Misbehavior Detection in Wi-Fi/LTE Coexistence over Unlicensed Bands

Samy, I; Han, X; Lazos, L; Li, M; Xiao, Y; Krunz, M

Misbehavior Detection in Wi-Fi/LTE Coexistence over Unlicensed Bands

Samy, I Han, X Lazos, L Li, M Xiao, Y Krunz, M

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Publisher:: IEEE COMPUTER SOC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Mobile Computing, 2023, 22, (8), pp. 4773-4791
Issue Date:: 2023

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Field	Value	Language
dc.contributor.author	Samy, I
dc.contributor.author	Han, X
dc.contributor.author	Lazos, L
dc.contributor.author	Li, M
dc.contributor.author	Xiao, Y
dc.contributor.author	Krunz, M
dc.date.accessioned	2024-03-22T00:42:15Z
dc.date.available	2024-03-22T00:42:15Z
dc.date.issued	2023
dc.identifier.citation	IEEE Transactions on Mobile Computing, 2023, 22, (8), pp. 4773-4791
dc.identifier.issn	1536-1233
dc.identifier.issn	1558-0660
dc.identifier.uri	http://hdl.handle.net/10453/177001
dc.description.abstract	We address the problem of detecting misbehavior in the coexistence between LTE and Wi-Fi systems operating in the 5 GHz U-NII unlicensed bands. We define selfish misbehavior strategies for the LTE that can yield an unfair share of the spectrum resources. Such strategies are based on manipulating the operational parameters of the LTE-LAA standard. Prior methods for detecting misbehavior in homogeneous settings are not applicable in a spectrum sharing scenario because the devices of one system cannot decode the transmissions of another. We develop implicit sensing techniques that can accurately estimate the operational parameters of LTE transmissions under various topological scenarios and {\em without decoding.} These techniques apply correlation-based signal detection to infer the required information. Our techniques are validated through experiments on a USRP testbed. We further apply a statistical inference framework for determining deviations of the LTE behavior from the coexistence etiquette. By characterizing the detection and false alarm probabilities, we show that our framework yields high detection accuracy at a very low false alarm rate. Although our methods focus on detecting misbehavior of the LTE system, they can be generalized to detect Wi-Fi misbehavior and to other coexistence scenarios.
dc.language	English
dc.publisher	IEEE COMPUTER SOC
dc.relation.ispartof	IEEE Transactions on Mobile Computing
dc.relation.isbasedon	10.1109/TMC.2022.3164326
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4604 Cybersecurity and privacy
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Misbehavior Detection in Wi-Fi/LTE Coexistence over Unlicensed Bands
dc.type	Journal Article
utslib.citation.volume	22
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
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	2024-03-22T00:42:13Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	22
utslib.citation.issue	8

Abstract:

We address the problem of detecting misbehavior in the coexistence between LTE and Wi-Fi systems operating in the 5 GHz U-NII unlicensed bands. We define selfish misbehavior strategies for the LTE that can yield an unfair share of the spectrum resources. Such strategies are based on manipulating the operational parameters of the LTE-LAA standard. Prior methods for detecting misbehavior in homogeneous settings are not applicable in a spectrum sharing scenario because the devices of one system cannot decode the transmissions of another. We develop implicit sensing techniques that can accurately estimate the operational parameters of LTE transmissions under various topological scenarios and {\em without decoding.} These techniques apply correlation-based signal detection to infer the required information. Our techniques are validated through experiments on a USRP testbed. We further apply a statistical inference framework for determining deviations of the LTE behavior from the coexistence etiquette. By characterizing the detection and false alarm probabilities, we show that our framework yields high detection accuracy at a very low false alarm rate. Although our methods focus on detecting misbehavior of the LTE system, they can be generalized to detect Wi-Fi misbehavior and to other coexistence scenarios.

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http://hdl.handle.net/10453/177001