DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Jinsol | ko |
dc.contributor.author | Ji, Dong Jin | ko |
dc.contributor.author | Cho, Dong-Ho | ko |
dc.date.accessioned | 2021-06-28T04:30:08Z | - |
dc.date.available | 2021-06-28T04:30:08Z | - |
dc.date.created | 2021-03-11 | - |
dc.date.issued | 2021-06 | - |
dc.identifier.citation | IEEE WIRELESS COMMUNICATIONS LETTERS, v.10, no.6, pp.1173 - 1177 | - |
dc.identifier.issn | 2162-2337 | - |
dc.identifier.uri | http://hdl.handle.net/10203/286271 | - |
dc.description.abstract | Physical-layer network coding (PNC) is an effective technique for enhancing wireless network throughput. Recently, it has been demonstrated that convolutional autoencoders effectively works in point-to-point communication systems, but their application to wireless relay networks is scarcely explored. In this letter, we propose a convolutional autoencoder for PNC in a two-way relay channel. The constellation mapping and demapping of symbols at each node are determined adaptively through a deep learning technique, such that the bit error rate performance is improved for high-order modulation. Simulation results verify the advantages of the proposed scheme over the conventional PNC scheme for various modulation types. IEEE | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | High-Order Modulation Based on Deep Neural Network for Physical-Layer Network Coding | - |
dc.type | Article | - |
dc.identifier.wosid | 000659548300007 | - |
dc.identifier.scopusid | 2-s2.0-85101731851 | - |
dc.type.rims | ART | - |
dc.citation.volume | 10 | - |
dc.citation.issue | 6 | - |
dc.citation.beginningpage | 1173 | - |
dc.citation.endingpage | 1177 | - |
dc.citation.publicationname | IEEE WIRELESS COMMUNICATIONS LETTERS | - |
dc.identifier.doi | 10.1109/LWC.2021.3060750 | - |
dc.contributor.localauthor | Cho, Dong-Ho | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Relays | - |
dc.subject.keywordAuthor | Modulation | - |
dc.subject.keywordAuthor | Convolutional codes | - |
dc.subject.keywordAuthor | Convolution | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Simulation | - |
dc.subject.keywordAuthor | Entropy | - |
dc.subject.keywordAuthor | Autoencoder | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | physical-layer network coding | - |
dc.subject.keywordAuthor | quadrature amplitude modulation | - |
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