Robust Beamforming Optimization for Self-Sustainable Intelligent Reflecting Surface Assisted Wireless Networks

Zou, Y; Long, Y; Gong, S; Hoang, DT; Liu, W; Cheng, W; Niyato, D

Robust Beamforming Optimization for Self-Sustainable Intelligent Reflecting Surface Assisted Wireless Networks

Zou, Y Long, Y Gong, S Hoang, DT Liu, W Cheng, W Niyato, D

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Cognitive Communications and Networking, 2022, 8, (2), pp. 856-870
Issue Date:: 2022-06-01

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Field	Value	Language
dc.contributor.author	Zou, Y
dc.contributor.author	Long, Y
dc.contributor.author	Gong, S
dc.contributor.author	Hoang, DT
dc.contributor.author	Liu, W
dc.contributor.author	Cheng, W
dc.contributor.author	Niyato, D
dc.date.accessioned	2023-03-21T04:38:10Z
dc.date.available	2023-03-21T04:38:10Z
dc.date.issued	2022-06-01
dc.identifier.citation	IEEE Transactions on Cognitive Communications and Networking, 2022, 8, (2), pp. 856-870
dc.identifier.issn	2332-7731
dc.identifier.issn	2332-7731
dc.identifier.uri	http://hdl.handle.net/10453/167945
dc.description.abstract	We focus on an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) system where the IRS sustains its operations by harvesting energy from the access point (AP) in the power splitting (PS) protocol. We aim to minimize the AP's transmit power subject to the receivers' signal-to-noise ratio (SNR) and the IRS's energy budget constraints. A two-stage optimization framework is proposed to jointly optimize the AP's active beamforming, the IRS's passive beamforming, and the reflection amplitude. Given the reflection amplitude, we employ alternating optimization to update the beamforming strategies. Then, we determine the lower and upper bounds of the reflection amplitude in closed-form expressions, which help to update the reflection amplitude in a bisection method. We further extend our study to the robust case with uncertain channels. Our analysis reveals that the robust counterpart can be solved by the same optimization framework. Extensive simulations reveal that our algorithm is efficacy to balance the IRS's energy budget and the receiver's SNR performance. With uncertain channel information, a larger size of the IRS does not always ensure a higher performance improvement to information transmissions.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Cognitive Communications and Networking
dc.relation.isbasedon	10.1109/TCCN.2021.3133839
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Robust Beamforming Optimization for Self-Sustainable Intelligent Reflecting Surface Assisted Wireless Networks
dc.type	Journal Article
utslib.citation.volume	8
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
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-21T04:38:09Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	8
utslib.citation.issue	2

Abstract:

We focus on an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) system where the IRS sustains its operations by harvesting energy from the access point (AP) in the power splitting (PS) protocol. We aim to minimize the AP's transmit power subject to the receivers' signal-to-noise ratio (SNR) and the IRS's energy budget constraints. A two-stage optimization framework is proposed to jointly optimize the AP's active beamforming, the IRS's passive beamforming, and the reflection amplitude. Given the reflection amplitude, we employ alternating optimization to update the beamforming strategies. Then, we determine the lower and upper bounds of the reflection amplitude in closed-form expressions, which help to update the reflection amplitude in a bisection method. We further extend our study to the robust case with uncertain channels. Our analysis reveals that the robust counterpart can be solved by the same optimization framework. Extensive simulations reveal that our algorithm is efficacy to balance the IRS's energy budget and the receiver's SNR performance. With uncertain channel information, a larger size of the IRS does not always ensure a higher performance improvement to information transmissions.

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http://hdl.handle.net/10453/167945