.citation.beginningpage | 720 | - |
dc.citation.endingpage | 723 | - |
dc.citation.publicationname | IEEE COMMUNICATIONS LETTERS | - |
dc.identifier.doi | 10.1109/LCOMM.2018.2792019 | - |
dc.contributor.localauthor | Cho, Dong-Ho | - |
dc.contributor.nonIdAuthor | Lee, Woongsup | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Sparse code multiple access (SCMA) | - |
dc.subject.keywordAuthor | deep neural network (DNN) | - |
dc.subject.keywordAuthor | autoencoder | - |
dc.subject.keywordAuthor | deep learning | - |
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