Numerically Efficient Miniaturization-Oriented Optimization of an Ultra-Wideband Spline-Parameterized Antenna

Abstract

Design of ultra-wideband radiators for modern handheld applications is a challenging task that involves not only selection of an appropriate topology, but also its tuning oriented towards balancing the electrical performance and size. In this work, a low-cost design of a compact, broadband, splineparameterized monopole antenna has been considered. The framework used for the structure design implements trust-region-based methods, space mapping correction mechanisms, and a meta-optimization loop that permits a gradual increase of the problem dimensionality. The optimized structure is characterized by small dimensions of 11.4 mm × 17.2 mm and an overall footprint of only 195 mm 2 . The radiator operates within 3.1 GHz to 12 GHz bandwidth with the reflection coefficient amplitude level of around -10 dB. The computational cost of the structure design amounts to just 163 simulations of the high-fidelity EM model, which is low having in mind that the antenna is represented by a total of 38 adjustable parameters. The optimized radiator has been compared against state-of-the-art structures from the literature. Benchmarking of the presented optimization algorithm has also been performed. Numerical results have been confirmed by measurements of the fabricated antenna prototype.INDEX TERMS antenna miniaturization; computer-aided design; meta-optimization; spline-based antennas; surrogate-based optimization; trust-region methods; UWB antennas.

An Early History of Optimization Technology for Automated Design of Microwave Circuits

“…Now with Reykjavik University, Iceland, he is surely the most identifiable researcher in our community with respect to all aspects of surrogate methodologies and their applications. Koziel and his team spearheaded innovative electromagnetic simulation-driven and surrogate-based optimization procedures for microwave circuits and antenna design, including variable-fidelity optimization frameworks [209], [210], [211]; surrogate-assisted tuning [191], [212], [213], yield estimation [214], [215], and multi-objective optimization [216], [217], [218], [219], [220]; methodologies for rapid re-design by inverse surrogates [221], [222]; microwave component miniaturization [223], [224]; dimensionally reduced and domain confined surrogates [225], [226], [227]; response feature-based nominal design [228], [229], [230], [231], [232], yield optimization [233], [234], [235], [236], [237], and robust design by tolerance maximization [238], [239], [240].…”