DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ji, Dong Jin | ko |
dc.contributor.author | Cho, Dong-Ho | ko |
dc.date.accessioned | 2020-10-13T01:55:04Z | - |
dc.date.available | 2020-10-13T01:55:04Z | - |
dc.date.created | 2020-07-02 | - |
dc.date.created | 2020-07-02 | - |
dc.date.issued | 2020-09 | - |
dc.identifier.citation | IEEE COMMUNICATIONS LETTERS, v.24, no.9, pp.1976 - 1980 | - |
dc.identifier.issn | 1089-7798 | - |
dc.identifier.uri | http://hdl.handle.net/10203/276518 | - |
dc.description.abstract | Recent advancements in machine learning for communications show that channel autoencoders could revolutionize conventional communication systems through end-to-end optimization. For channel autoencoders to reliably transmit over the air, a scheme to enable adaptive use of resources is needed. Thus, we propose ConvAE-Advanced, an improved channel autoencoder structure that can adaptively transmit across multiple timeslots. ConvAE-Advanced utilizes an unexploited input dimension in ConvAE by the use of the resource-aware residual block and whole resource power normalization. This enabled ConvAE-Advanced to adaptively transmit information according to channel conditions. Simulations for a 2-by-2 multiple-input multiple-output system under the WINNER2 A1 scenario shows that ConvAE-Advanced outperforms ConvAE across all SNR ranges. Most importantly, ConvAE-Advanced can achieve a better BER and achievable rate performance without additional wireless resource usage compared to ConvAE. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | ConvAE-Advanced: Adaptive Transmission Across Multiple Timeslots For Error Resilient Operation | - |
dc.type | Article | - |
dc.identifier.wosid | 000568659300027 | - |
dc.identifier.scopusid | 2-s2.0-85091183768 | - |
dc.type.rims | ART | - |
dc.citation.volume | 24 | - |
dc.citation.issue | 9 | - |
dc.citation.beginningpage | 1976 | - |
dc.citation.endingpage | 1980 | - |
dc.citation.publicationname | IEEE COMMUNICATIONS LETTERS | - |
dc.identifier.doi | 10.1109/LCOMM.2020.2995857 | - |
dc.contributor.localauthor | Cho, Dong-Ho | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Transmitters | - |
dc.subject.keywordAuthor | Receivers | - |
dc.subject.keywordAuthor | Channel models | - |
dc.subject.keywordAuthor | Kernel | - |
dc.subject.keywordAuthor | Image coding | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Wireless communication | - |
dc.subject.keywordAuthor | Channel autoencoder | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | machine learning for communications | - |
dc.subject.keywordAuthor | multiple input multiple output | - |
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