Open Access iconOpen Access

ARTICLE

New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

Shimaa M. Amer1, Ashraf A. M. Khalaf2, Amr H. Hussein3,4, Salman A. Alqahtani5, Mostafa H. Dahshan6, Hossam M. Kassem3,4,*

1 Electronics and Communications Engineering Department, Higher Institute of Engineering and Technology, Kafr Elsheikh, Egypt
2 Electronics and Electrical Communications Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt
3 Electronics and Electrical Communications Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt
4 Electronics and Electrical Communications Engineering Department, Faculty of Engineering, Horus University Egypt, New Damietta, Egypt
5 Department of Computer Engineering, College of computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia
6 School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW, 2795, Australia

* Corresponding Author: Hossam M. Kassem. Email: email

(This article belongs to the Special Issue: Artificial Intelligence of Things (AIoT): Emerging Trends and Challenges)

Computer Modeling in Engineering & Sciences 2024, 138(3), 2749-2767. https://doi.org/10.32604/cmes.2023.029138

Abstract

Side lobe level reduction (SLL) of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service (QOS) in recent and future wireless communication systems starting from 5G up to 7G. Furthermore, it improves the array gain and directivity, increasing the detection range and angular resolution of radar systems. This study proposes two highly efficient SLL reduction techniques. These techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm (GA) to develop the Conv/GA and DConv/GA, respectively. The convolution process determines the element’s excitations while the GA optimizes the element spacing. For elements linear antenna array (LAA), the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length . This new vector is divided into three different sets of excitations including the odd excitations, even excitations, and middle excitations of lengths , , and , respectively. When the same element spacing as the original LAA is used, it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with a much wider half-power beamwidth (HPBW). While the middle excitations give the same HPBW as the original LAA with a relatively higher SLL. To mitigate the increased HPBW of the odd and even excitations, the element spacing is optimized using the GA. Thereby, the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the SLL. Furthermore, for extreme SLL reduction, the DConv/GA is introduced. In this technique, the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation vectors. It provides a relatively wider HPBW than the original LAA with about quad-fold reduction in the SLL.

Keywords


Cite This Article

APA Style
Amer, S.M., Khalaf, A.A.M., Hussein, A.H., Alqahtani, S.A., Dahshan, M.H. et al. (2024). New antenna array beamforming techniques based on hybrid convolution/genetic algorithm for 5G and beyond communications. Computer Modeling in Engineering & Sciences, 138(3), 2749-2767. https://doi.org/10.32604/cmes.2023.029138
Vancouver Style
Amer SM, Khalaf AAM, Hussein AH, Alqahtani SA, Dahshan MH, Kassem HM. New antenna array beamforming techniques based on hybrid convolution/genetic algorithm for 5G and beyond communications. Comput Model Eng Sci. 2024;138(3):2749-2767 https://doi.org/10.32604/cmes.2023.029138
IEEE Style
S.M. Amer, A.A.M. Khalaf, A.H. Hussein, S.A. Alqahtani, M.H. Dahshan, and H.M. Kassem, “New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications,” Comput. Model. Eng. Sci., vol. 138, no. 3, pp. 2749-2767, 2024. https://doi.org/10.32604/cmes.2023.029138



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 493

    View

  • 1198

    Download

  • 0

    Like

Share Link