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강화학습 기반의 위성통신 빔호핑 최적화 기법
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | 정병창 | - |
| dc.contributor.author | 김남이 | - |
| dc.date.accessioned | 2023-05-15T07:41:15Z | - |
| dc.date.available | 2023-05-15T07:41:15Z | - |
| dc.date.issued | 2023-04 | - |
| dc.identifier.issn | 2234-4772 | - |
| dc.identifier.issn | 2288-4165 | - |
| dc.identifier.uri | https://scholarworks.gnu.ac.kr/handle/sw.gnu/59383 | - |
| dc.description.abstract | In this paper, we suggest a reinforcement learning algorithm in satellite-aided communication system. Satellite-aided communication are drawing attention because it can increase stability and robustness of wireless communication. To improve the efficiency of satellite-aided communication, beamhopping method is introduced. In beamhopping, satellite beams are divided and operated in time-based system. The actual efficiency of beamhopping can be depend on the hopping pattern. To determine beamhopping pattern, we introduced multiplay Thompson sampling which is a family of reinforcement learning. We mathematically modeled the features of satellite communication and then applied reinforcement learning. By means of simulations, the performances of proposed learning were analyzed in terms of capacity and regret. | - |
| dc.format.extent | 4 | - |
| dc.language | 한국어 | - |
| dc.language.iso | KOR | - |
| dc.publisher | 한국정보통신학회 | - |
| dc.title | 강화학습 기반의 위성통신 빔호핑 최적화 기법 | - |
| dc.title.alternative | Beamhopping method in satellite communication based on reinforcement learning | - |
| dc.type | Article | - |
| dc.publisher.location | 대한민국 | - |
| dc.identifier.bibliographicCitation | 한국정보통신학회논문지, v.27, no.4, pp 575 - 578 | - |
| dc.citation.title | 한국정보통신학회논문지 | - |
| dc.citation.volume | 27 | - |
| dc.citation.number | 4 | - |
| dc.citation.startPage | 575 | - |
| dc.citation.endPage | 578 | - |
| dc.identifier.kciid | ART002952045 | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | kci | - |
| dc.subject.keywordAuthor | satellite communications | - |
| dc.subject.keywordAuthor | beam-hopping | - |
| dc.subject.keywordAuthor | reinforcement learning | - |
| dc.subject.keywordAuthor | regret minimization | - |
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