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Reinforcement Learning Based 5G Enabled Cognitive Radio Networks
  • Puspita, Ratih Hikmah ;
  • Shah, Syed Danial Ali ;
  • Lee, Gyu Min ;
  • Roh, Byeong Hee ;
  • Oh, Jimyeong ;
  • Kang, Sungjin
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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.555-558
Keyword
cognitive radionetwork slicingreinforcement learningspectrum 5G
Mesh Keyword
Cognitive radio networkCognitive radio network (CRN)Future research directionsMachine learning techniquesNetwork slicingspectrum 5GSpectrum managementSpectrum sharing
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) is a spectrum sharing technology that facilitates a hierarchal coexistence between licensed and license-exempt users over licensed bands. One of the biggest challenges in cognitive radio network (CRN) is efficient spectrum management. Recently, a trend has shifted towards the use of machine learning techniques such as reinforcement learning for learning problem in CRN. This paper provides an insight into the working principles of reinforcement learning based CRN and summarizes the recent survey papers done on the topic of learning based CRN. This paper also presents a 5G technology i.e. network slicing, based intelligent CRN architecture for efficient spectrum management. Some challenges in the existing solutions and future research directions are also introduced.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36450
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85078224749&origin=inward
DOI
https://doi.org/10.1109/ictc46691.2019.8939986
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8932631
Type
Conference
Funding
This research was supported partially by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2019-2018-0-01431) supervised by the IITP(Institute for Information communications Technology Promotion), and also supported partially by the LIG Nex1 Co., Ltd.. \u2021: corresponding author
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Roh, Byeong-hee노병희
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