DC Field | Value | Language |
---|---|---|
dc.contributor.author | Noh, Sangmi | ko |
dc.contributor.author | Ji, Dong Jin | ko |
dc.contributor.author | Cho, Dong-Ho | ko |
dc.date.accessioned | 2021-10-31T06:42:49Z | - |
dc.date.available | 2021-10-31T06:42:49Z | - |
dc.date.created | 2021-10-31 | - |
dc.date.created | 2021-10-31 | - |
dc.date.created | 2021-10-31 | - |
dc.date.created | 2021-10-31 | - |
dc.date.issued | 2021-10 | - |
dc.identifier.citation | IEEE COMMUNICATIONS LETTERS, v.25, no.10, pp.3262 - 3266 | - |
dc.identifier.issn | 1089-7798 | - |
dc.identifier.uri | http://hdl.handle.net/10203/288473 | - |
dc.description.abstract | As high-frequency bands are considered for wireless communications, the in-phase and quadrature imbalance caused by the local oscillator and frequency mixer is becoming more severe. This study proposed an I/Q phase imbalance estimator based on the self-attention mechanism, which captures long-range dependencies to learn the relations among the input data symbols. Simulation results showed that the proposed self-attention-based method reduces the estimation error compared with the conventional methods. The proposed method can reduce the required data symbols to estimate the I/Q imbalance by half while achieving better performance at high signal-to-noise ratio regimes. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | A Self-Attention-Based I/Q Imbalance Estimator for Beyond 5G Communication Systems | - |
dc.type | Article | - |
dc.identifier.wosid | 000704824300027 | - |
dc.identifier.scopusid | 2-s2.0-85111590364 | - |
dc.type.rims | ART | - |
dc.citation.volume | 25 | - |
dc.citation.issue | 10 | - |
dc.citation.beginningpage | 3262 | - |
dc.citation.endingpage | 3266 | - |
dc.citation.publicationname | IEEE COMMUNICATIONS LETTERS | - |
dc.identifier.doi | 10.1109/LCOMM.2021.3100629 | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Cho, Dong-Ho | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Signal to noise ratio | - |
dc.subject.keywordAuthor | Neural networks | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Mixers | - |
dc.subject.keywordAuthor | Correlation | - |
dc.subject.keywordAuthor | Transceivers | - |
dc.subject.keywordAuthor | Receivers | - |
dc.subject.keywordAuthor | Machine learning for communications | - |
dc.subject.keywordAuthor | self-attention | - |
dc.subject.keywordAuthor | I | - |
dc.subject.keywordAuthor | Q imbalance estimation | - |
dc.subject.keywordPlus | COMPENSATION | - |
dc.subject.keywordPlus | NETWORKS | - |
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