Joint User, Channel, Modulation-Coding Selection, and RIS Configuration for Jamming Resistance in Multiuser OFDMA Systems

Yuan, X; Hu, S; Ni, W; Liu, RP; Wang, X

Joint User, Channel, Modulation-Coding Selection, and RIS Configuration for Jamming Resistance in Multiuser OFDMA Systems

Yuan, X

Hu, S Ni, W

Liu, RP Wang, X

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Communications, 2023, 71, (3), pp. 1631-1645
Issue Date:: 2023-03-01

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Field	Value	Language
dc.contributor.author	Yuan, X https://orcid.org/0000-0002-9167-1613
dc.contributor.author	Hu, S
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Liu, RP
dc.contributor.author	Wang, X
dc.date.accessioned	2024-04-12T04:51:13Z
dc.date.available	2024-04-12T04:51:13Z
dc.date.issued	2023-03-01
dc.identifier.citation	IEEE Transactions on Communications, 2023, 71, (3), pp. 1631-1645
dc.identifier.issn	0090-6778
dc.identifier.issn	1558-0857
dc.identifier.uri	http://hdl.handle.net/10453/177829
dc.description.abstract	Reconfigurable intelligent surfaces (RISs) can potentially combat jamming. It is non-trivial to perform holistic selections of users, data streams, and modulation-coding modes for all subchannels, and RIS configuration in a downlink multiuser OFDMA system under jamming attacks, because of a mixed-integer program nature and difficulties in acquiring the channel state information (CSI) of the channels to and from the RIS and from an uncooperative jammer. We propose a new deep reinforcement learning (DRL)-based approach that learns through changes in the data rates of the users to reject jamming and maximize the sum rate. The key idea is to decouple the continuous RIS configuration from the discrete selections of users, data streams, subchannels, and modulation-coding modes. Another critical aspect is that we show the optimal selections almost surely follow a winner-takes-all strategy. Accordingly, the new DRL framework learns the RIS configuration with a twin-delayed deep deterministic policy gradient and takes the winner-takes-all strategy to evaluate the reward, thereby reducing the action space and accelerating learning. Simulations show the framework converges fast and fulfills the benefit of the RIS. With no need for the CSI of the channels to and from the RIS and from the jammer, the framework offers practical value.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Communications
dc.relation.isbasedon	10.1109/TCOMM.2023.3238062
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0804 Data Format, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Joint User, Channel, Modulation-Coding Selection, and RIS Configuration for Jamming Resistance in Multiuser OFDMA Systems
dc.type	Journal Article
utslib.citation.volume	71
utslib.for	0804 Data Format
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/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
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	in_progress	*
dc.date.updated	2024-04-12T04:51:11Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	71
utslib.citation.issue	3

Abstract:

Reconfigurable intelligent surfaces (RISs) can potentially combat jamming. It is non-trivial to perform holistic selections of users, data streams, and modulation-coding modes for all subchannels, and RIS configuration in a downlink multiuser OFDMA system under jamming attacks, because of a mixed-integer program nature and difficulties in acquiring the channel state information (CSI) of the channels to and from the RIS and from an uncooperative jammer. We propose a new deep reinforcement learning (DRL)-based approach that learns through changes in the data rates of the users to reject jamming and maximize the sum rate. The key idea is to decouple the continuous RIS configuration from the discrete selections of users, data streams, subchannels, and modulation-coding modes. Another critical aspect is that we show the optimal selections almost surely follow a winner-takes-all strategy. Accordingly, the new DRL framework learns the RIS configuration with a twin-delayed deep deterministic policy gradient and takes the winner-takes-all strategy to evaluate the reward, thereby reducing the action space and accelerating learning. Simulations show the framework converges fast and fulfills the benefit of the RIS. With no need for the CSI of the channels to and from the RIS and from the jammer, the framework offers practical value.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/177829