DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ji, Dong Jin | ko |
dc.contributor.author | Park, Jinsol | ko |
dc.contributor.author | Cho, Dong-Ho | ko |
dc.date.accessioned | 2019-11-18T06:20:34Z | - |
dc.date.available | 2019-11-18T06:20:34Z | - |
dc.date.created | 2019-09-03 | - |
dc.date.created | 2019-09-03 | - |
dc.date.issued | 2019-10 | - |
dc.identifier.citation | IEEE COMMUNICATIONS LETTERS, v.23, no.10, pp.1769 - 1772 | - |
dc.identifier.issn | 1089-7798 | - |
dc.identifier.uri | http://hdl.handle.net/10203/268452 | - |
dc.description.abstract | In this letter, we propose ConvAE, a new channel autoencoder structure. ConvAE uses residual blocks with convolutional layers. This configuration increases performance while decreasing computational complexity at run-time compared with conventional channel autoencoders. The simulations using both conventional and proposed autoencoders for a 2-by-2 multiple-input multiple-output (MIMO) system under Rayleigh and Nakagami-m fading show that the ConvAE is able to attain a lower bit error rate and higher achievable rate relative to the conventional channel autoencoder schemes. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | ConvAE: A New Channel Autoencoder Based on Convolutional Layers and Residual Connections | - |
dc.type | Article | - |
dc.identifier.wosid | 000492420300021 | - |
dc.identifier.scopusid | 2-s2.0-85077797971 | - |
dc.type.rims | ART | - |
dc.citation.volume | 23 | - |
dc.citation.issue | 10 | - |
dc.citation.beginningpage | 1769 | - |
dc.citation.endingpage | 1772 | - |
dc.citation.publicationname | IEEE COMMUNICATIONS LETTERS | - |
dc.identifier.doi | 10.1109/LCOMM.2019.2930287 | - |
dc.contributor.localauthor | Cho, Dong-Ho | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Receivers | - |
dc.subject.keywordAuthor | Transmitters | - |
dc.subject.keywordAuthor | MIMO communication | - |
dc.subject.keywordAuthor | Peak to average power ratio | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Convolutional codes | - |
dc.subject.keywordAuthor | Autoencoder | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | multiple input multiple output | - |
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