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A metasurface (MS)-inspired low-profile circularly polarized (CP) slot array with a wide CP band and broadband low radar cross section (RCS) is proposed in this communication. The slot array consists of four element antennas, four grounded substrates, and a sequential-rotated feeding network. In terms of radiation performance, the array yields a wide CP band resulting from the CP element antenna and the sequential-rotated feeding network. The CP element antenna is achieved due to the polarization conversion property of the MS-based superstrate. The feeding network with multiple related design parameters is optimized by an artificial intelligence (AI)-driven antenna design method to find the widest bandwidth. In terms of scattering performance, broadband RCS reduction is achieved by using a hybrid RCS reduction technique that combines two destructive interference principles. The $\vert \text{S}_{11}\vert < -10$ dB bandwidth reaches 53.2%, the AR < 3 dB bandwidth reaches 50%, and the RCS reduction bandwidth reaches 147.8% for a low-profile structure with a relatively low number of MS unit cells. A prototype was fabricated and measured. The measured and simulated results are in good agreement. [ABSTRACT FROM AUTHOR]

This paper presents a millimeter-wave direction of arrival estimation (DoA) technique powered by dynamic aperture optimization. The frequency-diverse medium in this work is a lens-loaded oversized mmWave cavity that hosts quasi-random wave-chaotic radiation modes. The presence of the lens is shown to confine the radiation within the field of view and improve the gain of each radiation mode; hence, enhancing the accuracy of the DoA estimation. It is also shown, for the first time, that a lens loaded-cavity can be transformed into a lens-loaded dynamic aperture by introducing a mechanically controlled mode-mixing mechanism inside the cavity. This work also proposes a way of optimizing this lens-loaded dynamic aperture by exploiting the mode mixing mechanism governed by a machine learning-assisted evolutionary algorithm. The concept is verified by a series of extensive simulations of the dynamic aperture states obtained via the machine learning-assisted evolutionary optimization technique. The simulation results show a 25 % improvement in the conditioning for the DoA estimation using the proposed technique. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]