Link Performance Estimation Techniques for MIMO-OFDM Systems with Maximum Likelihood Receiver
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Moon, Sung-Hyun | - |
dc.contributor.author | Lee, Kyoung-Jae | - |
dc.contributor.author | Kim, Jihoon | - |
dc.contributor.author | Lee, Inkyu | - |
dc.date.accessioned | 2021-09-06T20:20:43Z | - |
dc.date.available | 2021-09-06T20:20:43Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2012-05 | - |
dc.identifier.issn | 1536-1276 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/108532 | - |
dc.description.abstract | Link adaptation allows a communication system to adapt its transmission modes according to channel conditions. Although a maximum likelihood (ML) receiver for multiple-input multiple-output (MIMO) systems provides optimal performance, estimating its link performance has been a difficult problem. In this paper, we propose a new link performance abstraction technique for MIMO orthogonal frequency-division multiplexing systems with the ML receiver. The performance of ML detection (MLD) is estimated by employing capacity bounds of two simple linear receivers. Then, we give a simple parametrization to compute the desired per-stream signal-to-noise ratio (SNR) values, which can be applied for both vertically and horizontally coded MIMO systems. Based on the derived per-stream SNR estimates, the block error rate is obtained using the received-bit information rate metrics. We also examine the effect of imperfect channel estimation as well as spatial correlations among antennas. Finally, extensive simulation results show that the proposed method provides superior estimation accuracy in the MIMO-MLD link evaluation with very low computational complexity. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | INTERLEAVED CODED OFDM | - |
dc.subject | ADAPTIVE MODULATION | - |
dc.subject | V-BLAST | - |
dc.subject | CAPACITY | - |
dc.subject | DIVERSITY | - |
dc.subject | BOUNDS | - |
dc.subject | QAM | - |
dc.subject | AMC | - |
dc.title | Link Performance Estimation Techniques for MIMO-OFDM Systems with Maximum Likelihood Receiver | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Inkyu | - |
dc.identifier.doi | 10.1109/TWC.2012.032712.111304 | - |
dc.identifier.scopusid | 2-s2.0-84861462367 | - |
dc.identifier.wosid | 000304242200019 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, v.11, no.5, pp.1808 - 1816 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS | - |
dc.citation.title | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS | - |
dc.citation.volume | 11 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 1808 | - |
dc.citation.endPage | 1816 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | INTERLEAVED CODED OFDM | - |
dc.subject.keywordPlus | ADAPTIVE MODULATION | - |
dc.subject.keywordPlus | V-BLAST | - |
dc.subject.keywordPlus | CAPACITY | - |
dc.subject.keywordPlus | DIVERSITY | - |
dc.subject.keywordPlus | BOUNDS | - |
dc.subject.keywordPlus | QAM | - |
dc.subject.keywordPlus | AMC | - |
dc.subject.keywordAuthor | Link adaptation | - |
dc.subject.keywordAuthor | PHY abstraction | - |
dc.subject.keywordAuthor | MIMO-OFDM | - |
dc.subject.keywordAuthor | maximum-likelihood receiver | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea 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.