DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Hwanjin | ko |
dc.contributor.author | Kim, Sucheol | ko |
dc.contributor.author | Lee, Hyeongtaek | ko |
dc.contributor.author | Jang, Chulhee | ko |
dc.contributor.author | Choi, Yongyun | ko |
dc.contributor.author | Choi, Junil | ko |
dc.date.accessioned | 2021-03-02T01:10:17Z | - |
dc.date.available | 2021-03-02T01:10:17Z | - |
dc.date.created | 2021-02-18 | - |
dc.date.created | 2021-02-18 | - |
dc.date.created | 2021-02-18 | - |
dc.date.issued | 2021-01 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON COMMUNICATIONS, v.69, no.1, pp.518 - 528 | - |
dc.identifier.issn | 0090-6778 | - |
dc.identifier.uri | http://hdl.handle.net/10203/281084 | - |
dc.description.abstract | This paper focuses on channel prediction techniques for massive multiple-input multiple-output (MIMO) systems. Previous channel predictors are based on theoretical channel models, which would be deviated from realistic channels. In this paper, we develop and compare a vector Kalman filter (VKF)-based channel predictor and a machine learning (ML)-based channel predictor using the realistic channels from the spatial channel model (SCM), which has been adopted in the 3GPP standard for years. First, we propose a low-complexity mobility estimator based on the spatial average using a large number of antennas in massive MIMO. The mobility estimate can be used to determine the complexity order of developed predictors. The VKF-based channel predictor developed in this paper exploits the autoregressive (AR) parameters estimated from the SCM channels based on the Yule-Walker equations. Then, the ML-based channel predictor using the linear minimum mean square error (LMMSE)-based noise pre-processed data is developed. Numerical results reveal that both channel predictors have substantial gain over the outdated channel in terms of the channel prediction accuracy and data rate. The ML-based predictor has larger overall computational complexity than the VKF-based predictor, but once trained, the operational complexity of ML-based predictor becomes smaller than that of VKF-based predictor. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Massive MIMO Channel Prediction: Kalman Filtering Vs. Machine Learning | - |
dc.type | Article | - |
dc.identifier.wosid | 000608689300035 | - |
dc.identifier.scopusid | 2-s2.0-85099748144 | - |
dc.type.rims | ART | - |
dc.citation.volume | 69 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 518 | - |
dc.citation.endingpage | 528 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON COMMUNICATIONS | - |
dc.identifier.doi | 10.1109/TCOMM.2020.3027882 | - |
dc.contributor.localauthor | Choi, Junil | - |
dc.contributor.nonIdAuthor | Jang, Chulhee | - |
dc.contributor.nonIdAuthor | Choi, Yongyun | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Massive MIMO | - |
dc.subject.keywordAuthor | mobility estimation | - |
dc.subject.keywordAuthor | channel prediction | - |
dc.subject.keywordAuthor | autoregressive model | - |
dc.subject.keywordAuthor | vector Kalman filter | - |
dc.subject.keywordAuthor | machine learning | - |
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