Quantum Genetic Algorithm for the Design of Miniaturized and Reconfigurable IoT Antennas

This article leverages an automated quantum genetic algorithm (QGA) to design miniaturized antennas for Internet of Things (IoT) devices. The QGA implemented in this work automates the design process and enables the antenna designer to generate static or reconfigurable miniaturized microstrip folded antenna topologies. The QGA is also implemented to generate a pixelation pattern within the antenna radiating surface to optimally tune the antenna operation for IoT communication needs as well as for Wi-Fi operation. In this article, we also present a QGA-optimized folded miniaturized antenna in both static and reconfigurable topologies. The reconfiguration is achieved through a single radio frequency (RF) micro-electro-mechanical switch (RF MEMS) whose location is optimized through the automated process. The automated design process achieves 87% antenna size reduction at 868 MHz compared to a typical patch antenna operating at the same frequency. As a result, the static and reconfigurable antenna prototypes achieve good radiation efficiencies in comparison to their miniature size with acceptable gains thus satisfying the IoT physical and operational constraints. Both antennas are fabricated and tested, where excellent agreement is noticed between simulated and measured results.