Fast Beamforming Strategy: Learning the AoD of the Dominant Path
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Song, Yongmin | - |
dc.contributor.author | Kang, Jeongwan | - |
dc.contributor.author | Kim, Sunwoo | - |
dc.contributor.author | Jwa, Hyekyung | - |
dc.contributor.author | Na, Jeehyeon | - |
dc.date.accessioned | 2021-07-30T05:22:46Z | - |
dc.date.available | 2021-07-30T05:22:46Z | - |
dc.date.created | 2021-05-11 | - |
dc.date.issued | 2020-02 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/4460 | - |
dc.description.abstract | Small cell networks and directional beamforming have been regarded as the solutions to achieve high data rates and compensate for the high path loss in the millimeter-wave (mmWave) communications. Large antenna array in mmWave communications causes considerable overhead to select the beam using exhaustive beam search, which significantly affects the efficiency of these mobile communications. In this paper, we propose a fast beamforming strategy by estimating the angle of departure (AoD) of the dominant path by exploiting the position of the mobile station, leveraging the deep neural network. Simulation results, based on accurate ray-tracing, show that we can achieve up to 0.58° of angle accuracy. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Fast Beamforming Strategy: Learning the AoD of the Dominant Path | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sunwoo | - |
dc.identifier.doi | 10.1109/ICAIIC48513.2020.9065255 | - |
dc.identifier.scopusid | 2-s2.0-85084083434 | - |
dc.identifier.bibliographicCitation | 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, pp.267 - 271 | - |
dc.relation.isPartOf | 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 | - |
dc.citation.title | 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 | - |
dc.citation.startPage | 267 | - |
dc.citation.endPage | 271 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Beam forming networks | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Deep neural networks | - |
dc.subject.keywordPlus | Millimeter waves | - |
dc.subject.keywordPlus | Mobile telecommunication systems | - |
dc.subject.keywordPlus | Angle accuracies | - |
dc.subject.keywordPlus | Angle of departures | - |
dc.subject.keywordPlus | Directional beamforming | - |
dc.subject.keywordPlus | Millimeter waves (mmwave) | - |
dc.subject.keywordPlus | Mm-wave Communications | - |
dc.subject.keywordPlus | Mobile communications | - |
dc.subject.keywordPlus | Mobile station | - |
dc.subject.keywordPlus | Small cell Networks | - |
dc.subject.keywordPlus | Beamforming | - |
dc.subject.keywordAuthor | AoD | - |
dc.subject.keywordAuthor | beamforming | - |
dc.subject.keywordAuthor | deep neural network | - |
dc.subject.keywordAuthor | mm Wave | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9065255 | - |
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