DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jang, Ki Joung | ko |
dc.contributor.author | Yoon, Youngkeun | ko |
dc.contributor.author | Kim, Junseok | ko |
dc.contributor.author | Kim, Jong Ho | ko |
dc.contributor.author | Hwang, Ganguk | ko |
dc.date.accessioned | 2021-11-23T06:40:15Z | - |
dc.date.available | 2021-11-23T06:40:15Z | - |
dc.date.created | 2021-11-22 | - |
dc.date.created | 2021-11-22 | - |
dc.date.created | 2021-11-22 | - |
dc.date.issued | 2021-11 | - |
dc.identifier.citation | IEEE COMMUNICATIONS LETTERS, v.25, no.11, pp.3719 - 3723 | - |
dc.identifier.issn | 1089-7798 | - |
dc.identifier.uri | http://hdl.handle.net/10203/289355 | - |
dc.description.abstract | Rainfall is considered as one of the most crucial atmospheric elements that cause attenuation in signal propagation, especially in high-frequency range for fifth-generation (5G) and beyond wireless networks. To unveil the complex relationships between rain attenuation and other factors including rainfall rate, we propose a new rain attenuation prediction model for terrestrial line-of-sight (LoS) propagation using Gaussian Process Regression (GPR). In the proposed model the Recommendation ITU-R P.530 (called the ITU-R model) is used as the mean function in GPR, and to capture the deviation of measured rain attenuation from the ITU-R model we develop a latent function in GPR motivated from the ITU-R model. We validate with the ITU-R study group 3 databank (DBSG3) that the proposed model provides high prediction accuracy. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Rain Attenuation Prediction Model for Terrestrial Links Using Gaussian Process Regression | - |
dc.type | Article | - |
dc.identifier.wosid | 000716696200060 | - |
dc.identifier.scopusid | 2-s2.0-85114729528 | - |
dc.type.rims | ART | - |
dc.citation.volume | 25 | - |
dc.citation.issue | 11 | - |
dc.citation.beginningpage | 3719 | - |
dc.citation.endingpage | 3723 | - |
dc.citation.publicationname | IEEE COMMUNICATIONS LETTERS | - |
dc.identifier.doi | 10.1109/LCOMM.2021.3109619 | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Hwang, Ganguk | - |
dc.contributor.nonIdAuthor | Yoon, Youngkeun | - |
dc.contributor.nonIdAuthor | Kim, Junseok | - |
dc.contributor.nonIdAuthor | Kim, Jong Ho | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Rain | - |
dc.subject.keywordAuthor | Attenuation | - |
dc.subject.keywordAuthor | Predictive models | - |
dc.subject.keywordAuthor | Mathematical model | - |
dc.subject.keywordAuthor | Kernel | - |
dc.subject.keywordAuthor | Computational modeling | - |
dc.subject.keywordAuthor | ITU | - |
dc.subject.keywordAuthor | Gaussian process regression | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | prediction methods | - |
dc.subject.keywordAuthor | supervised learning | - |
dc.subject.keywordAuthor | rain attenuation | - |
dc.subject.keywordAuthor | millimeter wave propagation | - |
dc.subject.keywordAuthor | wireless networks | - |
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