Reinforcement Learning-Aided Channel Estimator in Time-Varying MIMO Systems
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Tae-Kyoung | - |
dc.contributor.author | Min, Moonsik | - |
dc.date.accessioned | 2023-07-23T12:40:18Z | - |
dc.date.available | 2023-07-23T12:40:18Z | - |
dc.date.created | 2023-07-21 | - |
dc.date.issued | 2023-06 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/88592 | - |
dc.description.abstract | This paper proposes a reinforcement learning-aided channel estimator for time-varying multi-input multi-output systems. The basic concept of the proposed channel estimator is the selection of the detected data symbol in the data-aided channel estimation. To achieve the selection successfully, we first formulate an optimization problem to minimize the data-aided channel estimation error. However, in time-varying channels, the optimal solution is difficult to derive because of its computational complexity and the time-varying nature of the channel. To address these difficulties, we consider a sequential selection for the detected symbols and a refinement for the selected symbols. A Markov decision process is formulated for sequential selection, and a reinforcement learning algorithm that efficiently computes the optimal policy is proposed with state element refinement. Simulation results demonstrate that the proposed channel estimator outperforms conventional channel estimators by efficiently capturing the variation of the channels. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | SENSORS | - |
dc.title | Reinforcement Learning-Aided Channel Estimator in Time-Varying MIMO Systems | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 001017967300001 | - |
dc.identifier.doi | 10.3390/s23125689 | - |
dc.identifier.bibliographicCitation | SENSORS, v.23, no.12 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85163963480 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 23 | - |
dc.citation.number | 12 | - |
dc.contributor.affiliatedAuthor | Kim, Tae-Kyoung | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | data-aided channel estimation | - |
dc.subject.keywordAuthor | non-iterative approach | - |
dc.subject.keywordAuthor | first-order Gaussian-Markov channel model | - |
dc.subject.keywordAuthor | reinforcement learning | - |
dc.subject.keywordPlus | MASSIVE MIMO | - |
dc.subject.keywordPlus | PILOT | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.