Microstrip antenna modelling based on image‐based convolutional neural network

Abstract

Convolutional neural networks (CNN) have a strong feature extraction ability for images and present a high level of efficiency and accuracy in object detection and image recognition. When CNN is used to model microwave devices, the existing literature generally uses its size parameters as one‐dimensional (1‐D) input, which does not give full play to the image‐processing ability of CNN. In order to make full use of the characteristics of CNN, this letter converts the 1‐D input of microwave devices into the form of an image model, that is, the 1‐D input is transformed into a two‐dimensional (2‐D) matrix composed of 0 and 1 as the input. The image model is combined with CNN, called image‐based CNN (ICNN), which establishes a deep learning surrogate model between the physical parameters and electrical properties of microwave devices and improves the accuracy and generalization ability of the model. Taking the resonant frequency of the microstrip antenna as a simulation example, modelling was carried out by the proposed ICNN and compared with the mainstream machine learning methods. The results show that the proposed method has high convergence and fitting accuracy.

Optimization of Circularly Polarized Omnidirectional Base Station Antenna by Machine Learning

“…ML has been widely applied, such as data mining, securities market analysis, natural language processing, computer vision, speech and handwriting recognition, search engines, strategic games, robot applications, biometric recognition, medical diagnosis, detection of credit card fraud, DNA sequence sequencing, as well as in the field of electromagnetics [1] [2]. There are many concern ML algorithms applied in antennas optimization domain, including support vector machine [3], Gaussian process [4], deep Gaussian process [5], student's T process [6], extreme learning machine [7], broad learning system [8], artificial neural network [8], deep neural network [9], convolutional neural network [10], etc. Reference [3] measured electronically steerable parasitic array radiator patterns, and then used the support vector machine training process to handle antenna-based DOA estimation.…”

“…Reference [9] presented a new deep neural network approach for the adaptive beamforming of antenna array, where a recurrent neural network based on the gated recurrent unit architecture was used as a beamformer for producing proper complex weights in order to feed the antenna array. In order to make full use of the characteristics of convolutional neural network, [10] converted the 1-dimension input of antenna into the form of an image model, which established a deep surrogate model between the physical parameters of the antenna and its electrical properties, and improved the model's accuracy and generalization ability. In this paper, we apply some ML methods, including Linear Regression, k-Nearest Neighbor, Support Vector Regressor, Multi Layer Perception Neural Network, Gaussian Process Regressor, Decision Tree, Bagging, Random Forest, Gradient Boosting, Extra Trees Regression, AdaBoost, XGBoost, LightGBM, CatBoost, Hist Gradient Boosting, Stacked Generalization, to model a circularly polarized omnidirectional base station antenna.…”