Surrogate models are widely used in antenna design for optimization efficiency improvement. Currently, the targeted antennas often have a small number of design variables and specifications, and the surrogate model training time is short. However, modern antennas become increasingly complex, which needs much more design variables and specifications, making the training time become a new bottleneck, i.e., in some cases, even longer than electromagnetic (EM) simulation time. Therefore, a new method, called training cost reduced surrogate model-assisted hybrid differential evolution for complex antenna optimization (TR-SADEA), is presented in this article. The key innovations include: 1) a self-adaptive Gaussian process surrogate modeling method with a significantly reduced training time while mostly maintaining the antenna performance prediction accuracy and 2) a new hybrid surrogate model-assisted antenna optimization framework that reduces the training time and increases the convergence speed. An indoor base station antenna with 2G to 5G cellular bands (45 design variables and 12 specifications) and a 5G outdoor base station antenna (23 design variables and 18 specifications) are used to demonstrate TR-SADEA. Experimental results show that more than 90% of the training time and about 20% iterations (simulations and surrogate modeling) are reduced compared to a state-of-the-art method while obtaining high antenna performance.

Gaussian process (GP) is a very popular machine learning method for online surrogate model-assisted antenna design optimization. Despite many successes, two improvements are important for GP-based antenna global optimization methods, including (1) the convergence speed (i.e., the number of necessary electromagnetic simulations to obtain a high-performance design), and (2) the GP model training cost when there are several tens of design variables and/or specifications. In both aspects, state-of-the-art GP-based methods show practical but not desirable performance. Therefore, a new method, called self-adaptive Bayesian neural network surrogate model-assisted differential evolution for antenna optimization (SB-SADEA), is presented in this paper. The key innovations include: (1) The introduction of the Bayesian neural network (BNN)-based antenna surrogate modeling method into this research area, replacing GP modeling, and (2) a bespoke self-adaptive lower confidence bound method for antenna design landscape making use of the BNN-based antenna surrogate model. The performance of SB-SADEA is demonstrated by two challenging design cases, showing considerable improvement in terms of both convergence speed and machine learning cost compared to state-of-the-art GP-based antenna global optimization methods.

This paper proposes a new design of a triple-band dual-polarized indoor base station antenna for mobile communication systems serving the 2G, 3G, 4G, and the new sub-6 GHz 5G applications. The design consists of two orthogonal dipole antennas to provide dual polarization. Each dipole consists of three different radiator types-elliptical dipole, bowtie dipole, and cat-ear shaped arms for different bands. The proposed antenna offers three broad bands with the fractional bandwidths of 31.3% (0.7-0.96 GHz), 55.3% (1.7-3 GHz), and 14% (3.3-3.8 GHz) which can be controlled independently. The design also offers stable radiation patterns within the desired frequency bands, high polarization purity and, a simple feeding structure with a compact size and low profile which make this new design an ideal candidate for indoor mobile base stations serving the 2G, 3G, 4G, and the new sub-6GHz 5G applications.

A dual-band dual-polarized base station antenna for the fifth-generation (5G) mobile system is presented in this paper. The proposed antenna covers the frequency bands from 3.3 to 3.8 GHz (the lower band) and from 4.8 to 5.0 GHz (the upper band) with good isolation between its ports (≥ 20 dB). It consists of two double-oval-shaped dipoles, two double-oval-shaped feeding lines and a cavity reflector. In this design, parts of the dipole antenna structure are used as the feeding lines and it is found that using one arm of the dipole to feed the whole antenna can improve impedance matching. The dual-band performance is achieved by integrating a small oval-shaped loop within the large oval-shaped loop without increasing the size of the radiating patch. The size of the radiating patch is only 0.26λ0 × 0.2λ0 (λ0 is the free-space wavelength at 3.3 GHz). The cavity reflector improves the gain performance and reduces the overall size of the antenna, which is only 0.66 λ0 × 0.66λ0 × 0.2λ0. The antenna has an average realized gain of 7.56 dBi in the lower band and 7.42 dBi in the higher band. Meanwhile, for both bands, the radiation pattern is stable, and the half power beamwidth is within 65° ± 5°. Both simulated and measured results demonstrate that the antenna is a very good candidate for 5G mobile base stations.

An ionic liquid (IL) is used to make antennas for the first time. Unlike water, the proposed material has a large liquid range (−69.8 °C–350 °C), a relative permittivity of≈3, an extremely low dielectric loss, and very stable thermophysical material properties. It can be used for liquid dielectric resonator antennas (DRAs) or as a loading material for performance enhancement. Importantly, the proposed liquid loading scheme is relatively simple and of low cost, but it can markedly improve the antenna performance. As design examples, a liquid-loaded wideband linearly polarized (LP) monopole antenna with an omnidirectional radiation pattern is first presented. Then, the LP antenna is modified to a wideband circularly polarized (CP) antenna with boresight radiation. These antenna examples demonstrate a frequency coverage of 1.25–5 GHz, a wide CP bandwidth, a relatively high gain (>4 dBi), high radiation efficiency >85%, and an electrical size of $0.42\,\,\lambda _{0}\times 0.42 \lambda _{0}\times 0.17\lambda _{0}$ . The experimental results show that the liquid loading works well under a wide range of temperatures. It effectively reduces the antenna electrical size by 40% and improves the impedance matching by 5 dB. Therefore, the proposed liquid loading scheme can be applied to a variety of antenna/RF designs.