126
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Electromagnetic modeling of reconfigurable antenna array for 5G communications

ORCID Icon, ORCID Icon, ORCID Icon &
Pages 2368-2383 | Received 17 Jan 2021, Accepted 29 Jun 2021, Published online: 08 Jul 2021
 

ABSTRACT

The integration of extra wireless services in the limited size of mobile devices is a very challenging task. In this context, the antenna array presents the best choice to meet these requirements. This research demonstrates a reconfigurable antenna array system proposed for future 5G applications. The characteristics of the proposed structure are examined while using electromagnetic modeling. The mathematical formulation reposes on combining the moment method and generalized equivalent circuit’s methods (MoM-GEC), and the deduced electromagnetic equations allow the computation of the input impedance Zin, the reflection parameter S11 and the current and electric field distributions. Software simulations prove good agreement with MoM-GEC results. The obtained results offer the possibility to generate various modes governed by a decision tree. Thus, these modes are related to different resonant frequencies suitable for 5G mobile communications with a large bandwidth reaching 560 MHz.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

H. Helali

Heithem Helali received a master’s degree in Fundamental Physics and an MSc degree in Condensed matter physics from the Faculty of Mathematical, Physical and Natural Sciences of Tunis (FST). He is a PhD student in Telecommunications at the National Engineering School of Tunis. His research interests focus on the reconfigurable antennas structures using the MoM-GEC method for microwave and antenna applications.

S. Smirani

Soulayma Smirani obtained an engineering degree in Electrical Engineering and a master's degree in Electronics and Advanced Technologies from the Higher National School of Engineering of Tunis (ENSIT). Currently, she is a PhD student in Telecommunications at the National Engineering School of Tunis (ENIT). Her research interest is in the electromagnetic modeling of rectenna for energy harvesting used in several applications and in different frequency ranges.

M. Aidi

Mourad Aidi is currently an associate professor at Gabes University. He received a master's degree in Applied Physics from the Higher School of Sciences and Techniques of Tunis and an MSc degree in energetic physics from the National Institute of Applied Science and Technology (INSAT). In 2016, he earned his PhD in Telecommunications from the National Engineering School of Tunis. His research interests include electromagnetic modeling of the nano-devices based on carbon nanotubes and graphene layers for telecommunication applications.

T. Aguili

Taoufik Aguili is currently a professor at the National Engineering School of Tunis (ENIT), Tunis El Manar University. He is also the director of Communications Systems Laboratory (Syscom), and he is responsible for research and master’s degree in the communications and information's technology Department. His research interests include modeling of microwave systems and nano-devices, numerical methods in electromagnetics, electromagnetic wave phenomena in layered media, integrated transmission lines, waveguides and antennas, and leaky-wave phenomena.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 561.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.