Energy-Efficient UAV Communications with Interference Management: Deep Learning Framework

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorGhavimi, Fayezehen_US
dc.contributor.authorJantti, Rikuen_US
dc.contributor.departmentDepartment of Communications and Networkingen_US
dc.date.accessioned2020-08-12T09:12:23Z
dc.date.available2020-08-12T09:12:23Z
dc.date.issued2020-04en_US
dc.description| openaire: EC/H2020/815191/EU//PriMO-5G
dc.description.abstractIn this paper, an interference-aware energy- efficient scheme for a network of coexisting aerial-terrestrial cellular users is proposed. In particular, each aerial user aims at achieving a trade-off between maximizing energy efficiency and spectral efficiency while minimizing the incurred interference on the terrestrial users along its path. To provide a solution, we first formulate the energy efficiency problem for UAVs as an optimization problem by considering different key performance indicators (KPIs) for the network, coexisting terrestrial users, and UAVs as aerial users. Then, leveraging tools from deep learning, we transform this problem into a deep queue learning problem and present a learning-powered solution that incorporates the KPIs of interest in the design of the reward function to solve energy efficiency maximization for aerial users while minimizing interference to terrestrial users. A broad set of simulations have been conducted in order to investigate how the altitude of UAVs, and the tolerable level of interference, shape the optimal energy-efficient policy in the network. Simulation results show that the proposed scheme achieves better energy and spectral efficiency for UAV and less interference to terrestrial users incurred from aerial users. The obtained results further provide insights on the benefits of leveraging intelligent energy-efficient scheme. For example, a significant increase in the energy efficiency of aerial users with respect to increases in their spectral efficiency, while a considerable decrease in incurred interference to the terrestrial users is achieved in comparison to the non-learning scheme.en
dc.description.versionPeer revieweden
dc.format.extent6
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationGhavimi , F & Jantti , R 2020 , Energy-Efficient UAV Communications with Interference Management: Deep Learning Framework . in 2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020 - Proceedings . , 9124759 , IEEE , IEEE Wireless Communications and Networking Conference , Seoul , Korea, Republic of , 25/05/2020 . https://doi.org/10.1109/WCNCW48565.2020.9124759en
dc.identifier.doi10.1109/WCNCW48565.2020.9124759en_US
dc.identifier.isbn9781728151786
dc.identifier.otherPURE UUID: 7c4638c2-3bb9-43d6-b82c-bac0a2ef1277en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/7c4638c2-3bb9-43d6-b82c-bac0a2ef1277en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85087908851&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/44909118/AALTO_Energy_Efficient_UAV_Communications_with_Interference_Management_Deep_Reinforcement_Learning_Framework.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/45680
dc.identifier.urnURN:NBN:fi:aalto-202008124694
dc.language.isoenen
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/815191/EU//PriMO-5Gen_US
dc.relation.ispartofIEEE Wireless Communications and Networking Conferenceen
dc.relation.ispartofseries2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020 - Proceedingsen
dc.rightsopenAccessen
dc.subject.keywordcellular networksen_US
dc.subject.keyworddeep reinforcement learningen_US
dc.subject.keyworddroneen_US
dc.subject.keywordEnergy efficiencyen_US
dc.subject.keywordinterference managementen_US
dc.subject.keywordmachine learningen_US
dc.subject.keywordunmanned aerial vehicle (UAV)en_US
dc.titleEnergy-Efficient UAV Communications with Interference Management: Deep Learning Frameworken
dc.typeConference article in proceedingsfi
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