University of Illinois at Chicago
POORT-DISSERTATION-2019.pdf (24.33 MB)

A Novel Optically Reconfigurable and Conformal Transmitarray

Download (24.33 MB)
posted on 2019-08-01, 00:00 authored by Marco D Poort
Reconfigurable antennas that can adapt to changes in their environment are gaining importance in a number of fields such as mobile communications, vehicular radar, and electromagnetic imaging. Beam steering antennas are one form of reconfigurable antenna that are becoming critical for advanced applications such 5G cellular networks because of their ability to target specific users which can increase signal to noise ratios and spectral recycling which will allow for more users to be reached with a broad band channels that increase connection speeds. 5G networks will also have access to a broader range of the electromagnetic spectrum than 4G networks because they will utilize the mm-wave frequency range which was previously not allocated for cellular systems. A major downside to these mm-wave antennas is that they require a denser network of antennas, sometimes even with a direct line of sight to the target user. This may often not be feasible in environments such as historic places or parks where a large number of visible antennas may detract from the original purpose of the location and create aesthetic problems. Conformal antennas are a way to mitigate the aesthetic impacts of the antenna because it may be curved and hidden inside a structure already present in the existing surroundings. Conformal design can also be important in vehicular antennas, where drag is a concern, or security antennas, where the system may need to be low profile to avoid detection. This work aims to present a conformal beam-steering antenna which utilizes an optical switching method and heuristic design approach to allow for a flexible system that can be utilized in a wide number of applications. The chapter on the theoretical design considerations examines the system model and the compact genetic algorithm that is used to design the antenna array patterns. Heuristic algorithms such as genetic algorithms require a model that accurately describes the overall system but can be rapidly computed such that multiple patterns may be rapidly iterated to find promising results. As such this work develops a simplified transmitarray model that stores critical values and computes pattern data using only standard matrix operations. This chapter also examines the compact genetic algorithm that is used in conjunction with this model to design antenna array patterns for this system. The next chapter focuses on the physical realization of the system and demonstrates how most techniques for constructing the antenna. This chapter also demonstrates the optically tunable surface which is used to alter the transmitarray pattern and steer the main beam. This work shows the techniques to construct the horn antenna using a frame that is 3D printed and laser cut with light weight and low cost plastics that are then coated with copper tape to create the mechanical foundation of the structure. Lastly the system of designing optical masks that can be reliably projected onto the curved system is introduced in this chapter. The final chapter presents an experimental validation of the system and extraction of several key parameters. First this chapter shows that the optically tunable surface may be utilized in a singly periodic fashion such as the current antenna and demonstrates that the antenna is still broadly tunable. The experiment also extracts antenna impedance measurements which are important for impedance matching systems. This chapter concludes by showing the system’s ability to steer a radiation beam and create a connection with a narrow bandwidth to a remote target.



Uslenghi, Piergiorgio LE


Uslenghi, Piergiorgio LE


Electrical and Computer Engineering

Degree Grantor

University of Illinois at Chicago

Degree Level

  • Doctoral

Degree name

PhD, Doctor of Philosophy

Committee Member

Erricolo, Danilo Graglia, Roberto D Metlushko, Vitali Sievenpiper, Daniel F Stroscio, Michael

Submitted date

August 2019

Thesis type



  • en

Issue date


Usage metrics


    No categories selected


    Ref. manager