Application of Machine Learning Method to the Prediction of EM Response of Reflectarray Antenna Elements

Shi, Liang; Zhang, Qing He; Zhang, Shi Hui; Liu, Guangxu; Yi, Chao

doi:10.1109/piers-fall48861.2019.9021372

Cited by 4 publications

(7 citation statements)

References 10 publications

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“…The incident wave is compensated by different phases and then radiated, that is, the main beam is formed by superposition in a given direction [1]. Assuming that NN  reflectarray elements are periodically arranged on the plane, and the feeder radiates EM waves to the graphene patch to generate reflected and scattered fields, the radiation field of every nth ( 2 1,..., n N = ) graphene reflectarray antenna unit cells can be superimposed as follows [9], [12], [22]:…”

Section: Characteristics Of Graphene Reconfigurable Reflectarray Antennamentioning

confidence: 99%

“…In order to verify the accuracy and effectiveness of the method in this paper, the calculation formula of error index and calculation efficiency is given below to quantitatively evaluate the performance of the proposed method. The magnitude and phase errors 1  and 2  of the scattering coefficient matrix () S z are defined as follows [9], [12]:…”

Section: Use Cnn To Characterize Graphene Reflectarray Antenna Elementmentioning

confidence: 99%

“…In recent years, the artificial intelligence method of machine learning has been widely used in the field of EM engineering, such as EM scattering [9], antenna diagnosis [10], parameter reconstruction [11], etc. Some effective machine learning methods [9], [12]- [14] for analyzing reflectarray antennas are also proposed. In [9], the author used support vector regression (SVR) to predict the EM response of single-layer "Phoenix" unit cell, but the network topology structure of SVR is relatively complex, and the computational efficiency is not particularly obvious.…”

Section: Introductionmentioning

confidence: 99%

“…Some effective machine learning methods [9], [12]- [14] for analyzing reflectarray antennas are also proposed. In [9], the author used support vector regression (SVR) to predict the EM response of single-layer "Phoenix" unit cell, but the network topology structure of SVR is relatively complex, and the computational efficiency is not particularly obvious.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Graphene Reconfigurable Reflectarray Antenna Electromagnetic Response Prediction Using Deep Learning

Shi

Zhang

et al. 2021

IEEE Access

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Aiming at the time-consuming problem of the full-wave (FW) simulation of the scattering characteristics of the traditional graphene reconfigurable reflectarray antenna, a fast prediction method of electromagnetic (EM) response based on deep learning is proposed. The convolutional neural network (CNN) method in deep learning is effectively used in the research of this paper. This method first discretizes the input vector (patch geometry, chemical potential, frequency, incident angle, etc.) of the graphene reflectarray antenna, and then preprocesses the data into a two-dimensional image suitable for CNN training, and finally uses CNN to train the model instead of extensive FW simulation calculations, the EM response of the reflectarray antenna is calculated. The training results of three algorithms of support vector regression (SVR), radial basis function network (RBFN) and CNN are comprehensively compared. The experimental results show that CNN method has good performance and accuracy in the EM response prediction of the graphene reconfigurable reflectarray antenna, with an accuracy of over 99%, and can also save at least 99% of time.INDEX TERMS Convolutional neural network (CNN), electromagnetic (EM) response, graphene, reconfigurable reflectarray antenna.

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Section: Characteristics Of Graphene Reconfigurable Reflectarray Antennamentioning

confidence: 99%

Section: Use Cnn To Characterize Graphene Reflectarray Antenna Elementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Graphene Reconfigurable Reflectarray Antenna Electromagnetic Response Prediction Using Deep Learning

Shi

Zhang

et al. 2021

IEEE Access

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“…A recent approach to accelerate the design of reflectarray antennas is the use of machine learning techniques, such as artificial neural networks (ANN) [1][2][3], kriging [4] or support vector machines applied to regression (SVR) [5,6]. These algorithms are employed to obtain surrogate models of the electromagnetic behavior of the reflectarray unit cell, which is characterized by the matrix of reflection coefficients.…”

Section: Introductionmentioning

confidence: 99%

Systematic Study of the Influence of the Angle of Incidence Discretization in Reflectarray Analysis to Improve Support Vector Regression Surrogate Models

2020

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A systematic study concerning the discretization of the angle of incidence in surrogate models obtained with support vector regression (SVR) is presented. The problem addressed in this work arises from the dependence of the reflection coefficients on the angle of incidence. While the direct coefficients are usually stable with the angle of incidence, this is not the case with the cross-coefficients, which translates this behavior to the crosspolar component of the radiation pattern. Then, correctly assessing this influence and minimizing radiation pattern distortion allow training SVR surrogate models per angle of incidence without penalizing accuracy in the prediction of the far field. The results shown in this work are directly relevant to improving the computational performance of SVRs applied to reflectarray design since they allow reducing the dimensionality of the models by generating surrogate models per angle of incidence instead of including the angles of incidence as input variables. In addition, it highlights the importance of a proper discretization of the angles of incidence for a correct prediction of the crosspolar pattern for its subsequent optimization, especially for advanced space applications with tight crosspolar requirements.

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Accurate Characterization of Graphene Reconfigurable Reflectarray Antenna Element by SVR

Shi

Zhang

et al. 2021

IEEE J. Multiscale Multiphys. Comput. Tech.

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Application of Machine Learning Method to the Prediction of EM Response of Reflectarray Antenna Elements

Cited by 4 publications

References 10 publications

Efficient Graphene Reconfigurable Reflectarray Antenna Electromagnetic Response Prediction Using Deep Learning

Efficient Graphene Reconfigurable Reflectarray Antenna Electromagnetic Response Prediction Using Deep Learning

Systematic Study of the Influence of the Angle of Incidence Discretization in Reflectarray Analysis to Improve Support Vector Regression Surrogate Models

Accurate Characterization of Graphene Reconfigurable Reflectarray Antenna Element by SVR

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