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dc.contributor.authorBakirtzis, S
dc.contributor.authorQiu, K
dc.contributor.authorZhang, J
dc.contributor.authorWassell, I
dc.date.accessioned2022-07-07T23:30:07Z
dc.date.available2022-07-07T23:30:07Z
dc.date.issued2022
dc.identifier.isbn9788831299046
dc.identifier.issn2164-3342
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338889
dc.description.abstractEfficient and accurate indoor radio propagation modeling tools are essential for the design and operation of wireless communication systems. Lately, several attempts to combine radio propagation solvers with machine learning (ML) have been made. In this paper, motivated by the recent advances in the area of computer vision, we present a new ML propagation model using convolutional encoder-decoders. Specifically, we couple a ray-tracing simulator with either a U-Net or an SDU-Net, showing that the use of atrous convolutions utilized in SDU-Net can enhance significantly the performance of an ML propagation model. The proposed data-driven framework, called DeepRay, can be trained to predict the received signal strength in a given indoor environment. More importantly, once trained over multiple input geometries, DeepRay can be employed to directly predict the signal level for unknown indoor environments. We demonstrate this approach in various indoor environments using long range (LoRa) devices operating at 868 MHz.
dc.description.sponsorshipThis work was supported by European Commission through the Horizon 2020 Framework Programme, H2020-MSCA-ITN-2019, MSCA-ITN-EID, Grant No. 860239, BANYAN.
dc.publisherIEEE
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.titleDeepRay: Deep Learning Meets Ray-Tracing
dc.typeConference Object
dc.publisher.departmentDepartment of Computer Science and Technology
dc.date.updated2022-07-06T11:02:32Z
prism.endingPage5
prism.publicationDate2022
prism.publicationName2022 16th European Conference on Antennas and Propagation, EuCAP 2022
prism.startingPage1
dc.identifier.doi10.17863/CAM.86296
dcterms.dateAccepted2021-12-20
rioxxterms.versionofrecord10.23919/eucap53622.2022.9769203
rioxxterms.versionAM
dc.contributor.orcidWassell, Ian [0000-0001-7927-5565]
cam.issuedOnline2022-05-11
pubs.conference-name2022 16th European Conference on Antennas and Propagation (EuCAP)
pubs.conference-start-date2022-03-27
cam.orpheus.successMon Jul 11 08:50:21 BST 2022 - Embargo updated
cam.depositDate2022-07-06
pubs.conference-finish-date2022-04-01
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2023-03-27


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