DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ji, Dong Jin | ko |
dc.contributor.author | Cho, Dong-Ho | ko |
dc.date.accessioned | 2021-06-01T04:50:05Z | - |
dc.date.available | 2021-06-01T04:50:05Z | - |
dc.date.created | 2021-03-11 | - |
dc.date.created | 2021-03-11 | - |
dc.date.issued | 2021-05 | - |
dc.identifier.citation | IEEE WIRELESS COMMUNICATIONS LETTERS, v.10, no.5, pp.1079 - 1082 | - |
dc.identifier.issn | 2162-2337 | - |
dc.identifier.uri | http://hdl.handle.net/10203/285400 | - |
dc.description.abstract | Recently the idea of using deep learning algorithms in massive multiple-input multiple-output channel state information feedback has been studied in great detail. To use the deep-learning-based feedback schemes over the air, they must be able to operate under extremely large antennas. These schemes also need to be verified under a realistic channel model and propagation environment. Thus, we propose ChannelAttention, a deep-learning-based channel state information feedback scheme that utilizes attention layers and residual blocks. Simulations for a 64-by-64 QuaDRiGa channel model based on the 3GPP 38.901 urban microcell scenario show that ChannelAttention surpasses the normalized mean square error and cosine similarity performance of the conventional CsiNet+ scheme across all compression ratio ranges. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | ChannelAttention: Utilizing Attention Layers for Accurate Massive MIMO Channel Feedback | - |
dc.type | Article | - |
dc.identifier.wosid | 000648187800036 | - |
dc.identifier.scopusid | 2-s2.0-85100860977 | - |
dc.type.rims | ART | - |
dc.citation.volume | 10 | - |
dc.citation.issue | 5 | - |
dc.citation.beginningpage | 1079 | - |
dc.citation.endingpage | 1082 | - |
dc.citation.publicationname | IEEE WIRELESS COMMUNICATIONS LETTERS | - |
dc.identifier.doi | 10.1109/LWC.2021.3057934 | - |
dc.contributor.localauthor | Cho, Dong-Ho | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Machine learning for communications | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | channel feedback | - |
dc.subject.keywordAuthor | multiple-input multiple-output | - |
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