Radio Access Technology characterisation through object detection

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dc.contributor.authorFonseca, Erikaen
dc.contributor.authorSantos, Joao F.en
dc.contributor.authorPaisana, Franciscoen
dc.contributor.authorDaSilva, Luiz A.en
dc.date.accessioned2022-01-04T20:18:23Zen
dc.date.available2022-01-04T20:18:23Zen
dc.date.issued2021-02-15en
dc.date.updated2022-01-04T20:18:21Zen
dc.description.abstractRadio Access Technology (RAT) classification and monitoring are essential for efficient coexistence of different communication systems in shared spectrum. Shared spectrum, including operation in license-exempt bands, is envisioned in the fifth generation of wireless technology (5G) standards (e.g., 3GPP Rel. 16). In this paper, we propose a Machine Learning (ML) approach to characterise the spectrum utilisation and facilitate the dynamic access to it. Recent advances in Convolutional Neural Networks (CNNs) enable us to perform waveform classification by processing spectrograms as images. In contrast to other ML methods that can only provide the class of the monitored RATs, the solution we propose can recognise not only different RATs in shared spectrum, but also identify critical parameters such as inter-frame duration, frame duration, centre frequency, and signal bandwidth by using object detection and a feature extraction module to extract features from spectrograms. We have implemented and evaluated our solution using a dataset of commercial transmissions, as well as in a Software-Defined Radio (SDR) testbed environment. The scenario evaluated was the coexistence of WiFi and LTE transmissions in shared spectrum. Our results show that our approach has an accuracy of 96% in the classification of RATs from a dataset that captures transmissions of regular user communications. It also shows that the extracted features can be precise within a margin of 2%, and can detect above 94% of objects under a broad range of transmission power levels and interference conditions.en
dc.description.versionAccepted versionen
dc.format.extentPages 12-19en
dc.format.extent8 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.comcom.2020.12.021en
dc.identifier.eissn1873-703Xen
dc.identifier.issn0140-3664en
dc.identifier.urihttp://hdl.handle.net/10919/107374en
dc.identifier.volume168en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000618345800002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectTechnologyen
dc.subjectComputer Science, Information Systemsen
dc.subjectEngineering, Electrical & Electronicen
dc.subjectTelecommunicationsen
dc.subjectComputer Scienceen
dc.subjectEngineeringen
dc.subjectDynamic spectrum accessen
dc.subjectSignal detectionen
dc.subjectObject detectionen
dc.subjectCognitive radioen
dc.subjectNetworking & Telecommunicationsen
dc.subject0805 Distributed Computingen
dc.subject0906 Electrical and Electronic Engineeringen
dc.subject1005 Communications Technologiesen
dc.titleRadio Access Technology characterisation through object detectionen
dc.title.serialComputer Communicationsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen

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