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

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Ghavimi, Fayezeh
dc.contributor.author Jantti, Riku
dc.date.accessioned 2020-08-12T09:12:23Z
dc.date.available 2020-08-12T09:12:23Z
dc.date.issued 2020-04
dc.identifier.citation Ghavimi , 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/WCNCW48565.2020.9124759 en
dc.identifier.isbn 9781728151786
dc.identifier.other PURE UUID: 7c4638c2-3bb9-43d6-b82c-bac0a2ef1277
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/7c4638c2-3bb9-43d6-b82c-bac0a2ef1277
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85087908851&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/44909118/AALTO_Energy_Efficient_UAV_Communications_with_Interference_Management_Deep_Reinforcement_Learning_Framework.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/45680
dc.description | openaire: EC/H2020/815191/EU//PriMO-5G
dc.description.abstract In 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.format.extent 6
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation info:eu-repo/grantAgreement/EC/H2020/815191/EU//PriMO-5G
dc.relation.ispartof IEEE Wireless Communications and Networking Conference en
dc.relation.ispartofseries 2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020 - Proceedings en
dc.rights openAccess en
dc.title Energy-Efficient UAV Communications with Interference Management: Deep Learning Framework en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Communications and Networking
dc.subject.keyword cellular networks
dc.subject.keyword deep reinforcement learning
dc.subject.keyword drone
dc.subject.keyword Energy efficiency
dc.subject.keyword interference management
dc.subject.keyword machine learning
dc.subject.keyword unmanned aerial vehicle (UAV)
dc.identifier.urn URN:NBN:fi:aalto-202008124694
dc.identifier.doi 10.1109/WCNCW48565.2020.9124759

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