Abstract

Over-the-air (OTA) testing is particularly useful in determining the performance of multi-antenna communication devices in real-world environments. Traditional OTA testing technologies include the reverberation chamber (RC) and the multi-probe anechoic chamber. More recently, the reconfigurable OTA chamber (ROTAC), which is a RC that has probes lining its chamber walls, has been proposed. These probes are either driven with a source, possibly combined with a channel emulator, or are terminated with a tunable impedance. Controlling the excitations and terminations on the probes can alter the fields within the chamber and thereby synthesize an antenna response at the device under test (DUT). This dissertation provides understanding of the ROTAC through the development of a moment method (MoM) model. This understanding informs the design of chamber parameters such as the number of probes required, the placement of probes, polarization purity, and chamber size. Additionally, a machine learning (ML) model, named RotacNet, is designed and trained to synthesize a desired antenna response at the DUT accounting for the interactions between the chamber fields and the DUT in the reverberant environment. RotacNet performs remarkably well at synthesizing antenna responses for a DUT comprising two dipole antennas in a simulated environment. The model also proves robust to inputs degraded by noise and to noise at the probes which limits accurate control of the chamber. RotacNet is a compelling example of ML in solving complex non-linear electromagnetics inverse problems. It is anticipated that the principles used to design and train RotacNet could be applied to other disciplines of electromagnetics and achieve similar outcomes.

Degree

PhD

College and Department

Ira A. Fulton College of Engineering; Electrical and Computer Engineering

Rights

https://lib.byu.edu/about/copyright/