<p>This paper presents a physics-informed Gaussian process regression (PI-GPR) data augmentation framework, combined with standalone machine-learning predictive models, for the design, optimization, and performance prediction of a flower-shaped wideband millimeter-wave antenna and its extension to a compact multiple-input multiple-output (MIMO) configuration. The framework expands an initial CST-simulated dataset to 2,014 physics-consistent samples by integrating closed-form analytical modeling, electromagnetic constraints, and data-driven GPR, eliminating the need for exhaustive full-wave simulations. Following the data augmentation, five predictive modeling techniques including response surface methodology (RSM), artificial neural network (ANN), ridge regression (RR), random forest (RF), and gradient boosting (GB) are developed and cross-validated, where the RF model achieved improved performance of <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(R^2 = 0.999\)</EquationSource></InlineEquation>, RMSE <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(= 0.031\)</EquationSource></InlineEquation> and 0.028, and MSE <InlineEquation ID="IEq3"><EquationSource Format="TEX">\(= 0.001\)</EquationSource></InlineEquation> and 0.001 for resonant frequency (<InlineEquation ID="IEq4"><EquationSource Format="TEX">\(F_r\)</EquationSource></InlineEquation>) and impedance bandwidth (BW), respectively. The optimized single-element antenna, designed on a Rogers RT/5880 substrate with overall dimensions of <InlineEquation ID="IEq5"><EquationSource Format="TEX">\(0.7\lambda _0 \times 0.7\lambda _0 \times 0.069\lambda _0~\textrm{mm}^3\)</EquationSource></InlineEquation>, achieves a gain of 3.0 dBi at 28 GHz and an impedance bandwidth spanning 22–41.378 GHz. This element antenna is subsequently transformed into a compact quad-port <InlineEquation ID="IEq6"><EquationSource Format="TEX">\(2{\times }2\)</EquationSource></InlineEquation> MIMO antenna with overall dimensions of <InlineEquation ID="IEq7"><EquationSource Format="TEX">\(1.563\lambda _0 \times 1.563\lambda _0 \times 0.069\lambda _0\)</EquationSource></InlineEquation> mm<InlineEquation ID="IEq8"><EquationSource Format="TEX">\(^3\)</EquationSource></InlineEquation>, yielding a bandwidth of 19.833 GHz, a peak element gain of 6.22 dBi, an envelope correlation coefficient (ECC) below 0.0035, and a diversity gain (DG) exceeding 9.983 dB. All results are validated by independent CST and HFSS electromagnetic simulations, which show close agreement. The proposed methodology demonstrates a scalable and simulation-efficient approach to mmWave MIMO antenna design for interference-prone, multi-sensor Internet of Things (IoT) applications.</p>

Physics-informed-GPR data augmentation framework for flower-shaped antenna performance optimization and prediction using machine learning models

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2026年7月12日

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Lijaddis Getnet Ayalew,
Tsion Yigzaw Kumlachew,
Demessu Kebede Chaka,
Ephrem Getachew Demesa

摘要

This paper presents a physics-informed Gaussian process regression (PI-GPR) data augmentation framework, combined with standalone machine-learning predictive models, for the design, optimization, and performance prediction of a flower-shaped wideband millimeter-wave antenna and its extension to a compact multiple-input multiple-output (MIMO) configuration. The framework expands an initial CST-simulated dataset to 2,014 physics-consistent samples by integrating closed-form analytical modeling, electromagnetic constraints, and data-driven GPR, eliminating the need for exhaustive full-wave simulations. Following the data augmentation, five predictive modeling techniques including response surface methodology (RSM), artificial neural network (ANN), ridge regression (RR), random forest (RF), and gradient boosting (GB) are developed and cross-validated, where the RF model achieved improved performance of \(R^2 = 0.999\), RMSE \(= 0.031\) and 0.028, and MSE \(= 0.001\) and 0.001 for resonant frequency (\(F_r\)) and impedance bandwidth (BW), respectively. The optimized single-element antenna, designed on a Rogers RT/5880 substrate with overall dimensions of \(0.7\lambda _0 \times 0.7\lambda _0 \times 0.069\lambda _0~\textrm{mm}^3\), achieves a gain of 3.0 dBi at 28 GHz and an impedance bandwidth spanning 22–41.378 GHz. This element antenna is subsequently transformed into a compact quad-port \(2{\times }2\) MIMO antenna with overall dimensions of \(1.563\lambda _0 \times 1.563\lambda _0 \times 0.069\lambda _0\) mm\(^3\), yielding a bandwidth of 19.833 GHz, a peak element gain of 6.22 dBi, an envelope correlation coefficient (ECC) below 0.0035, and a diversity gain (DG) exceeding 9.983 dB. All results are validated by independent CST and HFSS electromagnetic simulations, which show close agreement. The proposed methodology demonstrates a scalable and simulation-efficient approach to mmWave MIMO antenna design for interference-prone, multi-sensor Internet of Things (IoT) applications.