Fitness Landscape Analysis and Niching Genetic Algorithm for Hybrid Beamforming in RIS-Aided Communications

Bai Yan1,2,3,4; Qi Zhao2,4; Menke Li2; Jin Zhang2,3,5; J. Andrew Zhang6; Xin Yao1,2,3,4

2022-12-05

10.1016/j.asoc.2022.109725

APPLIED SOFT COMPUTING   Impact Factor & Quartile

1568-4946

1872-9681

Reconfigurable intelligent surface (RIS) is a revolutionizing technology to achieve cost-effective com-munications. 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.(c) 2022 Published by Elsevier B.V.

Reconfigurable intelligent surface Fitness landscape analysis Evolutionary algorithm Niching

SCI

English

First ; Corresponding

[61701216] ; [JCYJ20180507181527806] ; [2020B121201001] ; [2016ZT06G587] ; [KYTDPT20181011104007]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications

WOS:000895417000003

ELSEVIER

COMPUTER SCIENCE

人工提交

1.Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen 518055, China
2.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
3.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen 518055, China
4.School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China
5.Peng Cheng Laboratory, Shenzhen 518000, China
6.Global Big Data Technologies Centre (GBDTC), University of Technology Sydney, NSW 2007, Australia

Bai Yan,Qi Zhao,Menke Li,et al. Fitness Landscape Analysis and Niching Genetic Algorithm for Hybrid Beamforming in RIS-Aided Communications[J]. APPLIED SOFT COMPUTING,2022,131.

Bai Yan,Qi Zhao,Menke Li,Jin Zhang,J. Andrew Zhang,&Xin Yao.(2022).Fitness Landscape Analysis and Niching Genetic Algorithm for Hybrid Beamforming in RIS-Aided Communications.APPLIED SOFT COMPUTING,131.

Bai Yan,et al."Fitness Landscape Analysis and Niching Genetic Algorithm for Hybrid Beamforming in RIS-Aided Communications".APPLIED SOFT COMPUTING 131(2022).