Adaptive Nullification of Multiple Correlated Jammers Using Deep Reinforcement Learning

Hoang, LM; Nguyen, DN; Zhang, JA; Hoang, DT

Adaptive Nullification of Multiple Correlated Jammers Using Deep Reinforcement Learning

Hoang, LM Nguyen, DN Zhang, JA Hoang, DT

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Vehicular Technology, 2025, PP, (99), pp. 1-14
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Hoang, LM
dc.contributor.author	Nguyen, DN
dc.contributor.author	Zhang, JA
dc.contributor.author	Hoang, DT
dc.date.accessioned	2025-07-11T02:21:44Z
dc.date.available	2025-07-11T02:21:44Z
dc.date.issued	2025-01-01
dc.identifier.citation	IEEE Transactions on Vehicular Technology, 2025, PP, (99), pp. 1-14
dc.identifier.issn	0018-9545
dc.identifier.issn	1939-9359
dc.identifier.uri	http://hdl.handle.net/10453/188175
dc.description.abstract	Nullifying/suppressing the jamming signals can be problematic when the correlation between transmitted jamming signals is deliberately varied over time, making the nullspace estimation of the jamming channel much more challenging. This is because the time-varying correlation between transmitted jamming signals causes an impact on the nullspace estimation similar to that caused by a time-varying jamming channel. Most existing interference nullification solutions only consider unchanged correlations or heuristically adapt to the time-varying correlation by continuously monitoring the residual jamming signals before updating the estimated (nullifying) beam-forming matrix for the data transmission phase. In this paper, we systematically formulate the optimization problem of the nullspace estimation and data transmission phases. Then, to deal with the uncertainty and incomplete information caused by the jammers that deliberately vary their jamming strategy, we reformulate the problem using a partially observable semi-Markov decision process (POSMDP). We then design a deep dueling Q-learning-based framework to optimally tune the duration of the nullspace estimation and data transmission phases. Extensive simulations demonstrate that the proposed techniques effectively deal with time-varying correlated jamming signals.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Vehicular Technology
dc.relation.isbasedon	10.1109/TVT.2025.3564410
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Automobile Design & Engineering
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Adaptive Nullification of Multiple Correlated Jammers Using Deep Reinforcement Learning
dc.type	Journal Article
utslib.citation.volume	PP
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Global Big Data Technologies Centre (GBDTC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Cyber Security and Privacy (CCSP)
utslib.copyright.status	in_progress	*
dc.date.updated	2025-07-11T02:21:42Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Nullifying/suppressing the jamming signals can be problematic when the correlation between transmitted jamming signals is deliberately varied over time, making the nullspace estimation of the jamming channel much more challenging. This is because the time-varying correlation between transmitted jamming signals causes an impact on the nullspace estimation similar to that caused by a time-varying jamming channel. Most existing interference nullification solutions only consider unchanged correlations or heuristically adapt to the time-varying correlation by continuously monitoring the residual jamming signals before updating the estimated (nullifying) beam-forming matrix for the data transmission phase. In this paper, we systematically formulate the optimization problem of the nullspace estimation and data transmission phases. Then, to deal with the uncertainty and incomplete information caused by the jammers that deliberately vary their jamming strategy, we reformulate the problem using a partially observable semi-Markov decision process (POSMDP). We then design a deep dueling Q-learning-based framework to optimally tune the duration of the nullspace estimation and data transmission phases. Extensive simulations demonstrate that the proposed techniques effectively deal with time-varying correlated jamming signals.

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http://hdl.handle.net/10453/188175