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Deep Learning-Aided 5G Channel Estimation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Le Ha, An | - |
dc.contributor.author | Trinh Van Chien | - |
dc.contributor.author | Tien Hoa Nguyen | - |
dc.contributor.author | Choi, Wan | - |
dc.contributor.author | Van Duc Nguyen | - |
dc.date.accessioned | 2022-10-17T04:27:27Z | - |
dc.date.available | 2022-10-17T04:27:27Z | - |
dc.date.created | 2022-10-04 | - |
dc.date.issued | 2021-01 | - |
dc.identifier.citation | PROCEEDINGS OF THE 2021 15TH INTERNATIONAL CONFERENCE ON UBIQUITOUS INFORMATION MANAGEMENT AND COMMUNICATION (IMCOM 2021), p. 9377351 | - |
dc.identifier.issn | 2644-0164 | - |
dc.identifier.uri | https://hdl.handle.net/10371/186271 | - |
dc.description.abstract | Deep learning has demonstrated the important roles in improving the system performance and reducing computational complexity for 5G-and-heyond networks. In this paper, we propose a new channel estimation method with the assistance of deep learning in order to support the least squares estimation, which is a low-cost method but having relatively high channel estimation errors. This goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile used for simulations in the 5G networks under the severity of Doppler effects. Numerical results demonstrate the superiority of the proposed deep learning-assisted channel estimation method over the other channel estimation methods in previous works in terms of mean square errors. | - |
dc.language | 영어 | - |
dc.publisher | IEEE | - |
dc.title | Deep Learning-Aided 5G Channel Estimation | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/IMCOM51814.2021.9377351 | - |
dc.citation.journaltitle | PROCEEDINGS OF THE 2021 15TH INTERNATIONAL CONFERENCE ON UBIQUITOUS INFORMATION MANAGEMENT AND COMMUNICATION (IMCOM 2021) | - |
dc.identifier.wosid | 000672556500009 | - |
dc.identifier.scopusid | 2-s2.0-85103740935 | - |
dc.citation.startpage | 9377351 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Choi, Wan | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
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