Deep Reinforcement Learning-Driven Secrecy Design for Intelligent Reflecting Surface-Based 6G-IoT Networks

Saleem, R; Ni, W; Ikram, M; Jamalipour, A

Deep Reinforcement Learning-Driven Secrecy Design for Intelligent Reflecting Surface-Based 6G-IoT Networks

Saleem, R Ni, W

Ikram, M Jamalipour, A

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2022, PP, (99), pp. 1-1
Issue Date:: 2022-01-01

Closed Access

	Filename	Description	Size
	Deep_Reinforcement_Learning-Driven_Secrecy_Design_for_Intelligent_Reflecting_Surface-Based_6G-IoT_Networks.pdf	Published version	1.26 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Saleem, R
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Ikram, M
dc.contributor.author	Jamalipour, A
dc.date.accessioned	2023-03-22T21:33:58Z
dc.date.available	2023-03-22T21:33:58Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Internet of Things Journal, 2022, PP, (99), pp. 1-1
dc.identifier.issn	2372-2541
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/168068
dc.description.abstract	The sixth-generation (6G) wireless communication has called for higher bandwidth and massive connectivity of Internet-of-Things (IoT) devices. The increased connectivity also demands advanced levels of network security, which are critical to maintaining due to severe signal attenuation at higher frequencies. Intelligent reflecting surface (IRS) is an increasingly popular, efficient, solution to cater to higher data rates, better coverage range, and reduced signal blockages. In this paper, an IRS-based model is proposed to address the issue of network security under trusted-untrusted device diversity, where the untrusted devices may potentially eavesdrop on the trusted devices. A mathematical design of the system model is presented, and an optimization problem is formulated. The secrecy rate of the trusted devices is maximized while guaranteeing Quality-of-Service (QoS) to all the legitimate, trusted and untrusted devices. A deep deterministic policy gradient (DDPG) algorithm is devised to jointly optimize the active and passive beamforming matrices owing to the complex and continuous nature of action and state spaces. The results confirm a maximum gain of 2-2.5 times in the sum secrecy rate of trusted devices under the proposed model, as compared to the benchmark cases. The results also ensure the throughput performance of all trusted and untrusted devices. The performance of the proposed DDPG model is evaluated under meticulously selected hyper-parameters.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2022.3232360
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.title	Deep Reinforcement Learning-Driven Secrecy Design for Intelligent Reflecting Surface-Based 6G-IoT Networks
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0805 Distributed Computing
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	*
dc.date.updated	2023-03-22T21:33:57Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

The sixth-generation (6G) wireless communication has called for higher bandwidth and massive connectivity of Internet-of-Things (IoT) devices. The increased connectivity also demands advanced levels of network security, which are critical to maintaining due to severe signal attenuation at higher frequencies. Intelligent reflecting surface (IRS) is an increasingly popular, efficient, solution to cater to higher data rates, better coverage range, and reduced signal blockages. In this paper, an IRS-based model is proposed to address the issue of network security under trusted-untrusted device diversity, where the untrusted devices may potentially eavesdrop on the trusted devices. A mathematical design of the system model is presented, and an optimization problem is formulated. The secrecy rate of the trusted devices is maximized while guaranteeing Quality-of-Service (QoS) to all the legitimate, trusted and untrusted devices. A deep deterministic policy gradient (DDPG) algorithm is devised to jointly optimize the active and passive beamforming matrices owing to the complex and continuous nature of action and state spaces. The results confirm a maximum gain of 2-2.5 times in the sum secrecy rate of trusted devices under the proposed model, as compared to the benchmark cases. The results also ensure the throughput performance of all trusted and untrusted devices. The performance of the proposed DDPG model is evaluated under meticulously selected hyper-parameters.

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

http://hdl.handle.net/10453/168068