Efficient Prediction of the EM Response of Reflectarray Antenna Elements by an Advanced Statistical Learning Method

Salucci, Marco; Tenuti, L.; Oliveri, Giacomo; Massa, Andrea

doi:10.1109/tap.2018.2835566

Cited by 68 publications

(30 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The prediction of the electromagnetic response of complex‐shaped reflectarray elements was presented in Reference 170, where the authors presented an innovative LBE method based on Ordinary Kriging to obtain reliable predictions. Full‐Wave simulations were used in order to generate a training set composed of the elevation angle, azimuth, operating frequency, and the degrees‐of‐freedom (DoF) for each array element, along with the corresponding field distribution.…”

Section: Predicting Antenna Parameters With Machine Learning Modelsmentioning

confidence: 99%

A review on the design and optimization of antennas using machine learning algorithms and techniques

Misilmani

Naous

Khatib

2020

Int J RF Microw Comput Aided Eng

View full text Add to dashboard Cite

This paper presents a focused and comprehensive literature survey on the use of machine learning (ML) in antenna design and optimization. An overview of the conventional computational electromagnetics and numerical methods used to gain physical insight into the design of the antennas is first presented. The major aspects of ML are then presented, with a study of its different learning categories and frameworks. An overview and mathematical briefing of regression models built with ML algorithms is then illustrated, with a focus on those applied in antenna synthesis and analysis. An in‐depth overview on the different research papers discussing the design and optimization of antennas using ML is then reported, covering the different techniques and algorithms applied to generate antenna parameters based on desired radiation characteristics and other antenna specifications. Various investigated antennas are sorted based on antenna type and configuration to assist the readers who wish to work with a specific type of antennas using ML.

show abstract

Section: Predicting Antenna Parameters With Machine Learning Modelsmentioning

confidence: 99%

A review on the design and optimization of antennas using machine learning algorithms and techniques

Misilmani

Naous

Khatib

2020

Int J RF Microw Comput Aided Eng

View full text Add to dashboard Cite

show abstract

“…Recently, artificial neural network (ANN) as a relatively new computational tool has attracted much attentions 14‐21 . Inspired by an animal's central nervous systems, in the ANN a system consisting of densely interconnected adaptive processing nodes called artificial neurons are generated to perform massively parallel computations for data processing and knowledge representation.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by an animal's central nervous systems, in the ANN a system consisting of densely interconnected adaptive processing nodes called artificial neurons are generated to perform massively parallel computations for data processing and knowledge representation. Because of the remarkable information processing abilities including the nonlinearity, high parallelism, fault and noise tolerance, and so forth, the ANN has been widely applied in various fields including antenna shape prediction, 18 reflect array element design, 19 direction‐of‐arrival (DOA) estimation, 20 metasurface design 21 …”

Section: Introductionmentioning

confidence: 99%

Artificial neural network and convex optimization enable antenna array design

Wen

Shi

Yang

et al. 2021

Int J RF Microw Comput Aided Eng

View full text Add to dashboard Cite

This article proposed an optimization design method based on artificial neural network (ANN) and convex optimization for antenna arrays. The ANN is used to automatically design and optimize radiation performance of antenna element and distance between the antenna elements. The convex optimization method is employed for optimal syntheses of main lobe direction and side lobe level (SLL) of the arrays. Due to introduction of the element pattern with the coupling effect between antenna elements into the array synthesis, the radiation performance of the antenna array obtained by the proposed optimization design method agrees well with that calculated by the full‐wave simulation. Numerical examples including the linear and planar arrays are given to demonstrate good performance of the proposed optimization design method including low SLL and high efficiency.

show abstract

“…Here, in continuation to [19] and [20], we use 2 different ANN, one for magnitude and one for phase, in order to improve the prediction accuracy of the full reflection matrix. Efficient approaches based on ordinary kriging and support vector machines have also been proposed recently [21]. Whatever the used model, the second step in the design approach is to select the best geometry for all cells, in order to meet the specifications for the array radiation.…”

Section: Introductionmentioning

confidence: 99%

Spherical Mapping of the Second-Order Phoenix Cell for Unbounded Direct Reflectarray Copolar Optimization

Richard¹,

Loison²,

Gillard³

et al. 2019

PIER C

View full text Add to dashboard Cite

A general synthesis approach is proposed for reflectarrays using second order Phoenix cells. It relies on an original spherical representation that transforms the optimization domain in a continuous and unbounded space with reduced dimension. This makes the synthesis problem simpler and automatically guarantees smooth variations in the optimized layout. The proposed mapping is combined with an Artificial Neural Network (ANN) based behavioral model of the cell and integrated in a min/max optimization process. Bi-cubic spline expansions are used to decrease the number of variables. As an application, a contoured beam for space communication in the [3.6-4.2] GHz band is considered. The gain improvement compared to an initial Phase Only synthesis (POS) is up to 1.62 dB at the upper frequency. Full-wave simulation of the final array is provided as a validation.

show abstract

Efficient Prediction of the EM Response of Reflectarray Antenna Elements by an Advanced Statistical Learning Method

Cited by 68 publications

References 32 publications

A review on the design and optimization of antennas using machine learning algorithms and techniques

A review on the design and optimization of antennas using machine learning algorithms and techniques

Artificial neural network and convex optimization enable antenna array design

Spherical Mapping of the Second-Order Phoenix Cell for Unbounded Direct Reflectarray Copolar Optimization

Contact Info

Product

Resources

About