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Application of Heuristic-Learning Model to Reduce Spectrum Sensing Energy in Cognitive Radio Network
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Publication Year
2019-10-01
Journal
ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future, pp.648-652
Keyword
cognitive radioheuristic learningmarkov modelprimary user predictionreduce energy
Mesh Keyword
Cognitive radio networkHeuristic approachHeuristic learningMarkov modelPrediction methodsPrimary Usersreduce energySpectrum utilization
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Networks and CommunicationsComputer Science ApplicationsInformation Systems and ManagementManagement of Technology and InnovationSafety, Risk, Reliability and QualityMedia TechnologyControl and Optimization
Abstract
Cognitive radio (CR) can access licensed spectrum opportunistically without creating any interference to the licensed users. This is possible due to frequent spectrum sensing to identify the underutilized spectrum bands. However, the behavior of spectrum sensing consumes remarkable amount of battery power and thus reduces the lifetime of a user. Though the primary concept of CR is to enhance spectrum utilization, the importance of energy efficiency brings several new challenges. For a user with limited battery power, better throughout and energy efficiency can be paradoxical. In this work, a low complexity heuristic approach is proposed along with a prediction method based on learning. This approach reduces energy consumption by avoiding unnecessary sensing processes according to the prediction. The significances of our proposed approach are shown through simulations.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36447
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85078295265&origin=inward
DOI
https://doi.org/10.1109/ictc46691.2019.8939848
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8932631
Type
Conference
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Choi, Youngjune Image
Choi, Youngjune최영준
Department of Software and Computer Engineering
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