Hostname: page-component-848d4c4894-x24gv Total loading time: 0 Render date: 2024-05-11T18:10:20.728Z Has data issue: false hasContentIssue false
  • English
  • Français

Meta-heuristic optimization algorithms for synthesis of reconfigurable hexagonal array antenna in two principle vertical planes

Published online by Cambridge University Press:  26 April 2021

Bitan Misra Open the ORCID record for Bitan Misra [Opens in a new window]  and
Gautam Kumar Mahanti
Bitan Misra*
Affiliation:
Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, West Bengal, India
Gautam Kumar Mahanti
Affiliation:
Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, West Bengal, India
*
Author for correspondence: Bitan Misra, E-mail: misra.bitan2013@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

This study illustrates the dynamical reconfiguration of a concentric hexagonal antenna array radiation to generate a pencil beam and flat-top beam simultaneously by electronic control in two principle vertical planes under consideration. Both the beams share a common normalized optimal current excitation amplitude distribution while the optimal sets of phase excitation coefficients are varied radically across the hexagons to generate a flat-top beam. The proposed approach is able to solve the underlying multi-objective problem and flexible enough to the efficient implementation of additional design constraints in the considered φ-planes. In this paper, a set of simulation-based examples are presented in an integrated way. The outcomes validate the effectiveness of the stated optimization using meta-heuristic optimization algorithms (teaching–learning-based optimization, symbiotic organism search, multi-verse optimization) to reach the solution globally and prove actual relevance to the concerned applications.

Keywords

Meta-heuristic algorithmshexagonal array antennapencil beamflat-top beam
Type
Computer Aided Design
Information
International Journal of Microwave and Wireless Technologies , Volume 14 , Issue 2 , March 2022 , pp. 158 - 165
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press in association with the European Microwave Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Elliott, RS (2003) Antenna Theory and Design. New York: Wiley Interscience.CrossRefGoogle Scholar
Bucci, OM, Mazzarella, G and Panariello, G (1991) Reconfigurable arrays by phase-only control. IEEE Transactions on Antennas and Propagation 39, 919–925.CrossRefGoogle Scholar
Buttazzoni, G and Vescovo, R (2012) Power synthesis for reconfigurable arrays by phase-only control with simultaneous dynamic range ratio and near-field reduction. IEEE Transactions on Antennas and Propagation 60, 1161–1165.CrossRefGoogle Scholar
Gies, D and Rahmat-Samii, Y (2003) Particle swarm optimization for reconfigurable phase-differentiated array design. Microwave and Optical Technology Letters 38, 168–175.CrossRefGoogle Scholar
Boeringer, DW and Werner, DH (2004) Particle swarm optimization versus genetic algorithms for phased array synthesis. IEEE Transactions on Antennas and Propagation 52, 771–779.CrossRefGoogle Scholar
Mahanti, GK, Chakrabarty, A and Das, S (2007) Phase-only and amplitude-phase synthesis of dual-pattern linear antenna arrays using floating-point genetic algorithms. Progress in Electromagnetics Research 68, 247–259.CrossRefGoogle Scholar
Baskar, S, Alphones, A and Suganthan, PN (2005) Genetic-algorithm-based design of a reconfigurable antenna array with discrete phase shifters. Microwave and Optical Technology Letters 45, 461–465.CrossRefGoogle Scholar
Li, X and Yin, M (2011) Hybrid differential evolution with biogeography-based optimization for design of a reconfigurable antenna array with discrete phase shifters. International Journal of Antennas and Propagation 2011, 1–12.CrossRefGoogle Scholar
Yan, L, Yong-Chang, J, Ya-Ming, Z and Yan-Yan, T (2014) Synthesis of phase-only reconfigurable linear arrays using multiobjective invasive weed optimization based on decomposition. International Journal of Antennas and Propagation 2011, 1–11.Google Scholar
Jamunaa, D, Hasoon, FN and Mahanti, GK (2019) Symbiotic organisms search optimisation algorithm for synthesis of phase-only reconfigurable concentric circular antenna array with uniform amplitude distribution. International Journal of Electronics Letters 8.Google Scholar
Jamunaa, D, Mahanti, GK and Hasoon Al Attar, FN (2019) Design of phase-only reconfigurable planar array antenna in selected phi cuts using various meta-heuristic optimization algorithms. Sådhanå 44, 83.Google Scholar
Misra, B and Mahanti, GK (2020) Minimization of side lobe level of non-uniformly spaced concentric elliptical array antennas for a desired value of first null beam width in vertical and horizontal planes. Electromagnetics 40, 254–261.CrossRefGoogle Scholar
Misra, B and Mahanati, GK (2020) Side lobe level reduction of thinned concentric elliptical array antenna in vertical and horizontal plane for a desired peak directivity. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields 34.Google Scholar
Castorina, G, Donato, LD, Morabito, AF, Isernia, T and Sorbello, G (2016) Analysis and design of a concrete embedded antenna for wireless monitoring applications [Antenna Applications Corner]. IEEE Antennas and Propagation Magazine 58, 76–93.CrossRefGoogle Scholar
Rao, RV and Patel, V (2013) An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica 20, 710–720.Google Scholar
Cheng, MY and Prayogo, D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers and Structures 139, 98–112.CrossRefGoogle Scholar
Mirjalili, S, Mirjalili, SM and Hatamlou, A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications 27, 495–513.CrossRefGoogle Scholar
Gozasht, F, Dadashzadeh, GR and Nikmehr, S (2007) A comprehensive performance study of circular and hexagonal array geometries in the LMS algorithm for smart antenna applications. Progress in Electromagnetics Research 68, 281–296.CrossRefGoogle Scholar
Kretly, LC, Cerqueira, AS Jr and Tavora, AAS (2002) A hexagonal adaptive antenna array concept for wireless communication applications, The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 1, 247–249.Google Scholar
Mahmoud, KR, El-Adway, M, Ibrahem, SMM, Basnel, R, Mahmoud, R and Zainud-Deen, SH (2007) A comparison between circular and hexagonal array geometries for smart antenna systems using particle swarm algorithm. Progress in Electromagnetics Research [PIER] 72, 75–90.CrossRefGoogle Scholar
Bera, R, Lanjewar, R, Mandal, D, Kar, R and Ghoshal, SP (2015) Comparative Study of Circular and Hexagonal Antenna Array Synthesis using Improved Particle Swarm Optimization, International Conference on Advanced Computing Technologies and Applications (ICACTA-2015), Procedia Computer Science 45, 651–660.CrossRefGoogle Scholar