Fitness landscape analysis and niching genetic approach for hybrid beamforming in RIS-aided communications

Yan, B; Zhao, Q; Li, M; Zhang, J; Zhang, JA; Yao, X

Fitness landscape analysis and niching genetic approach for hybrid beamforming in RIS-aided communications

Yan, B Zhao, Q Li, M Zhang, J

Zhang, JA Yao, X

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Applied Soft Computing, 2022, 131
Issue Date:: 2022-12-01

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Field	Value	Language
dc.contributor.author	Yan, B
dc.contributor.author	Zhao, Q
dc.contributor.author	Li, M
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-6102-3762
dc.contributor.author	Zhang, JA
dc.contributor.author	Yao, X
dc.date.accessioned	2023-01-18T04:38:18Z
dc.date.available	2023-01-18T04:38:18Z
dc.date.issued	2022-12-01
dc.identifier.citation	Applied Soft Computing, 2022, 131
dc.identifier.issn	1568-4946
dc.identifier.issn	1872-9681
dc.identifier.uri	http://hdl.handle.net/10453/165112
dc.description.abstract	Reconfigurable intelligent surface (RIS) is a revolutionizing technology to achieve cost-effective communications. The active beamforming at the base station (BS) and the discrete phase shifts at RIS should be jointly designed to customize the propagation environment. However, current phase-shift setting methods ignore the non-separable property of phase shifts, degrading the performance, especially in cases with a large-sized RIS. To understand the problem characteristics related to the phase shifts and further tailor an eligible method with such characteristics, this paper, for the first time, analyzes the fitness landscape of the sum-rate maximization problem (maximizing the sum rate of users in a downlink multi-user multiple-input single-output system assisted by a RIS). Results show that the problem has a severe unstructured and rugged landscape, especially in cases with a large-sized RIS. This observation answers why current methods are ineligible and provides insightful guidance for designing a more intelligent method. With the landscape findings in mind, this paper introduces a niching genetic algorithm to solve the problem. In particular, the niching idea is employed to locate multiple local optima. These local optima act as stepping stones to facilitate approaching the global optima. Simulation results demonstrate that the proposed niching genetic algorithm obtains significant capacity gains over current methods in cases with large-sized RIS.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Applied Soft Computing
dc.relation.isbasedon	10.1016/j.asoc.2022.109725
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Fitness landscape analysis and niching genetic approach for hybrid beamforming in RIS-aided communications
dc.type	Journal Article
utslib.citation.volume	131
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
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	embargoed	*
utslib.copyright.embargo	2024-12-01T00:00:00+1000Z
dc.date.updated	2023-01-18T04:38:16Z
pubs.publication-status	Published
pubs.volume	131

Abstract:

Reconfigurable intelligent surface (RIS) is a revolutionizing technology to achieve cost-effective communications. The active beamforming at the base station (BS) and the discrete phase shifts at RIS should be jointly designed to customize the propagation environment. However, current phase-shift setting methods ignore the non-separable property of phase shifts, degrading the performance, especially in cases with a large-sized RIS. To understand the problem characteristics related to the phase shifts and further tailor an eligible method with such characteristics, this paper, for the first time, analyzes the fitness landscape of the sum-rate maximization problem (maximizing the sum rate of users in a downlink multi-user multiple-input single-output system assisted by a RIS). Results show that the problem has a severe unstructured and rugged landscape, especially in cases with a large-sized RIS. This observation answers why current methods are ineligible and provides insightful guidance for designing a more intelligent method. With the landscape findings in mind, this paper introduces a niching genetic algorithm to solve the problem. In particular, the niching idea is employed to locate multiple local optima. These local optima act as stepping stones to facilitate approaching the global optima. Simulation results demonstrate that the proposed niching genetic algorithm obtains significant capacity gains over current methods in cases with large-sized RIS.

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http://hdl.handle.net/10453/165112