Abstract
A novel generalizable surrogate modeling approach is specifically developed for frequency reconfigurable antennas. The generalizable modeling processes is based on the rigorous mathematical derivation, including the solution of a non-linear overdetermined system, the optimization in the complex field, and the interpolation in multi-dimensional continuous space. As a post-processing method, the approach can convert the discrete data of CAD simulation to a surrogate model. Subsequently, a reconfigurable UWB antenna with a tunable notch-band is taken as an example to demonstrate that the surrogate modeling approach is feasible, effective, and precise. It also has the flexible ability to adapt to strict requirements and complicated scenarios. The proposed surrogate model is a good candidate for the interface standard between a reconfigurable antenna and signal processing part in a Cognitive Radio system.
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