Data-Driven Bayesian Optimization Framework for Rapidly Developing Novel Wideband, Low-Profile Dipole Antenna with 3D Printed Technology

This paper presents an innovative framework for fast-developing broadband, low-profile, dual-polarized crossdipole antennas. This approach integrates a broadband, petalshaped cross dipole with a 3D-printed structure comprising multi-layers of high permittivity material with varying heights and radii. In the absence of a theoretical model and a satisfactory initial design, the design process employs the enhanced Treestructured Parzen Estimator Bayesian Optimization (TPE-BO) algorithm, a significant advancement over traditional Bayesian methods, for effective hyper-parameter tuning to streamline the design. The proposed method only adjusts the high dielectric constant loading (HDL) structure without changing the original dimensions of the dipole. Compared to conventional optimization algorithms integrated within CST, this method efficiently achieves the desired broadband and low profile performance. Meanwhile, the mechanism of the HDL structure in increasing the bandwidth (BW) while enabling the dipole’s low profile performance is explained. The proposed approach has been validated by fabricating and measuring a hybrid antenna prototype. The measured results show that the antenna achieves an overlap
BW between the impedance matching BW and the 3 dB gain BW of 79.3% (from 1.62 to 3.75 GHz) with a voltage standing wave ratio (VSWR) of less than 1.6. Additionally, the isolation between the two ports exceeds 20.5 dB. Significantly, this HDL technique notably enhances the BW and reduces the antenna’s profile from 0.2λL to 0.1λL compared to a single petal-shaped cross dipole. Thus, this study provides a simple, effective, and repeatable design strategy for realizing broadband and low profile performance in dipole antennas.