Self-Optimizing Metamaterial Antennas

Abstract

The reconfigurable holographic metamaterial antenna is an attractive new technology for satellite communications, particularly in mobile applications. This antenna is thin, light-weight, consumes little power to operate, and is inexpensive to manufacture. Yet, perhaps its most promising attribute is software-controlled beam steering - this antenna does not require moving parts and is still able to receive or transmit data without interruption to a moving, non-geosynchronous satellite (for example, a satellite in low Earth orbit, medium Earth orbit, or highly elliptic orbit), from a moving platform (for example, a plane, ship, or automobile), or both. However, the antenna cannot achieve continuous, high performance operation without robust, adaptive optimal pattern synthesis. Yet, as the antenna hardware is new and under development, so also has the adaptive software control aspect not been developed. This thesis derives and demonstrates an effective adaptive control approach to manage and improve the beam pattern of the reconfigurable holographic metamaterial antenna (RHMA) continuously during operation when deployed. This approach is developed in three steps. First, a computational model of the antenna that accurately predicts the antenna response is derived and validated to study the RHMA's behavior and experiment with synthesis of patterns. Second, a pattern synthesis approach that cancels sidelobes that typically occur in the RHMA's radiation pattern is designed and proven effective both computationally and experimentally. Finally, an adaptive controller is added to the pattern synthesis, which enables continuous automatic, robust control of the RHMA even in the presence of measurement error and changing environmental conditions. Altogether, the work in this thesis significantly advances the demonstrated capabilities of the RHMA. This thesis is the first work that not only theoretically explores pattern synthesis of the RHMA, but then designs and implements a viable adaptive controller for self-optimizing beam pattern control. The algorithmic infrastructure demonstrated in this work meets the dynamic operational requirements of the RHMA, and as an overall algorithmic package, the mathematical framework provides a robust, machine-intelligent architecture capable of enabling the RHMA technology.