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Deep Q-learning-based resource allocation for solar-powered users in cognitive radio networks

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Alternative Title
Deep Q-learning-based resource allocation for solar-powered users in cognitive radio networks
Abstract
This paper considers uplink solar-powered cognitive radio networks (CRNs) where multiple secondary users (SUs) transmit data to a secondary base station (SBS) by sharing a licensed channel of a primary system. A deep Q-learning (DQL) algorithm, which combines non-orthogonal multiple access (NOMA) and time division multiple access (TDMA) techniques, is proposed to maximize the long-term throughput of the system. By using our scheme, the agent (i.e. the SBS) can obtain the optimal decision by interacting with the environment to learn about system dynamics. Simulation results validate the superiority of the performance under the proposed scheme, compared with traditional schemes.
Author(s)
구인수황 티 흐엉 지앙Pham DuyThanh
Issued Date
2021
Type
Article
Keyword
Deep Q-learningEnergy harvestingNOMAPower allocationThroughput maximization
DOI
10.1016/j.icte.2021.01.008
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9048
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_07a3f6368fb2458f95237bd0ffc0d0cc&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Deep%20Q-learning-based%20resource%20allocation%20for%20solar-powered%20users%20in%20cognitive%20radio%20networks&offset=0&pcAvailability=true
Publisher
ICT EXPRESS
Location
대한민국
Language
영어
ISSN
2405-9595
Citation Volume
7
Citation Number
1
Citation Start Page
49
Citation End Page
59
Appears in Collections:
Engineering > IT Convergence
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