Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Design Optimization of Antennas
share announcement

Design Optimization of Antennas

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (25 February 2022) | Viewed by 15521

Special Issue Editors

Prof. Dr. Sławomir Kozieł

E-Mail Website
Guest Editor
Department of Engineering, Reykjavik University, 102 Reykjavik, Iceland
Interests: antenna design; surrogate modeling; em-driven design; multiobjective optimization; simulation-driven design; surrogate-based optimization; design optimization; computer-aided design; filtry gm-c; space mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Anna Pietrenko-Dabrowska

E-Mail Website
Guest Editor
Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland
Interests: numerical modeling of antennas and microwave devices; simulation-driven design; design optimization

Special Issue Information

Dear Colleagues,

To meet the needs of emerging application areas such as 5G wireless communications, the Internet of Things (IoT), medical imaging, remote sensing, space communications, or wearable/implantable devices, contemporary antenna systems have reached a high level of sophistication. The design of complex structures involves the adjustment of antenna topology but also meticulous tuning of geometry and material parameters. Practical realization of these tasks necessarily involves numerical optimization procedures, yet it is a challenging endeavor, partly due to the high CPU cost entailed by massive electromagnetic (EM) simulations (otherwise necessary to ensure reliability), but also resulting from the necessity of handing multiple performance figures and constraints.

Over the recent years, considerable research efforts have been directed toward the development of techniques that alleviate the difficulties pertinent to EM-driven antenna optimization, both in the context of computational efficiency and reliability. Apart from improvements concerning conventional methods, a number of algorithmic tools and concepts have been incorporated into antenna design frameworks, including adjoint sensitivities, topology optimization, surrogate modeling, and machine learning approaches, to mention a few.

In this Special Issue, we invite the scientific community to publish works highlighting recent advancements in simulation-based optimization of antenna systems.

The topics of interest include but are not limited to:

  • Simulation-driven antenna optimization;
  • Optimization using adjoint sensitivities;
  • Variable-fidelity antenna optimization;
  • Physics-based and data-driven modeling for antenna design;
  • Multiobjective design methods;
  • Global optimization methods;
  • Nature-inspired antenna optimization techniques;
  • Surrogate-assisted antenna optimization;
  • Inverse modeling techniques for antenna design;
  • Uncertainty quantification of antenna structures;
  • Yield-driven design;
  • Neural network approaches;
  • Space-mapping-based techniques;
  • Optimization-based pattern synthesis of antenna arrays;
  • Optimization of frequency selective surfaces;

Prof. Dr. Sławomir Kozieł
Dr. Anna Pietrenko-Dabrowska
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Simulation-driven antenna optimization
  • Optimization using adjoint sensitivities
  • Variable-fidelity antenna optimization
  • Physics-based and data-driven modeling for antenna design
  • Multiobjective design methods
  • Global optimization methods
  • Nature-inspired antenna optimization techniques
  • Surrogate-assisted antenna optimization
  • Inverse modeling techniques for antenna design
  • Uncertainty quantification of antenna structures
  • Yield-driven design
  • Neural network approaches
  • Space-mapping-based techniques
  • Optimization-based pattern synthesis of antenna arrays
  • Optimization of frequency selective surfaces

