Supervised Machine Learning Model for Accurate Output Prediction of Various Antenna Designs

Indharapu, Sai S.; Caruso, Anthony N.; Durbhakula, Kalyan C.

doi:10.1109/ap-s/usnc-ursi47032.2022.9886262

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…In this article, we present a detailed study and comparison of conventional optimization and ML-assisted optimization. Although ML models have been extensively used in fields such as biometrics [13][14][15], healthcare [16][17][18], and finance [19,20], their application in electromagnetics and antenna design optimization is relatively new [21]. Previous studies have utilized various ML models to optimize antenna designs, as summarized in [22].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-based Optimization of Hexagon-shaped Fractal Antenna for Ultra-wideband Communications

Indharapu,

Caruso,

Fields

et al. 2024

PIER C

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In the wireless communication industry, achieving gigabit-per-second data rates with low-profile, ultra-wideband (UWB) microstrip patch antennae poses a significant challenge. Conventional optimization algorithms, though effective, are often computationally expensive, particularly for complex antenna geometries with high degrees of freedom. There is an imperative need for new methodologies to address this challenge and revolutionize the antenna optimization process. Successful and timely development of antennas relies on the efficiency and computational speed of optimization algorithms, full-wave electromagnetic solvers, and the intuition of radio frequency engineers. To mitigate the dependence on complex and time-consuming processes, we propose an efficient machine learning (ML)-based antenna optimization methodology that minimizes optimization time by more than 90%. This paper aims to apply and study the performance of two specific ML models, the radial basis function (RBF), and the least squared regression (LSR) models, in the bandwidth optimization without increasing the aperture area of a hexagon-shaped fractal antenna. The hexagon-shaped fractal antenna was chosen for its UWB characteristics, low profile, and high degrees of freedom (10 adjustable parameters). The reflection coefficient response of a hexagon-shaped fractal antenna is predicted by the trained RBF and LSR models and further optimized by the genetic algorithm (GA). The proposed approach stands out among other notable works in this research domain, especially for UWB applications, by prioritizing the optimization of the mean of the reflection coefficient across the entire frequency range instead of solely targeting individual frequency points. The GA-based optimization using trained ML models has increased the bandwidth by 21.3% and reduced the computational time by 90% compared to conventional optimization without increasing the physical or electrical size of the antenna. Simulation and measurement results concurred with a maximum difference of 5%, demonstrating the efficacy of the ML approach for antenna optimization.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-based Optimization of Hexagon-shaped Fractal Antenna for Ultra-wideband Communications

Indharapu,

Caruso,

Fields

et al. 2024

PIER C

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show abstract

“…Report [8] gives a detailed comprehensive investigation of the performance of a machine learning technique K-Nearest Neighbor (KNN) in predicting the antenna output response of three distinct antenna designs. The different chosen designs have a unique set of design parameters.…”

Section: Introductionmentioning

confidence: 99%

Employing Machine Learning Models to Predict Return Loss Precisely in 5G Antenna

Jain,

Thakare,

Singhal

2023

PIER M

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To meet 5G requirements, designing an optimal antenna is challenging due to numerous design factors. Conventional electromagnetic modeling simulators require excessive time and processing power during the antenna design process. Machine learning (ML), an innovative technology, can be used in the domain of antenna design with favorable performance and can resolve problems that the previous conventional methods cannot. The main goal of this work is to create an antenna that operates at 28 GHz, which is a significant 5G band for the 5G futuristic infrastructure revolution, and to predict the return loss of an antenna using some machine learning models like K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XG-Boost), Decision Tree (DT), and Random Forest (RF). On comparing results, all models perform well with over 83% accuracy. However, the Random Forest model predicts return loss with higher accuracy at 90% and lower MSE and MAE values of 1.99 and 0.827, respectively. Moreover, this antenna holds potential for 5G applications and can be efficiently optimized using a machine learning approach, saving valuable time.

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Geometry Prediction of The Antenna Design Using Machine Learning Method

Agusriandi,

Setijadi,

Affandi

et al. 2023

2023 6th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

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Supervised Machine Learning Model for Accurate Output Prediction of Various Antenna Designs

Cited by 8 publications

References 3 publications

Machine Learning-based Optimization of Hexagon-shaped Fractal Antenna for Ultra-wideband Communications

Machine Learning-based Optimization of Hexagon-shaped Fractal Antenna for Ultra-wideband Communications

Employing Machine Learning Models to Predict Return Loss Precisely in 5G Antenna

Geometry Prediction of The Antenna Design Using Machine Learning Method

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