Design of high performances electronically reconfigurable antenna arrays by means of parasitic structures and RF-MEMS switches

This work explores the development of smart array antennas, able to electronically change their beam patterns through reconfigurable linear, planar, or conformal parasitic structures controlled by mean of a set of electronic switches namely the radio frequency micro-electromechanical switches (RF-MEMS). The motivation of this study is to extend and make competitive the use of reconfigurable antenna arrays in areas such as the Internet of Things (IoT) and wireless sensor networks. These hot research areas require a capillary distribution of sensors characterized by compact dimensions and limited cost. The radiating systems play a key role in such applications since the improvement of the radiating properties can dramatically reduce the power consumption of such sensors and extend their operative range. Standard smart antenna arrays such as phased, or fully adaptive arrays are extremely expensive, expensive and difficult to control. In this work, the use of smart reconfigurable antenna arrays that do not require expensive digital variable phase shifters and attenuators has been considered. The proposed antenna structures are based on reconfigurable parasitic structures, that thanks to suitable RF-MEMs switches are able to obtain a desired beam pattern, by steering the main beam in a given direction, removing eventually interfering signals and maximizing the signal-to-noise ratio SNR. The versatility, simplicity, and low cost of such antennas make them particularly suitable for the emergent wireless sensor networks and IoT applications. Theoretical background, design guidelines, and suggestions for their fabrication are detailed in this work. Some antenna models for different practical applications are designed, optimized, numerically assessed with commercial electromagnetic simulators, and commented to provide a valid alternative to researchers involved in antenna design for IoT applications.