Intelligent-CW: AI-based Framework for Controlling Contention Window in WLANs

Abyaneh, AHY; Hirzallah, M; Krunz, M

Intelligent-CW: AI-based Framework for Controlling Contention Window in WLANs

Abyaneh, AHY Hirzallah, M Krunz, M

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2019, 2019
Issue Date:: 2019-11-01

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Field	Value	Language
dc.contributor.author	Abyaneh, AHY
dc.contributor.author	Hirzallah, M
dc.contributor.author	Krunz, M
dc.date	2019-11-11
dc.date.accessioned	2020-04-30T20:59:26Z
dc.date.available	2020-04-30T20:59:26Z
dc.date.issued	2019-11-01
dc.identifier.citation	2019 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2019, 2019
dc.identifier.isbn	9781728123769
dc.identifier.issn	2334-3125
dc.identifier.uri	http://hdl.handle.net/10453/140367
dc.description.abstract	© 2019 IEEE. The heterogeneity of technologies that operate over the unlicensed 5 GHz spectrum, such as LTE-Licensed-Assisted-Access (LAA), 5G New Radio Unlicensed (NR-U), and WiFi, calls for more intelligent and efficient techniques to coordinate channel access beyond what current standards offer. Wi-Fi standards require nodes to adopt a fixed value for the minimum contention window (CW{min}), which prohibits a node from reacting to aggressive nodes that set their CWmin to small values. To address this problem, we propose a framework called Intelligent-CW (ICW) that allows nodes to adapt their CWmin values based on observed transmissions, ensuring they receive their fair share of the channel airtime. The CWmin value at a node is set based on a random forest, a machine learning model that includes a large number of decision trees. We train the random forest in a supervised manner over a large number of WLAN scenarios, including different misbehaving and aggressive scenarios. Under aggressive scenarios, our simulation results reveal that ICW provides nodes with higher throughput (153.9% gain) and 64% lower frame latency than standard techniques. In order to measure the fairness contribution of individual nodes, we introduce a new fairness metric. Based on this metric, ICW is shown to provide 10. 89 times improvement in fairness in aggressive scenarios compared to standard techniques.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2019
dc.relation.ispartof	IEEE International Symposium on Dynamic Spectrum Access Networks
dc.relation.isbasedon	10.1109/DySPAN.2019.8935851
dc.rights	© 2019 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.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Intelligent-CW: AI-based Framework for Controlling Contention Window in WLANs
dc.type	Conference Proceeding
utslib.location.activity	Newark, NJ, USA
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-04-30T20:59:13Z
pubs.finish-date	2019-11-14
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-11-11
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. The heterogeneity of technologies that operate over the unlicensed 5 GHz spectrum, such as LTE-Licensed-Assisted-Access (LAA), 5G New Radio Unlicensed (NR-U), and WiFi, calls for more intelligent and efficient techniques to coordinate channel access beyond what current standards offer. Wi-Fi standards require nodes to adopt a fixed value for the minimum contention window (CW{min}), which prohibits a node from reacting to aggressive nodes that set their CWmin to small values. To address this problem, we propose a framework called Intelligent-CW (ICW) that allows nodes to adapt their CWmin values based on observed transmissions, ensuring they receive their fair share of the channel airtime. The CWmin value at a node is set based on a random forest, a machine learning model that includes a large number of decision trees. We train the random forest in a supervised manner over a large number of WLAN scenarios, including different misbehaving and aggressive scenarios. Under aggressive scenarios, our simulation results reveal that ICW provides nodes with higher throughput (153.9% gain) and 64% lower frame latency than standard techniques. In order to measure the fairness contribution of individual nodes, we introduce a new fairness metric. Based on this metric, ICW is shown to provide 10. 89 times improvement in fairness in aggressive scenarios compared to standard techniques.

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http://hdl.handle.net/10453/140367