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dc.contributor.authorShrestha, Raju
dc.contributor.authorHa, Tien Ngoc
dc.contributor.authorPham, Viet Quoc
dc.contributor.authorRomero, Daniel
dc.date.accessioned2024-02-08T12:37:03Z
dc.date.available2024-02-08T12:37:03Z
dc.date.created2024-01-23T16:30:57Z
dc.date.issued2023
dc.identifier.citationShrestha, R., Ha, T. N., Pham, V. Q. & Romero, D. (2023). Radio Map Estimation in the Real-World : Empirical Validation and Analysis. IEEE Conference on Antenna Measurements and Applications, 169-174.en_US
dc.identifier.isbn979-8-3503-2304-7
dc.identifier.issn2643-6795
dc.identifier.urihttps://hdl.handle.net/11250/3116391
dc.descriptionAuthor's accepted manuscript.en_US
dc.description© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractRadio maps quantify received signal strength or other magnitudes of the radio frequency environment at every point of a geographical region. These maps play a vital role in a large number of applications such as wireless network planning, spectrum management, and optimization of communication systems. However, empirical validation of the large number of existing radio map estimators is highly limited. To fill this gap, a large data set of measurements has been collected with an autonomous unmanned aerial vehicle (UAV) and a representative subset of these estimators were evaluated on this data. The performance-complexity trade-off and the impact of fast fading are extensively investigated. Although sophisticated estimators based on deep neural networks (DNNs) exhibit the best performance, they are seen to require large volumes of training data to offer a substantial advantage relative to more traditional schemes. A novel algorithm that blends both kinds of estimators is seen to enjoy the benefits of both, thereby suggesting the potential of exploring this research direction further.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleRadio Map Estimation in the Real-World : Empirical Validation and Analysisen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© 2023 IEEEen_US
dc.subject.nsiVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.source.pagenumber169-174en_US
dc.source.journalIEEE Conference on Antenna Measurements and Applicationsen_US
dc.identifier.doihttps://doi.org/10.1109/CAMA57522.2023.10352759
dc.identifier.cristin2233190
dc.relation.projectNorges forskningsråd: 311994en_US
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal