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Ahead-Me Coverage (AMC): On Maintaining Enhanced Mobile Network Coverage for UAVs

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Hellaoui, Hamed
dc.contributor.author Yang, Bin
dc.contributor.author Taleb, Tarik
dc.contributor.author Manner, Jukka
dc.date.accessioned 2023-02-01T09:10:10Z
dc.date.available 2023-02-01T09:10:10Z
dc.date.issued 2023-01-11
dc.identifier.citation Hellaoui , H , Yang , B , Taleb , T & Manner , J 2023 , Ahead-Me Coverage (AMC): On Maintaining Enhanced Mobile Network Coverage for UAVs . in GLOBECOM 2022 - 2022 IEEE Global Communications Conference . , 10000874 , IEEE , pp. 2975-2980 , IEEE Global Communications Conference , Rio de Janeiro , Brazil , 04/12/2022 . https://doi.org/10.1109/GLOBECOM48099.2022.10000874 en
dc.identifier.isbn 978-1-6654-3541-3
dc.identifier.other PURE UUID: 1e99426b-0a07-4231-abd8-99717ca63818
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/1e99426b-0a07-4231-abd8-99717ca63818
dc.identifier.other PURE LINK: https://ieeexplore.ieee.org/document/10000874/
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85146926329&partnerID=8YFLogxK
dc.identifier.other PURE LINK: http://urn.fi/urn:nbn:fi-fe2023051143536
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/119521
dc.description | openaire: EC/H2020/857031/EU//5G!Drones
dc.description.abstract This paper proposes the concept of Ahead-Me Cov-erage (AMC) aiming to get the coverage of a cellular network ahead of the mobile users for maintaining enhanced Quality- of-Service (QoS) in cellular-connected unmanned aerial vehicle (UAV) networks. In such networks, each base station (BS) with an intelligent logic can automatically tilt the direction of its radio antennas based on the trajectory of UAV s. For this purpose, we first formulate AMC as an integer optimization problem for maximizing the minimum transmission rate of UAVs by jointly optimizing the angles of the different radio antenna, the resource allocation and the selection of the appropriate serving BS for the UAVs throughout their path. For this complex optimization problem, we then propose a solution based on Deep Reinforcement Learning (DRL) to solve it. Under this solution, we adopt a multi-heterogeneous agent-based approach (MHA-DRL) including two types of agents, namely the UAV agents and the BS agents. Each agent implements an Advantage Actor Critic (A2C) to learn optimal policies. Specifically, the BS agents aim to tilt their antennas to get ahead of the UAV s throughout their mobility, and the UAV agents target selecting the appropriate serving BSs along with resource allocation. Performance evaluations are presented to validate the effectiveness of the proposed approach. en
dc.format.extent 6
dc.format.extent 2975-2980
dc.language.iso en en
dc.relation info:eu-repo/grantAgreement/EC/H2020/857031/EU//5G!Drones
dc.relation.ispartof IEEE Global Communications Conference en
dc.relation.ispartofseries GLOBECOM 2022 - 2022 IEEE Global Communications Conference en
dc.rights openAccess en
dc.title Ahead-Me Coverage (AMC): On Maintaining Enhanced Mobile Network Coverage for UAVs en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Information and Communications Engineering
dc.contributor.department Chuzhou University
dc.contributor.department University of Oulu
dc.subject.keyword Performance evaluation
dc.subject.keyword Transmitting antennas
dc.subject.keyword Reinforcement learning
dc.subject.keyword Quality of service
dc.subject.keyword Interference
dc.subject.keyword Directive antennas
dc.subject.keyword Autonomous aerial vehicles
dc.identifier.urn URN:NBN:fi:aalto-202302011871
dc.identifier.doi 10.1109/GLOBECOM48099.2022.10000874

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