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

19 pages, 4920 KiB  
Article
Planar Array Failed Element(s) Radiation Pattern Correction: A Comparison
by Navaamsini Boopalan, Agileswari K. Ramasamy, Farrukh Nagi and Ammar Ahmed Alkahtani
Appl. Sci. 2021, 11(19), 9234; https://0-doi-org.brum.beds.ac.uk/10.3390/app11199234 - 04 Oct 2021
Viewed by 1653
Abstract
Phased arrays are widely used in different fields, such as broadcasting, radar, optics, and space communications. The principle of phased arrays is to generate a directed signal from a large number of antennas to be steered at any desired angle. This, however, increases [...] Read more.
Phased arrays are widely used in different fields, such as broadcasting, radar, optics, and space communications. The principle of phased arrays is to generate a directed signal from a large number of antennas to be steered at any desired angle. This, however, increases the probability of defective elements in an array. Faulty elements in an array cause asymmetry and result in increased sidelobe levels which rigorously distort the radiation pattern. Increased sidelobe radiation wastes energy and can cause interference by radiating and receiving signals in unintended directions. Therefore, it is necessary to find a method that can provide accuracy in the radiation pattern transmitted or received in the presence of failed element(s) in an array. This paper compares the few available optimization methods, namely, simulated annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Pattern Search (PS) methods. For each method, various types of failures were examined, and the most suitable techniques to recover the far-field radiation are recommended. The optimization is then carried out by selecting the optimal weights of the remaining working elements in the planar array. The optimized radiation pattern’s efficiency was evaluated by comparing the Signal to Noise Ratio (SNR) value of the optimized radiation with reference and failed radiation patterns. The PSO method showed a better performance compared to all the other methods in reducing the failed radiation pattern’s SNR value. In various types of failure tests, this method reduced the failed radiation pattern’s SNR from 1 to 10 dB. This method also successfully produced a radiation pattern that closely matches the reference pattern before any failed element(s) are presented in the array. The life cycle of a planar array system with faulty elements can be increased by optimizing the remaining active elements in the array with the PSO method. It also reduces the cost of restoring and replacing the failed elements in an array regularly. This approach also prevents near-field measurement that requires complicated processes using costly equipment. Full article
(This article belongs to the Special Issue Design Optimization of Antennas)
►▼ Show Figures

Figure 1

27 pages, 11717 KiB  
Article
Emerging Swarm Intelligence Algorithms and Their Applications in Antenna Design: The GWO, WOA, and SSA Optimizers
by Achilles D. Boursianis, Maria S. Papadopoulou, Marco Salucci, Alessandro Polo, Panagiotis Sarigiannidis, Konstantinos Psannis, Seyedali Mirjalili, Stavros Koulouridis and Sotirios K. Goudos
Appl. Sci. 2021, 11(18), 8330; https://0-doi-org.brum.beds.ac.uk/10.3390/app11188330 - 08 Sep 2021
Cited by 19 | Viewed by 3888
Abstract
Swarm Intelligence (SI) Algorithms imitate the collective behavior of various swarms or groups in nature. In this work, three representative examples of SI algorithms have been selected and thoroughly described, namely the Grey Wolf Optimizer (GWO), the Whale Optimization Algorithm (WOA), and the [...] Read more.
Swarm Intelligence (SI) Algorithms imitate the collective behavior of various swarms or groups in nature. In this work, three representative examples of SI algorithms have been selected and thoroughly described, namely the Grey Wolf Optimizer (GWO), the Whale Optimization Algorithm (WOA), and the Salp Swarm Algorithm (SSA). Firstly, the selected SI algorithms are reviewed in the literature, specifically for optimization problems in antenna design. Secondly, a comparative study is performed against widely known test functions. Thirdly, such SI algorithms are applied to the synthesis of linear antenna arrays for optimizing the peak sidelobe level (pSLL). Numerical tests show that the WOA outperforms the GWO and the SSA algorithms, as well as the well-known Particle Swarm Optimizer (PSO), in terms of average ranking. Finally, the WOA is exploited for solving a more computational complex problem concerned with the synthesis of an dual-band aperture-coupled E-shaped antenna operating in the 5G frequency bands. Full article
(This article belongs to the Special Issue Design Optimization of Antennas)
►▼ Show Figures

Graphical abstract

15 pages, 5158 KiB  
Article
Compact Ultra-Wideband Series-Feed Microstrip Antenna Arrays for IoT Communications
by Tiago Varum, João Caiado and João N. Matos
Appl. Sci. 2021, 11(14), 6267; https://0-doi-org.brum.beds.ac.uk/10.3390/app11146267 - 06 Jul 2021
Cited by 9 | Viewed by 4732
Abstract
Modern communication systems require high bandwidth to meet the needs of the huge number of sensors and the growing amount of data consumed, and an exponential growth is expected in the future with the integration of internet of things networks. Spectrum regions in [...] Read more.
Modern communication systems require high bandwidth to meet the needs of the huge number of sensors and the growing amount of data consumed, and an exponential growth is expected in the future with the integration of internet of things networks. Spectrum regions in the millimeter waves have aroused new interests, mainly because of the contiguous bands available to meet these needs. In return, and to combat the high losses of propagation in these frequencies, higher gain antennas are needed. This paper describes the use of a logarithmic architecture in the design of microstrip antenna arrays, creating structures with high gain and ultra-wide bandwidth. Three different solutions are presented with five, seven, and nine elements, reaching about 25%, 30%, and 44% of bandwidth, achieving ultra-wideband behavior, efficient and with a compact structure operating at frequencies in around 28 GHz. Full article
(This article belongs to the Special Issue Design Optimization of Antennas)
►▼ Show Figures

