Mikala Johnson

Sidelobe Canceling for Reconfigurable Holographic Metamaterial Antenna

Accurate and efficient methods for beam-steering of holographic metamaterial antennas is of critical importance for enabling consumer usage of satellite data capacities. We develop an algorithm capable of optimizing the beam pattern of the holographic antenna through software, reconfigurable controls. Our method provides an effective technique for antenna pattern optimization for a holographic antenna, which significantly suppresses sidelobes. The efficacy of the algorithm is demonstrated both on a computational model of the antenna and experimentally. Due to their exceptional portability, low-power consumption, and lack of moving parts, holographic antennas are an attractive and viable technology when combined with proven software-based strategies to optimize performance.

Discrete-dipole approximation model for control and optimization of a holographic metamaterial antenna

Since the discovery of materials with negative refractive index, widely known as metamaterials, it has been possible to develop new devices that utilize a metamaterials ability to control the path of electromagnetic energy. Of particular promise, and already under intensive development for commercial applications, are metamaterial antennas for satellite communications. Using reconfigurable metamaterials in conjunction with the principles of holography, these new antennas can electronically steer the high gain antenna beam required for broadband communications while not having any moving parts, being thinner, lighter weight, and less expensive, and requiring less power to operate than conventional alternatives. Yet, the promise of these devices will not be realized without efficient and effective control and optimization. Toward this end, in this paper a discrete-dipole approximation (DDA) model of a waveguide-fed planar metamaterial antenna is derived. The proposed model is demonstrated to accurately predict the radiation of a two-dimensional metamaterial at a much reduced computational cost to full-wave simulation and at much greater fidelity than simpler models typically used in the field. The predictive capabilities of the derived DDA model opens possibilities for model-based control design for optimal beam steering.

Value function approximation and model predictive control

Both global methods and on-line trajectory optimization methods are powerful techniques for solving optimal control problems; however, each has limitations. In order to mitigate the undesirable properties of each, we explore the possibility of combining the two. We explore two methods of deriving a descriptive final cost function to assist model predictive control (MPC) in selecting a good policy without having to plan as far into the future or having to fine-tune delicate cost functions. First, we exploit the large amount of data which is generated in MPC simulations (based on the receding horizon iterative LQG method) to learn, off-line, the global optimal value function for use as a final cost. We demonstrate that, while the global function approximation matches the value function well on some problems, there is relatively little improvement to the original MPC. Alternatively, we solve the Bellman equation directly using aggregation methods for linearly-solvable Markov Decision Processes to obtain an approximation to the value function and the optimal policy. Using both pieces of information in the MPC framework, we find controller performance of similar quality to MPC alone with long horizon, but now we may drastically shorten the horizon. Implementation of these methods shows that Bellman equation-based methods and on-line trajectory methods can be combined in real applications to the benefit of both.

Extremum-seeking control of the beam pattern of a reconfigurable holographic metamaterial antenna.

Robust, continuous, and software-defined beam pattern control of holographic metamaterial antennas is necessary to realize the potential of these low-power-consumption, thin, lightweight, inexpensive antennas for consumer usage of satellite communication. We present a complete feedback control approach that enables adaptive control of the radiation pattern for the electronically scanned metamaterial antenna that is robust to measurement noise and is able to continuously optimize performance throughout changing environmental conditions and antenna characteristics. The physical size, weight, and cost advantages of the metamaterial antenna make it an attractive technology when paired with robust and adaptive on-board software strategies to optimize antenna performance and self-tune for various environmental conditions.

Sidelobe canceling on a reconfigurable holographic metamaterial antenna

Holographic metamaterial antennas are a promising technology to increase consumer usage of satellite capacity by providing an economical means of connectivity on mobile platforms. Metamaterial antennas are built for portability - thin, lightweight, low-power consuming, and having no moving parts - achieving precision beam-steering electronically. We develop an optimization algorithm capable of performing adaptive optimization of antenna patterns. The efficacy of the algorithm is demonstrated experimentally.

An extremum-seeking controller for dynamic metamaterial antenna operation

Continuous beam pattern control of holographic metamaterial antennas during operation is necessary to realize the potential of these devices for mass usage of satellite communication. In this brief work, we introduce a feedback control approach that enables the dynamic operation of the metamaterial antenna by demonstrating an adaptive controller that is designed to optimize beam performance throughout changing environmental conditions and, thus, changing metamaterial response. We show in this work how the physical advantages of the metamaterial antenna can be enhanced when paired with adaptive software strategies to self-tune for varying environmental conditions.

Data Driven Control of Complex Optical Systems

Advances in data science are revolutionizing the characterization and control of complex optical systems, including the ultra-fast laser and the reconfigurable holographic metamaterial antenna. Methods from data science include machine learning, dimensionality reduction, and compressive sensing. We present these techniques on two optical systems.