Enabling Secure and Reliable Wireless Services With Intelligent Reflecting Surfaces

Abughalwa, Monir

Enabling Secure and Reliable Wireless Services With Intelligent Reflecting Surfaces

Abughalwa, Monir

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Publication Type:: Thesis
Issue Date:: 2025

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Field	Value	Language
dc.contributor.author	Abughalwa, Monir
dc.date.accessioned	2026-05-28T07:51:48Z
dc.date.available	2026-05-28T07:51:48Z
dc.date.issued	2025
dc.identifier.uri	http://hdl.handle.net/10453/195186
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	The forthcoming sixth-generation (6G) mobile networks are expected to deliver ultra-fast, low-latency, and highly reliable connectivity, supporting applications in smart cities, healthcare, transportation, and industry. However, 6G systems face major challenges, including extremely low latency, high reliability, and security requirements. Intelligent reflecting surfaces (IRS) have emerged as a promising technology to improve user data rates and secrecy by enhancing the received signal of legitimate users while limiting eavesdroppers’ interception. This thesis investigates IRS-assisted secure communication through joint optimization of the transmitter beamforming vector and the IRS programmable reflecting elements (PREs), considering practical constraints such as low-resolution IRS, imperfect channel state information (CSI), and different eavesdropper CSI assumptions (perfect, imperfect, or unknown). First, we study data rate maximization in a downlink IRS-aided system under the finite blocklength regime (FBR), where a base station serves multiple single-antenna users. Since achievable rates in the FBR are intricate functions of beamforming and IRS phase shifts, we propose a joint optimization framework that maximizes the geometric mean of user rates. A computationally efficient algorithm based on closed-form approximations is developed, and simulations confirm its effectiveness. Second, we address users’ secrecy in long blocklength (LBR) IRS-aided systems with low-resolution IRS. The objective is to maximize the minimum secrecy rate among all users under different CSI conditions. The resulting nonconvex problem is tackled by linearizing its objective function and then decomposing it into a series of tractable subproblems. For imperfect CSI, we use the successive convex approximation (SCA) method, and S-procedure to tackle the problem. Extensive simulations under practical settings validate the efficacy of the proposed framework. Finally, we extend the study to users’ secrecy in FBR-IRS-aided systems, where enhancing users’ secrecy is more challenging due to latency and reliability FBR constraints. We formulate problems to maximize both the minimum and sum secrecy rates while satisfying FBR constraints, by jointly optimizing the beamform and the IRS PREs. We address the nonconvex problems using linearization and the SCA technique. Extensive simulations under practical conditions demonstrate that, when it is feasible, the proposed framework can reliably ensure secure communications for all users under FBR constraints. In conclusion, this thesis develops robust and scalable optimization frameworks for data rate maximization and secrecy provisioning in IRS-aided 6G networks under both LBR and FBR regimes. The proposed methods provide scalable solutions to practical settings with large IRSs, enabling secure, reliable, and high-rate communication in future 6G systems.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/195186/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2025 Monir Abughalwa
dc.rights	au.edu.uts.lib/cph
dc.title	Enabling Secure and Reliable Wireless Services With Intelligent Reflecting Surfaces	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

The forthcoming sixth-generation (6G) mobile networks are expected to deliver ultra-fast, low-latency, and highly reliable connectivity, supporting applications in smart cities, healthcare, transportation, and industry. However, 6G systems face major challenges, including extremely low latency, high reliability, and security requirements. Intelligent reflecting surfaces (IRS) have emerged as a promising technology to improve user data rates and secrecy by enhancing the received signal of legitimate users while limiting eavesdroppers’ interception. This thesis investigates IRS-assisted secure communication through joint optimization of the transmitter beamforming vector and the IRS programmable reflecting elements (PREs), considering practical constraints such as low-resolution IRS, imperfect channel state information (CSI), and different eavesdropper CSI assumptions (perfect, imperfect, or unknown). First, we study data rate maximization in a downlink IRS-aided system under the finite blocklength regime (FBR), where a base station serves multiple single-antenna users. Since achievable rates in the FBR are intricate functions of beamforming and IRS phase shifts, we propose a joint optimization framework that maximizes the geometric mean of user rates. A computationally efficient algorithm based on closed-form approximations is developed, and simulations confirm its effectiveness. Second, we address users’ secrecy in long blocklength (LBR) IRS-aided systems with low-resolution IRS. The objective is to maximize the minimum secrecy rate among all users under different CSI conditions. The resulting nonconvex problem is tackled by linearizing its objective function and then decomposing it into a series of tractable subproblems. For imperfect CSI, we use the successive convex approximation (SCA) method, and S-procedure to tackle the problem. Extensive simulations under practical settings validate the efficacy of the proposed framework. Finally, we extend the study to users’ secrecy in FBR-IRS-aided systems, where enhancing users’ secrecy is more challenging due to latency and reliability FBR constraints. We formulate problems to maximize both the minimum and sum secrecy rates while satisfying FBR constraints, by jointly optimizing the beamform and the IRS PREs. We address the nonconvex problems using linearization and the SCA technique. Extensive simulations under practical conditions demonstrate that, when it is feasible, the proposed framework can reliably ensure secure communications for all users under FBR constraints. In conclusion, this thesis develops robust and scalable optimization frameworks for data rate maximization and secrecy provisioning in IRS-aided 6G networks under both LBR and FBR regimes. The proposed methods provide scalable solutions to practical settings with large IRSs, enabling secure, reliable, and high-rate communication in future 6G systems.

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