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Semantic Communication with Bayesian Reinforcement Learning in Edge Computing
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Publication Year
2023-01-01
Journal
Proceedings - 2023 IEEE Future Networks World Forum: Future Networks: Imagining the Network of the Future, FNWF 2023
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - 2023 IEEE Future Networks World Forum: Future Networks: Imagining the Network of the Future, FNWF 2023
Keyword
bayesian adaptive markov decision processbayesian reinforcement learningsemantic communication
Mesh Keyword
BayesianBayesian adaptive markov decision processBayesian reinforcement learningEdge computingEra technologiesInnovative technologyIt focusMarkov Decision ProcessesNetwork communicationsSemantic communication
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Networks and CommunicationsHardware and ArchitectureSafety, Risk, Reliability and Quality
Abstract
With the advent of 6G era, technologies integrating artificial intelligence and network communication have emerged as pivotal forces in shaping the future of connectivity. Among these innovative technologies, semantic communication stands out as it focuses on transmitting semantic representations rather than raw data sequences, thereby enhancing scalability, network efficiency, and performance. In this study, we propose a novel semantic communication framework utilizing Bayesian Rein-forcement Learning. Our proposed framework takes into account the relationship between the receiver (i.e., the edge servers) with robust computing capabilities and extensive knowledge bases, and the sender (i.e., the user device) with limited computing power and knowledge repositories, offloading the computational burden to the receiver. The receiver then proceeds with learning, considering the uncertainty of the model. This proposed framework is intended for application in semantic communication, which is not yet in use in actual communication systems, and has potential applications in areas such as edge computing and IoT.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36943
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85194186579&origin=inward
DOI
https://doi.org/10.1109/fnwf58287.2023.10520383
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10520248
Type
Conference
Funding
This work was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) (NRF-2020R1A2C1102284 and NRF-2021R1A2C1012776), and also supported by the BK21 FOUR program of the NRF of Korea funded by the Ministry of Education (NRF-5199991514504).
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Ko, Young-Bae고영배
Department of Software and Computer Engineering
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