Low-Wind-Load Broadband Dual-Polarized Antenna and Array Designs Using Sequential Multiphysics Machine-Learning-Assisted Optimization

Low-wind-load designs are increasingly crucial for ultralarge-scale base station arrays operating in the sub-6 GHz band. Here, a sequential multiphysics machine-learning-assisted optimization (MLAO) method is proposed for the rapid design of a compact antenna with aerodynamic favorability and excellent electromagnetic (EM) performance. The total project area is reduced by designing a compact radiator and replacing the traditional metal ground with a topologically innovative metal ground. A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> array formed by the above antenna showcases a remarkable 73% reduction in the wind load, when each antenna element is independently packaged in a small radome. Moreover, the EM performance is greatly enhanced by optimizing the dipole arm topologies. The antenna prototype is fabricated and measured with a broad impedance bandwidth of 3.2–5.0 GHz, isolation higher than 20 dB, and realized gain of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.5 \pm 1.2$ </tex-math></inline-formula> dBi. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> array shows a front-to-back ratio greater than 20 dB, cross-polarization discrimination greater than 15 dB, and realized gain of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$18.6 \pm 1.5$ </tex-math></inline-formula> dBi. These results demonstrate that the proposed antenna is suitable for 5G new radio frequency bands n77/n78/n79.