Beyond Max-weight Scheduling: A Reinforcement Learning-based Approach

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dc.contributor.authorBae, Jeongminko
dc.contributor.authorLee, Joohyunko
dc.contributor.authorChong, Songko
dc.date.accessioned2023-08-04T03:00:54Z-
dc.date.available2023-08-04T03:00:54Z-
dc.date.created2023-07-07-
dc.date.created2023-07-07-
dc.date.issued2019-06-
dc.identifier.citation17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2019-
dc.identifier.urihttp://hdl.handle.net/10203/311156-
dc.description.abstractAs network architecture becomes complex and the user requirement gets diverse, the role of efficient network resource management becomes more important. However, existing network scheduling algorithms such as the max-weight algorithm suffer from poor delay performance. In this paper, we present a reinforcement learning-based network scheduling algorithm that achieves both optimal throughput and low delay. To this end, we first formulate the network optimization problem as an MDP problem. Then we introduce a new state-action value function called W-function and develop a reinforcement learning algorithm called W-Learning that guarantees little performance loss during a learning process. Finally, via simulation, we verify that our algorithm shows delay reduction of up to 40.8% compared to the max-weight algorithm over various scenarios.-
dc.languageEnglish-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleBeyond Max-weight Scheduling: A Reinforcement Learning-based Approach-
dc.typeConference-
dc.identifier.wosid000643752100013-
dc.identifier.scopusid2-s2.0-85080582054-
dc.type.rimsCONF-
dc.citation.publicationname17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2019-
dc.identifier.conferencecountryFR-
dc.identifier.conferencelocationAvignon-
dc.identifier.doi10.23919/WiOPT47501.2019.9144097-
dc.contributor.localauthorChong, Song-
dc.contributor.nonIdAuthorLee, Joohyun-
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AI-Conference Papers(학술대회논문)
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