Computationally feasible narrow-band antenna modeling using response features

Abstract

Accurate and low-cost models of input characteristics are of primary importance from the point of view of efficient design of antenna structures. Yet, the modeling problem is difficult because reflection responses are highly nonlinear functions of frequency and change considerably when adjusting antenna dimensions. Conventional approximation-based models require massive datasets and often fail to provide required accuracy. This work demonstrates a possibility of dramatic reduction of the number of training samples, which is achieved by reformulating the modeling problem in a space of appropriately defined response features. The key factor is that dependence of feature point coordinates (both frequency and level) on antenna dimensions is less nonlinear than for the standard responses (S-parameters vs. frequency). Our methodology permits construction of reliable surrogates using much smaller datasets than those required by conventional approaches. Experimental validation indicates that our models provide accuracy that is sufficient for practical antenna design.