Compressive sensing based pilot reduction technique for massive MIMO systems
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Jun Won | - |
dc.contributor.author | Shim, Byonghyo | - |
dc.date.accessioned | 2022-07-07T04:29:18Z | - |
dc.date.available | 2022-07-07T04:29:18Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2015-10 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/143104 | - |
dc.description.abstract | Massive multi-input multi-output (MIMO) technique deploys a number of transmit antennas in base-station (BS) to support large number of users and high data throughput. Since BS needs to acquire channel state information from all transmit antennas, substantial amount of downlink pilot signals is required. In this paper, we suggest a new downlink pilot allocation strategy, inspired by the compressed sensing principle, that reduces the density of the pilot significantly. Key observation in the proposed approach is that the sparse structure of the channel impulse response (CIR) tends to change slower than the OFDM symbol rate. Through computer simulations, we show that the proposed scheme outperforms the conventional compressed sensing methods, achieving the performance bound provided by the Oracle-based Kalman smoother. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.title | Compressive sensing based pilot reduction technique for massive MIMO systems | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Jun Won | - |
dc.identifier.doi | 10.1109/ITA.2015.7308974 | - |
dc.identifier.scopusid | 2-s2.0-84961827780 | - |
dc.identifier.bibliographicCitation | 2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings, pp.115 - 118 | - |
dc.relation.isPartOf | 2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings | - |
dc.citation.title | 2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings | - |
dc.citation.startPage | 115 | - |
dc.citation.endPage | 118 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Antennas | - |
dc.subject.keywordPlus | Channel estimation | - |
dc.subject.keywordPlus | Channel state information | - |
dc.subject.keywordPlus | Communication channels (information theory) | - |
dc.subject.keywordPlus | Impulse response | - |
dc.subject.keywordPlus | Information theory | - |
dc.subject.keywordPlus | MIMO systems | - |
dc.subject.keywordPlus | Orthogonal frequency division multiplexing | - |
dc.subject.keywordPlus | Signal reconstruction | - |
dc.subject.keywordPlus | Signal to noise ratio | - |
dc.subject.keywordPlus | Allocation strategy | - |
dc.subject.keywordPlus | Channel impulse response | - |
dc.subject.keywordPlus | Compressive sensing | - |
dc.subject.keywordPlus | Multi input multi output | - |
dc.subject.keywordPlus | Performance bounds | - |
dc.subject.keywordPlus | Q measurements | - |
dc.subject.keywordPlus | Reduction techniques | - |
dc.subject.keywordPlus | Transmit antenna | - |
dc.subject.keywordPlus | Compressed sensing | - |
dc.subject.keywordAuthor | OFDM | - |
dc.subject.keywordAuthor | Q measurement | - |
dc.subject.keywordAuthor | Signal to noise ratio | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/7308974 | - |
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