Optimization of parasitic reconfigurable aperture antennas with a hybrid direct-Newton approach

Reconfigurable aperture (RECAP) antennas consist of a regular array of reconfigurable elements whose state can be changed dynamically, supporting beamforming, null-steering, adaptive matching, frequency agility, etc. Finding the required state of the reconfigurable loads for a given application is difficult, due to the non-convex and non-linear nature of the optimization problem, and generic unstructured global optimization methods such as genetic algorithms and particle swarm optimization have been proposed. Although appropriate for off-line optimization, such unstructured methods are computationally intensive and may not be appropriate for dynamic in-situ optimization, indicating the need for methods that exploit the problem structure directly. To this end, a novel method for optimization of parasitic RECAPs for beamforming and null-steering is presented, which combines direct optimization of a reduced-order reflection model of the RECAP with efficient Newton root optimization. Initial simulations presented herein suggest that the developed method may be orders of magnitude more efficient for beamforming and null-steering than unstructured optimization methods.