DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Minhoe | ko |
dc.contributor.author | Lee, Woongsup | ko |
dc.contributor.author | Cho, Dong-Ho | ko |
dc.date.accessioned | 2020-12-30T01:30:09Z | - |
dc.date.available | 2020-12-30T01:30:09Z | - |
dc.date.created | 2020-12-14 | - |
dc.date.created | 2020-12-14 | - |
dc.date.issued | 2020-11 | - |
dc.identifier.citation | ELECTRONICS, v.9, no.11 | - |
dc.identifier.issn | 2079-9292 | - |
dc.identifier.uri | http://hdl.handle.net/10203/279303 | - |
dc.description.abstract | In this paper, we investigate a deep learning based resource allocation scheme for massive multiple-input-multiple-output (MIMO) communication systems, where a base station (BS) with a large scale antenna array communicates with a user equipment (UE) using beamforming. In particular, we propose Deep Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. Through simulations, we confirm that the optimal beam vector can be found with a high probability. We also show that the complexity required to find the optimum beam vector can be reduced significantly in comparison with conventional beam search schemes. | - |
dc.language | English | - |
dc.publisher | MDPI | - |
dc.title | Deep Scanning-Beam Selection Based on Deep Reinforcement Learning in Massive MIMO Wireless Communication System | - |
dc.type | Article | - |
dc.identifier.wosid | 000592940300001 | - |
dc.identifier.scopusid | 2-s2.0-85095747312 | - |
dc.type.rims | ART | - |
dc.citation.volume | 9 | - |
dc.citation.issue | 11 | - |
dc.citation.publicationname | ELECTRONICS | - |
dc.identifier.doi | 10.3390/electronics9111844 | - |
dc.contributor.localauthor | Cho, Dong-Ho | - |
dc.description.isOpenAccess | Y | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | beam search | - |
dc.subject.keywordAuthor | deep reinforcement learning | - |
dc.subject.keywordAuthor | massive MIMO | - |
dc.subject.keywordAuthor | Q-learning | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.