DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 최준일 | - |
dc.contributor.author | Ko, Beomsoo | - |
dc.contributor.author | 고범수 | - |
dc.date.accessioned | 2024-07-26T19:31:15Z | - |
dc.date.available | 2024-07-26T19:31:15Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1051073&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/321053 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2023.2,[iii, 15 p. :] | - |
dc.description.abstract | however, the time overhead of training phase directly affects the latency of the system. In this paper, we propose a novel ML-based channel prediction technique, which can reduce both the time overhead of collecting training data and training a neural network by transforming the domain of channels from subcarrier to antenna in wideband massive MIMO systems. Numerical results show that the proposed technique can not only reduce the time overhead but also improve channel prediction performance compared to the ML-based channel prediction techniques without domain transformation. | - |
dc.description.abstract | To compensate the loss from outdated channel state information in wideband massive multiple-input multiple-output (MIMO) systems, channel prediction can be performed by leveraging the temporal correlation of wireless channels. Machine learning (ML)-based channel predictors for massive MIMO systems were designed recently | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 거대 다중 안테나▼a광대역▼a채널 예측▼a기계 학습▼a도메인 변환 | - |
dc.subject | Massive MIMO▼aWideband system▼aChannel prediction▼aMachine learning▼aDomain transformation | - |
dc.title | Massive MIMO channel prediction using machine learning: Power of domain transformation | - |
dc.title.alternative | 거대 다중 안테나 통신 시스템에서 기계 학습 기반 채널 예측: 도메인 변환의 활용 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | Choi, Junil | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.