There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 1362 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 1349 | - |
dc.citation.title | IEEE TRANSACTIONS ON MOBILE COMPUTING | - |
dc.citation.volume | 12 | - |
dc.contributor.author | Kim, Hyoil | - |
dc.contributor.author | Shin, Kang G. | - |
dc.date.accessioned | 2023-12-22T03:43:18Z | - |
dc.date.available | 2023-12-22T03:43:18Z | - |
dc.date.created | 2013-07-04 | - |
dc.date.issued | 2013-07 | - |
dc.description.abstract | We address the problem of rapidly discovering spectrum opportunities for seamless service provisioning in cognitive radio networks (CRNs). In particular, we focus on multichannel communications via channel-bonding with heterogeneous channel characteristics of ON/OFF patterns, sensing time, and channel capacity. Using dynamic programming (DP), we derive an optimal online sensing sequence incurring a minimal opportunity-discovery delay, and propose a suboptimal sequence that presents a near-optimal performance while incurring significantly less computational overhead than the DP algorithm. To facilitate fast opportunity discovery, we also propose a channel-management strategy that maintains a list of backup channels to be used at building the optimal sequence. A hybrid of maximum likelihood (ML) and Bayesian inference is introduced as well for flexible estimation of ON/OFF channel-usage patterns, which selectively chooses the better between the two according to the frequency of sensing and ON/OFF durations. The performance of the proposed schemes, in terms of the opportunity-discovery delay, is evaluated via in-depth simulation, and for the scenarios we considered, the proposed suboptimal sequence achieves a near-optimal performance with only an average of 0.5 percent difference from the optimal delay, and outperforms the previously proposed probabilistic scheme by up to 50.1 percent. In addition, the backup channel update scheme outperforms the no-update case by up to 49.9 percent. | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON MOBILE COMPUTING, v.12, no.7, pp.1349 - 1362 | - |
dc.identifier.doi | 10.1109/TMC.2012.108 | - |
dc.identifier.issn | 1536-1233 | - |
dc.identifier.scopusid | 2-s2.0-84878145505 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/3658 | - |
dc.identifier.url | http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84878145505 | - |
dc.identifier.wosid | 000319411600008 | - |
dc.language | 영어 | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.title | Optimal online sensing sequence in multichannel cognitive radio networks | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems; Telecommunications | - |
dc.relation.journalResearchArea | Computer Science; Telecommunications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Cognitive radio | - |
dc.subject.keywordAuthor | spectrum sensing | - |
dc.subject.keywordAuthor | sensing sequence | - |
dc.subject.keywordAuthor | backup channels | - |
dc.subject.keywordAuthor | candidate channels | - |
dc.subject.keywordAuthor | Bayesian estimation | - |
dc.subject.keywordPlus | ACCESS | - |
dc.subject.keywordPlus | ORDER | - |
dc.subject.keywordPlus | MAC | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.