Figure 1

18 pages, 41920 KiB  
Article
Bandwidth Improvement of an Inverted-F Antenna Using Dynamic Hybrid Binary Particle Swarm Optimization
by Jude Alnas, Garrett Giddings and Nathan Jeong
Appl. Sci. 2021, 11(6), 2559; https://0-doi-org.brum.beds.ac.uk/10.3390/app11062559 - 12 Mar 2021
Cited by 9 | Viewed by 2653
Abstract
This paper proposes a Dynamic Hybrid Binary Particle Swarm Optimization (DH-BPSO) algorithm to improve the bandwidth of an inverted-F antenna (IFA). The proposed algorithm improves upon the existing Artificial Immune System (AIS) algorithm by including a weighting factor that dynamically changes throughout the [...] Read more.
This paper proposes a Dynamic Hybrid Binary Particle Swarm Optimization (DH-BPSO) algorithm to improve the bandwidth of an inverted-F antenna (IFA). The proposed algorithm improves upon the existing Artificial Immune System (AIS) algorithm by including a weighting factor that dynamically changes throughout the optimization. DH-BPSO activates or deactivates a 12 × 2 grid of parasitic patches incorporated between the IFA and ground plane. The DH-BPSO optimized and conventional IFAs are fabricated and compared while maintaining the same antenna volume. The measurement results show that the optimized IFAs have characteristics of 58.6% wider bandwidths and 5.8% higher antenna gain for various ground clearance lengths at Long Term Evolution (LTE) 700 MHz band compared to the conventional IFAs. Full article
(This article belongs to the Special Issue Design Optimization of Antennas)
►▼ Show Figures

Figure 1

Review

Jump to: Research

14 pages, 5869 KiB  
Review
Performance-Driven Yield Optimization of High-Frequency Structures by Kriging Surrogates
by Slawomir Koziel and Anna Pietrenko-Dabrowska
Appl. Sci. 2022, 12(7), 3697; https://0-doi-org.brum.beds.ac.uk/10.3390/app12073697 - 06 Apr 2022
Cited by 5 | Viewed by 1482
Abstract
Uncertainty quantification is an important aspect of engineering design, as manufacturing tolerances may affect the characteristics of the structure. Therefore, the quantification of these effects is indispensable for an adequate assessment of design quality. Toward this end, statistical analysis is performed, for reliability [...] Read more.
Uncertainty quantification is an important aspect of engineering design, as manufacturing tolerances may affect the characteristics of the structure. Therefore, the quantification of these effects is indispensable for an adequate assessment of design quality. Toward this end, statistical analysis is performed, for reliability reasons, using full-wave electromagnetic (EM) simulations. Still, the computational expenditures associated with EM-driven statistical analysis often turn out to be unendurable. Recently, a performance-driven modeling technique has been proposed that may be employed for uncertainty quantification purposes and can enable circumventing the aforementioned difficulties. Capitalizing on this idea, this paper discusses a procedure for fast and simple surrogate-based yield optimization of high-frequency structures. The main concept of the approach is a tailored definition of the surrogate domain, which is based on a couple of pre-optimized designs that reflect the directions featuring maximum variability of the circuit responses with respect to its dimensions. A compact size of such a domain allows for the construction of an accurate metamodel therein using moderate numbers of training samples, and subsequently, it is employyed to enhance the yield. The implementation details are dedicated to a particular type of device. Results obtained for a ring-slot antenna and a miniaturized rat-race coupler imply that the cost of yield optimization process can be reduced to few dozens of EM analyses. Full article
(This article belongs to the Special Issue Design Optimization of Antennas)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop