Noise Learning-Based Denoising Autoencoder
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Woong-Hee | - |
dc.contributor.author | Ozger, Mustafa | - |
dc.contributor.author | Challita, Ursula | - |
dc.contributor.author | Sung, Ki Won | - |
dc.date.accessioned | 2022-02-24T12:40:22Z | - |
dc.date.available | 2022-02-24T12:40:22Z | - |
dc.date.created | 2022-02-07 | - |
dc.date.issued | 2021-09 | - |
dc.identifier.issn | 1089-7798 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/136739 | - |
dc.description.abstract | This letter introduces a new denoiser that modifies the structure of denoising autoencoder (DAE), namely noise learning based DAE (nlDAE). The proposed nlDAE learns the noise of the input data. Then, the denoising is performed by subtracting the regenerated noise from the noisy input. Hence, nlDAE is more effective than DAE when the noise is simpler to regenerate than the original data. To validate the performance of nlDAE, we provide three case studies: signal restoration, symbol demodulation, and precise localization. Numerical results suggest that nlDAE requires smaller latent space dimension and smaller training dataset compared to DAE. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | CHALLENGES | - |
dc.subject | NETWORKS | - |
dc.title | Noise Learning-Based Denoising Autoencoder | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Woong-Hee | - |
dc.identifier.doi | 10.1109/LCOMM.2021.3091800 | - |
dc.identifier.scopusid | 2-s2.0-85111629633 | - |
dc.identifier.wosid | 000694697800046 | - |
dc.identifier.bibliographicCitation | IEEE COMMUNICATIONS LETTERS, v.25, no.9, pp.2983 - 2987 | - |
dc.relation.isPartOf | IEEE COMMUNICATIONS LETTERS | - |
dc.citation.title | IEEE COMMUNICATIONS LETTERS | - |
dc.citation.volume | 25 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 2983 | - |
dc.citation.endPage | 2987 | - |
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.keywordPlus | CHALLENGES | - |
dc.subject.keywordPlus | NETWORKS | - |
dc.subject.keywordAuthor | Decoding | - |
dc.subject.keywordAuthor | Encoding | - |
dc.subject.keywordAuthor | Internet of Things | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Noise measurement | - |
dc.subject.keywordAuthor | Noise reduction | - |
dc.subject.keywordAuthor | Random variables | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | noise learning based denoising autoencoder | - |
dc.subject.keywordAuthor | precise localization | - |
dc.subject.keywordAuthor | signal restoration | - |
dc.subject.keywordAuthor | symbol demodulation | - |
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