Intelligent Tracking of Network Dynamics for Cross-Technology Coexistence Over Unlicensed Bands

Hirzallah, M; Krunz, M

Intelligent Tracking of Network Dynamics for Cross-Technology Coexistence Over Unlicensed Bands

Hirzallah, M Krunz, M

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 International Conference on Computing, Networking and Communications (ICNC), 2020, 00, pp. 698-703
Issue Date:: 2020-03-30

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Field	Value	Language
dc.contributor.author	Hirzallah, M
dc.contributor.author	Krunz, M
dc.date	2020-02-17
dc.date.accessioned	2021-05-21T02:48:46Z
dc.date.available	2021-05-21T02:48:46Z
dc.date.issued	2020-03-30
dc.identifier.citation	2020 International Conference on Computing, Networking and Communications (ICNC), 2020, 00, pp. 698-703
dc.identifier.isbn	978-1-7281-4906-6
dc.identifier.issn	2325-2626
dc.identifier.uri	http://hdl.handle.net/10453/149038
dc.description.abstract	Unlicensed bands offer great opportunities for numerous wireless technologies, including IEEE 802.11-based systems, 4G Licensed-Assisted-Access (LAA), and 5G New Radio Unlicensed (NR-U) networks. Achieving harmonious coexistence between these technologies requires real-time adaptation of their channel access, which can be facilitated by artificial intelligence (AI) and machine learning (ML) techniques. However, to leverage such techniques, we need to characterize the state of unlicensed wireless channel and the dynamics of the coexisting systems. In this paper, we introduce the concept of Sensing Fingerprint (SF) profile to characterize the state of coexisting networks and track their dynamics over unlicensed bands. We conduct extensive experiments to show the effectiveness of SF profile in tracking key network dynamics, including sensitivity thresholds of contending devices, their mobility, traffic loads, and other channel access parameters. AI-and ML-based controllers can utilize this tool to model the state of coexisting networks and track their dynamics.
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/icnc47757.2020.9049660
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Intelligent Tracking of Network Dynamics for Cross-Technology Coexistence Over Unlicensed Bands
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	2021-05-21T02:48:44Z
pubs.finish-date	2020-02-20
pubs.publication-status	Published
pubs.start-date	2020-02-17
pubs.volume	00

Abstract:

Unlicensed bands offer great opportunities for numerous wireless technologies, including IEEE 802.11-based systems, 4G Licensed-Assisted-Access (LAA), and 5G New Radio Unlicensed (NR-U) networks. Achieving harmonious coexistence between these technologies requires real-time adaptation of their channel access, which can be facilitated by artificial intelligence (AI) and machine learning (ML) techniques. However, to leverage such techniques, we need to characterize the state of unlicensed wireless channel and the dynamics of the coexisting systems. In this paper, we introduce the concept of Sensing Fingerprint (SF) profile to characterize the state of coexisting networks and track their dynamics over unlicensed bands. We conduct extensive experiments to show the effectiveness of SF profile in tracking key network dynamics, including sensitivity thresholds of contending devices, their mobility, traffic loads, and other channel access parameters. AI-and ML-based controllers can utilize this tool to model the state of coexisting networks and track their dynamics.

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http://hdl.handle.net/10453/149038