Deep Learning Based Identification of Wireless Protocols in the PHY layer

Berian, A; Aykin, I; Krunz, M; Bose, T

Deep Learning Based Identification of Wireless Protocols in the PHY layer

Berian, A Aykin, I Krunz, M Bose, T

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 International Conference on Computing, Networking and Communications (ICNC), 2020, 00, pp. 287-293
Issue Date:: 2020-03-30

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Field	Value	Language
dc.contributor.author	Berian, A
dc.contributor.author	Aykin, I
dc.contributor.author	Krunz, M
dc.contributor.author	Bose, T
dc.date	2020-02-17
dc.date.accessioned	2021-05-20T06:02:07Z
dc.date.available	2021-05-20T06:02:07Z
dc.date.issued	2020-03-30
dc.identifier.citation	2020 International Conference on Computing, Networking and Communications (ICNC), 2020, 00, pp. 287-293
dc.identifier.isbn	978-1-7281-4906-6
dc.identifier.issn	2325-2626
dc.identifier.uri	http://hdl.handle.net/10453/149014
dc.description.abstract	The need for Artificial Intelligence algorithms for future Cognitive Radio (CR) systems is unavoidable. For a CR to operate as best as possible it must identify who is present in spectrum of interest, and what they are doing (jamming, communicating, rogue transmission, etc.). Using this information, a CR can accordingly decide what to do next. Furthermore, being able to determine which wireless protocols are occupying spectrum is an important ability in heterogeneous wireless networks. In this work, we investigate the robustness of various Neural Network (NN) algorithms for classification of wireless protocols when looking at base-band In-phase/Quadrature (IQ) data without needing to decode. We propose a spectrum sensing algorithm based on NNs or other similarly behaved classification algorithms for identifying wireless technologies occupying spectrum. In previous literature, using base-band IQ data, researchers have shown that NN models can classify different modulation formats with promising accuracy. This work explores the potentials, usage, and limitations of using base-band IQ data for classifying various wireless network protocols that employ the same modulation format.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 International Conference on Computing, Networking and Communications (ICNC)
dc.relation.ispartof	International Conference on Computing, Networking and Communications
dc.relation.isbasedon	10.1109/icnc47757.2020.9049732
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Deep Learning Based Identification of Wireless Protocols in the PHY layer
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Big Island, HI, USA
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-20T06:02:05Z
pubs.finish-date	2020-02-20
pubs.publication-status	Published
pubs.start-date	2020-02-17
pubs.volume	00

Abstract:

The need for Artificial Intelligence algorithms for future Cognitive Radio (CR) systems is unavoidable. For a CR to operate as best as possible it must identify who is present in spectrum of interest, and what they are doing (jamming, communicating, rogue transmission, etc.). Using this information, a CR can accordingly decide what to do next. Furthermore, being able to determine which wireless protocols are occupying spectrum is an important ability in heterogeneous wireless networks. In this work, we investigate the robustness of various Neural Network (NN) algorithms for classification of wireless protocols when looking at base-band In-phase/Quadrature (IQ) data without needing to decode. We propose a spectrum sensing algorithm based on NNs or other similarly behaved classification algorithms for identifying wireless technologies occupying spectrum. In previous literature, using base-band IQ data, researchers have shown that NN models can classify different modulation formats with promising accuracy. This work explores the potentials, usage, and limitations of using base-band IQ data for classifying various wireless network protocols that employ the same modulation format.

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http://hdl.handle.net/10453/149014