Tools for Automated Antenna Design and Optimization

2007 ◽  
Vol 4 (5) ◽  
pp. 853-864 ◽  
Author(s):  
Jason Lohn ◽  
Gregory Hornby ◽  
Derek Linden
Keyword(s):  
Antenna Design ◽  
Design And Optimization ◽  
Automated Antenna Design

scholarly journals Neural Network Characterization of Reflectarray Antennas

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Angelo Freni ◽  
Marco Mussetta ◽  
Paola Pirinoli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Procedure ◽  
Antenna Design ◽  
Ann Model ◽  
Design And Optimization ◽  
Full Wave ◽  
Proposed Model ◽  
Artificial Neural Network Ann

An efficient artificial neural network (ANN) approach for the modeling of reflectarray elementary components is introduced to improve the numerical efficiency of the different phases of the antenna design and optimization procedure, without loss in accuracy. The comparison between the results of the analysis of the entire reflectarray designed using the simplified ANN model or adopting a full-wave characterization of the unit cell finally proves the effectiveness of the proposed model.

A Dynamic Evolutionary Algorithm for Automated Antenna Design

2012 ◽  
Vol 9 (2) ◽  
pp. 243-248
Author(s):  
Sanyou Zeng ◽  
Huanhuan Li ◽  
Zhengjun Li ◽  
Hongyong Jing ◽  
Wei Dong
Keyword(s):  
Evolutionary Algorithm ◽  
Antenna Design ◽  
Automated Antenna Design

scholarly journals Design and Optimization of Compact Printed Log-Periodic Dipole Array Antennas with Extended Low-Frequency Response

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2044
Author(s):  
Keyur K. Mistry ◽  
Pavlos I. Lazaridis ◽  
Zaharias D. Zaharis ◽  
Tian Hong Loh
Keyword(s):  
Frequency Response ◽  
Low Frequency ◽  
Trust Region ◽  
Size Reduction ◽  
Antenna Design ◽  
Baseline Model ◽  
Design And Optimization ◽  
Novel Technique ◽  
Array Antennas ◽  
Lower Frequencies

This paper initially presents an overview of different miniaturization techniques used for size reduction of printed log-periodic dipole array (PLPDA) antennas, and then continues by presenting a design of a conventional PLPDA design that operates from 0.7–8 GHz and achieves a realized gain of around 5.5 dBi in most of its bandwidth. This antenna design is then used as a baseline model to implement a novel technique to extend the low-frequency response. This is completed by replacing the longest straight dipole with a triangular-shaped dipole and by optimizing the four longest dipoles of the antenna using the Trust Region Framework algorithm in CST. The improved antenna with extended low-frequency response operates from 0.4 GHz to 8 GHz with a slightly reduced gain at the lower frequencies.

Consensus Deep Neural Networks for Antenna Design and Optimization

2021 ◽  
pp. 1-1
Author(s):  
Zoran Z. Stankovic ◽  
Dragan I. Olcan ◽  
Nebojsa S. Doncov ◽  
Branko M. Kolundzija
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Antenna Design ◽  
Design And Optimization

scholarly journals Efficiency Improvements of Antenna Optimization Using Orthogonal Fractional Experiments

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Yen-Sheng Chen ◽  
Ting-Yu Ku
Keyword(s):  
Factorial Design ◽  
Radio Frequency Identification ◽  
Heuristic Algorithms ◽  
Full Factorial Design ◽  
Antenna Design ◽  
Handheld Devices ◽  
Small Subset ◽  
Design And Optimization ◽  
Blind Search ◽  
Full Factorial

This paper presents an extremely efficient method for antenna design and optimization. Traditionally, antenna optimization relies on nature-inspired heuristic algorithms, which are time-consuming due to their blind-search nature. In contrast, design of experiments (DOE) uses a completely different framework from heuristic algorithms, reducing the design cycle by formulating the surrogates of a design problem. However, the number of required simulations grows exponentially if a full factorial design is used. In this paper, a much more efficient technique is presented to achieve substantial time savings. By using orthogonal fractional experiments, only a small subset of the full factorial design is required, yet the resultant response surface models are still effective. The capability of orthogonal fractional experiments is demonstrated through three examples, including two tag antennas for radio-frequency identification (RFID) applications and one internal antenna for long-term-evolution (LTE) handheld devices. In these examples, orthogonal fractional experiments greatly improve the efficiency of DOE, thereby facilitating the antenna design with less simulation runs.

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