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dc.contributor.author Wi, Gwangjin ko
dc.contributor.author Son, Sunghwa ko
dc.contributor.author Park, Kyung-Joon ko
dc.date.accessioned 2021-01-29T07:28:08Z -
dc.date.available 2021-01-29T07:28:08Z -
dc.date.created 2021-01-14 -
dc.date.issued 2020-10-22 -
dc.identifier.citation 11th International Conference on Information and Communication Technology Convergence, ICTC 2020, pp.370 - 372 -
dc.identifier.isbn 9781728167589 -
dc.identifier.issn 2162-1233 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/12887 -
dc.description.abstract In tactical networks, traffic should be delivered in a timely manner satisfying the quality of service (QoS) requirements for survivability and mission success. In this paper, we propose a centralized TDMA slot scheduling based on deep reinforcement learning (DRL) to guarantee the QoS requirements by minimizing end-to-end delay. We consider situations in which mission criticality of tactical traffic is dynamically changing. We introduce a DRL actor-critic algorithm to find a TDMA scheduling policy to minimize the weighted end-to-end delay which is a new metric reflecting the mission criticality of tactical traffic. The simulation results verify that the proposed scheduling policy can guarantee QoS requirements in tactical networks. © 2020 IEEE. -
dc.language English -
dc.publisher IEEE Computer Society -
dc.title Delay-aware TDMA Scheduling with Deep Reinforcement Learning in Tactical MANET -
dc.type Conference -
dc.identifier.doi 10.1109/ICTC49870.2020.9289080 -
dc.identifier.scopusid 2-s2.0-85098979436 -
dc.type.local Article(Overseas) -
dc.type.rims CONF -
dc.description.journalClass 1 -
dc.contributor.localauthor Park, Kyung-Joon -
dc.identifier.citationStartPage 370 -
dc.identifier.citationEndPage 372 -
dc.identifier.citationTitle 11th International Conference on Information and Communication Technology Convergence, ICTC 2020 -
dc.identifier.conferencecountry KO -
dc.identifier.conferencelocation 제주 -

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