Antenna Optimization Based on Auto-Context Broad Learning System

Abstract

To enhance the efficiency of antenna optimization, surrogate model methods can usually be used to replace the full-wave electromagnetic simulation software. Broad learning system (BLS), as an emerging network with strong extraction ability and remarkable computational efficiency, has revolutionized the conventional artificial intelligence (AI) methods and overcome the shortcoming of excessive time-consuming training process in deep learning (DL). However, it is difficult to model the regression relationship between input and output variables in the electromagnetic field with the unsatisfactory fitting capability of the original BLS. In order to further improve the performance of the model and speed up the design of microwave components to achieve more accurate prediction of hard-to-measure quality variables through easy-to-measure parameter variables, the conception of auto-context (AC) for the regression scenario is proposed in this paper, using the current BLS training results as the prior knowledge, which are taken as the context information and combined with the original inputs as new inputs for further training. Based on the previous prediction results, AC learns an iterated low-level and context model and then iterates to approach the ground truth, which is very general and easy to implement. Three antenna examples, including rectangular microstrip antenna (RMSA), circular MSA (CMSA), and printed dipole antenna (PDA), and 10 UCI regression datasets are employed to verify the effectiveness of the proposed model.

A Review on Optimization Techniques of Antennas Using AI and ML / DL Algorithms

“…These algorithms [7] helped in deriving the nonlinear relationship between the geometrical parameters and antenna characteristics. The popular ML algorithms used in antenna design are ANN, SVR, Gaussian Process Regression (GPR), LASSO, LR, Broad learning system (BLS), and KRR.…”

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.…”

“…In order to design UWB antennas, reference [7] proposed a deep learning model, and the deep belief network structure is determined by particle swarm algorithm, and then combined it with extreme learning machine. Aiming to speed up the antennas design by the easy-to-measure parameter variables achieving more accurate prediction of hard-to-measure quality variables, [8] proposed auto-context for the regression scenario, using the current broad learning system training results as prior knowledge, which was taken as the context information and then combined them with the original inputs becoming the new inputs for further training. 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.…”

Bandwidth Improvment of Dualband mm-wave Microstrip Antenna Using Genetic Algorithm

“…For dualband operation at 28 GHz and 38 GHz, a slot patch antenna was presented [12]. Dualband antenna also modeled using varaties of autonomous algorithm such as auto-context system learning [13], Gaussian's deep learning process [14], as well as evolutionary algorithms like genetic [15]. In [16], a dualband optimization of a patch antenna in microwave frequency using a genetic algorithm is described.The author of [17] describes a dualband antenna using genetic algorithm at mm-wave with a circular cell.…”