Protecting multi-function wireless systems from jammers with backscatter assistance: An intelligent strategy

Lotfi, I; Niyato, D; Sun, S; Dinh, HT; Li, Y; Kim, DI

Protecting multi-function wireless systems from jammers with backscatter assistance: An intelligent strategy

Lotfi, I Niyato, D Sun, S Dinh, HT Li, Y Kim, DI

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Vehicular Technology, 2021, 70, (11), pp. 11812-11826
Issue Date:: 2021-11-01

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Field	Value	Language
dc.contributor.author	Lotfi, I
dc.contributor.author	Niyato, D
dc.contributor.author	Sun, S
dc.contributor.author	Dinh, HT
dc.contributor.author	Li, Y
dc.contributor.author	Kim, DI
dc.date.accessioned	2022-03-27T00:58:42Z
dc.date.available	2022-03-27T00:58:42Z
dc.date.issued	2021-11-01
dc.identifier.citation	IEEE Transactions on Vehicular Technology, 2021, 70, (11), pp. 11812-11826
dc.identifier.issn	0018-9545
dc.identifier.issn	1939-9359
dc.identifier.uri	http://hdl.handle.net/10453/155597
dc.description.abstract	In this paper, we present a novel unified framework to protect multi-function wireless systems from jamming attacks. Examples of such multi-function system include joint radar and communication (JRC) systems and simultaneous wireless information and power transfer (SWIPT) systems. By abstracting the system functionalities as a joint optimization problem of multiple queues, we achieve effective resistance against jammers for the multi-functions simultaneously.We incorporate different antijamming techniques into one framework. Deception mechanism is adopted to lure the jammer to attack and make its actions more predictable, and ambient backscatter technology is used to leverage the jamming signals. Since conventional Markov decision process (MDP) has only one decision epoch at every time slot, it cannot be used to model the deception strategy which needs two decision epochs to leverage the jamming signals. We therefore formulate the problem using an advanced two-step MDP. After that, a deep reinforcement learning algorithm with a prioritized double deep Q-Learning architecture is proposed to learn optimal strategies in different system states. We show that by jointly considering the multi-functions of the system with potential jamming attacks during design phase, significant improvement can be achieved for both of the system functionalities.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/DE210100651
dc.relation.ispartof	IEEE Transactions on Vehicular Technology
dc.relation.isbasedon	10.1109/TVT.2021.3115474
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Automobile Design & Engineering
dc.title	Protecting multi-function wireless systems from jammers with backscatter assistance: An intelligent strategy
dc.type	Journal Article
utslib.citation.volume	70
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
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/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2022-03-27T00:58:36Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	70
utslib.citation.issue	11

Abstract:

In this paper, we present a novel unified framework to protect multi-function wireless systems from jamming attacks. Examples of such multi-function system include joint radar and communication (JRC) systems and simultaneous wireless information and power transfer (SWIPT) systems. By abstracting the system functionalities as a joint optimization problem of multiple queues, we achieve effective resistance against jammers for the multi-functions simultaneously.We incorporate different antijamming techniques into one framework. Deception mechanism is adopted to lure the jammer to attack and make its actions more predictable, and ambient backscatter technology is used to leverage the jamming signals. Since conventional Markov decision process (MDP) has only one decision epoch at every time slot, it cannot be used to model the deception strategy which needs two decision epochs to leverage the jamming signals. We therefore formulate the problem using an advanced two-step MDP. After that, a deep reinforcement learning algorithm with a prioritized double deep Q-Learning architecture is proposed to learn optimal strategies in different system states. We show that by jointly considering the multi-functions of the system with potential jamming attacks during design phase, significant improvement can be achieved for both of the system functionalities.

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http://hdl.handle.net/10453/155597