UniQGAN: Unified Generative Adversarial Networks for Augmented Modulation Classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Insup | - |
dc.contributor.author | Lee, Wonjun | - |
dc.date.accessioned | 2022-03-14T07:41:39Z | - |
dc.date.available | 2022-03-14T07:41:39Z | - |
dc.date.created | 2022-03-14 | - |
dc.date.issued | 2022-02 | - |
dc.identifier.issn | 1089-7798 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/138921 | - |
dc.description.abstract | Deep learning has been widely applied to automatic modulation classification (AMC), and there have been many studies on data augmentation techniques using deep generative models to improve performance. However, existing solutions need to train different models independently for each SNR, which leads to undeniable overhead. This letter presents UniQGAN, Unified Generative Adversarial Networks for IQ constellations of various SNRs, requiring a single model training. The proposed method introduces multi-conditions embedding and multi-domains classification to leverage both conditions, i.e., modulation type and SNR. Experimental results show that UniQGAN effectively improves the AMC performance, while the training time is reduced. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | UniQGAN: Unified Generative Adversarial Networks for Augmented Modulation Classification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Wonjun | - |
dc.identifier.doi | 10.1109/LCOMM.2021.3131476 | - |
dc.identifier.scopusid | 2-s2.0-85120555506 | - |
dc.identifier.wosid | 000754246100032 | - |
dc.identifier.bibliographicCitation | IEEE COMMUNICATIONS LETTERS, v.26, no.2, pp.355 - 358 | - |
dc.relation.isPartOf | IEEE COMMUNICATIONS LETTERS | - |
dc.citation.title | IEEE COMMUNICATIONS LETTERS | - |
dc.citation.volume | 26 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 355 | - |
dc.citation.endPage | 358 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordAuthor | Signal to noise ratio | - |
dc.subject.keywordAuthor | Generative adversarial networks | - |
dc.subject.keywordAuthor | Modulation | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Data models | - |
dc.subject.keywordAuthor | Training data | - |
dc.subject.keywordAuthor | Constellation diagram | - |
dc.subject.keywordAuthor | Automatic modulation classification | - |
dc.subject.keywordAuthor | generative adversarial networks | - |
dc.subject.keywordAuthor | single model training | - |
dc.subject.keywordAuthor | IQ~constellations | - |
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