RIS-UAV Integration for Enhanced Coverage and Energy-Efficient 6G Wireless Networks

Alsenwi, M; Abolhasan, M; Lipman, J

RIS-UAV Integration for Enhanced Coverage and Energy-Efficient 6G Wireless Networks

Alsenwi, M Abolhasan, M

Lipman, J

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

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Field	Value	Language
dc.contributor.author	Alsenwi, M
dc.contributor.author	Abolhasan, M https://orcid.org/0000-0002-4282-6666
dc.contributor.author	Lipman, J https://orcid.org/0000-0003-2877-1168
dc.date.accessioned	2025-09-03T07:44:17Z
dc.date.available	2025-09-03T07:44:17Z
dc.date.issued	2025-01-01
dc.identifier.citation	IEEE Transactions on Green Communications and Networking, 2025, PP, (99), pp. 1-1
dc.identifier.issn	2473-2400
dc.identifier.issn	2473-2400
dc.identifier.uri	http://hdl.handle.net/10453/189579
dc.description.abstract	This paper studies the beamforming and trajectory design in unmanned aerial vehicles (UAV) assisted wireless networks, where a UAV equipped with a reconfigurable intelligent surface (RIS) flies over a selected area to reflect signals from the BS toward users experiencing link blockages. The objective is to improve energy efficiency while considering the minimum data rate requirements. In this regard, we define a stochastic optimization problem that optimizes both UAV trajectory and RIS phase shifts to maximize network energy efficiency while considering the achieved data rate by each user. A learning framework based on deep reinforcement learning (DRL) is proposed to solve the formulated problem. In the proposed algorithm, a dual computational approach is utilized, where extensive offline training is conducted on a central cloud server while an edge server performs online decision-making. This setup allows for efficient optimization of UAV trajectories and RIS phase shifts in response to the dynamically changing network conditions. The simulation outcomes highlight the proposed algorithm’s success in fulfilling the Qualit-of-Service (QoS) of each user, alongside augmenting the system’s energy efficiency.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Green Communications and Networking
dc.relation.isbasedon	10.1109/TGCN.2025.3573948
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4606 Distributed computing and systems software
dc.title	RIS-UAV Integration for Enhanced Coverage and Energy-Efficient 6G Wireless Networks
dc.type	Journal Article
utslib.citation.volume	PP
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/Transport Research Centre (TRC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.date.updated	2025-09-03T07:44:16Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

This paper studies the beamforming and trajectory design in unmanned aerial vehicles (UAV) assisted wireless networks, where a UAV equipped with a reconfigurable intelligent surface (RIS) flies over a selected area to reflect signals from the BS toward users experiencing link blockages. The objective is to improve energy efficiency while considering the minimum data rate requirements. In this regard, we define a stochastic optimization problem that optimizes both UAV trajectory and RIS phase shifts to maximize network energy efficiency while considering the achieved data rate by each user. A learning framework based on deep reinforcement learning (DRL) is proposed to solve the formulated problem. In the proposed algorithm, a dual computational approach is utilized, where extensive offline training is conducted on a central cloud server while an edge server performs online decision-making. This setup allows for efficient optimization of UAV trajectories and RIS phase shifts in response to the dynamically changing network conditions. The simulation outcomes highlight the proposed algorithm’s success in fulfilling the Qualit-of-Service (QoS) of each user, alongside augmenting the system’s energy efficiency.

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http://hdl.handle.net/10453/189579