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dc.contributor.advisorRomero, Daniel
dc.contributor.advisorBeferull-Lozano, Baltasar Enrique
dc.contributor.authorTeganya, Yves
dc.date.accessioned2020-11-10T22:16:54Z
dc.date.available2020-11-10T22:16:54Z
dc.date.created2020-10-20T11:31:29Z
dc.date.issued2020
dc.identifier.citationTeganya, Y. (2020). Machine Learning Tools for Radio Map Estimation in Fading-Impaired Channels (Doctoral thesis). University of Agder, Kristiansand.en_US
dc.identifier.isbn978-82-7117-999-1
dc.identifier.issn1504-9272
dc.identifier.urihttps://hdl.handle.net/11250/2687236
dc.description.abstractIn spectrum cartography, also known as radio map estimation, one constructs maps that provide the value of a given channel metric such as as the received power, power spectral density (PSD), electromagnetic absorption, or channel-gain for every spatial location in the geographic area of interest. The main idea is to deploy sensors and measure the target channel metric at a set of locations and interpolate or extrapolate the measurements. Radio maps nd a myriad of applications in wireless communications such as network planning, interference coordination, power control, spectrum management, resource allocation, handoff optimization, dynamic spectrum access, and cognitive radio. More recently, radio maps have been widely recognized as an enabling technology for unmanned aerial vehicle (UAV) communications because they allow autonomous UAVs to account for communication constraints when planning a mission. Additional use cases include radio tomography and source localization.en_US
dc.language.isoengen_US
dc.publisher07 Mediaen_US
dc.relation.ispartofseriesDoctoral Dissertations at the University of Agder; no. 297
dc.relation.haspartPaper I: Teganya, Y., Lopez-Ramos, L. M.; Romero, D. & Beferull-Lozano, B. (2018). Localization-Free Power Cartography. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (p. 3549-3553). IEEE. https://doi.org/10.1109/ICASSP.2018.8461731. Author´s accepted manuscript. Full-text is available in AURA as a separate file: http://hdl.handle.net/11250/2594807.en_US
dc.relation.haspartPaper II: Teganya, Y., Romero, D., Lopez-Ramos, L. M. & Beferull-Lozano, B. (2019). Location-free Spectrum Cartography. IEEE Transactions on Signal Processing, 67(15), 4013-4026. https://doi.org/10.1109/TSP.2019.2923151. Author´s accepted manuscript. Full-text is available in AURA as a separate file: https://hdl.handle.net/11250/2647926.en_US
dc.relation.haspartPaper III: Teganya, Y. & Romero, D. (2020). Data-Driven Spectrum Cartography via Deep Completion Autoencoders. In ICC 2020 - 2020 IEEE International Conference on Communications. https://doi.org/10.1109/ICC40277.2020.9149400. Author´s accepted manuscript. Full-text is available in AURA as a separate file: .en_US
dc.relation.haspartPaper IV: Teganya, Y. & Romero, D. (Forthcoming). Deep Completion Autoencoders for Radio Map Estimation. IEEE Transactions on Wireless Communications. https://arxiv.org/abs/2005.05964. Author´s submitted manuscript. Full-text is not available in AURA as a separate file.en_US
dc.titleMachine Learning Tools for Radio Map Estimation in Fading-Impaired Channelsen_US
dc.typeDoctoral thesisen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2020 Yves Teganyaen_US
dc.subject.nsiVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.source.pagenumber155en_US
dc.source.issue297en_US
dc.identifier.cristin1840821
dc.relation.projectNorges forskningsråd: 280835en_US
dc.relation.projectNorges forskningsråd: 250910/F20en_US


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