Precise control of reflection response in bandwidth-enhanced planar antennas

Abstract

In this work, the issues of bandwidth enhancement of planar antennas and the relevance of precise and automated response control through numerical optimization have been investigated. Using an example of a planar antenna with parasitic radiator we illustrate possible effects of even minor modifications of the antenna geometry (here, applied to the ground plane) on its reflection performance. In particular, a proper handling of geometry parameters may lead to considerable broadening of the antenna bandwidth. For the sake of computational efficiency, the adjustment of geometry parameters is carried out using surrogate‐based optimization methods exploiting coarse‐discretization EM simulations as the underlying low‐fidelity antenna model. Additionally, suitably defined penalty function allows us to precisely control the maximum in‐band reflection so that sufficient margin to accommodate possible manufacturing tolerances can be achieved. The optimized designs of the two antenna structures considered in this work exhibit over 1.75 GHz (>31%) and 2.15 GHz (>38%) bandwidth, respectively, for the center frequency of 5.6 GHz. Simulation results are validated using measurements of the fabricated prototypes. Comparison with state‐of‐the‐art designs is also provided. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:653–659, 2016.

Computationally feasible narrow-band antenna modeling using response features

“…The set of training samples is denoted as X T = { x , x 2 , …, x N } ⊂ X . The corresponding EM model responses R ( x k ) are acquired beforehand.…”

“…Its basic formulation is recalled here for the convenience of the reader. We assume that X B = { x , x 2 , …, x N } is a base set, such that the responses f ( x j ) are known for j = 1, 2, …, N (here, f denotes a function of interest). Here, we use ordinary kriging that estimates the function f as f p ( x ) = µ + ε( x ), where µ is the mean of the response at base points, and ε is the error with zero expected value, and with a correlation structure being a function of a generalized distance between the base points.…”

“…Design of modern antenna structures heavily relies on fullwave electromagnetic (EM) simulation models. [1][2][3][4] Highfidelity EM analysis is indispensable to ensure accurate evaluation of antenna performance, particularly for compact antennas, antenna arrays with considerable cross-coupling between radiators, or when environmental components have to be considered (connectors, housing, installation fixtures). Unfortunately, EM models may be computationally expensive, especially for complex structures.…”

Multi-objective design optimization of antennas for reflection, size, and gain variability using kriging surrogates and generalized domain segmentation

“…There have been numerous techniques developed to speed up the optimization process. Popular methods include utilization of adjoint sensitivities, as well as surrogate‐assisted techniques . Surrogate‐based optimization is founded on the idea of shifting the optimization burden into a faster representation of the structure under design (the surrogate), which can be based on auxiliary data‐driven models or coarse‐discretization EM simulations …”

“…Popular methods include utilization of adjoint sensitivities, 15,16 as well as surrogate-assisted techniques. [17][18][19][20] Surrogate-based optimization is founded on the idea of shifting the optimization burden into a faster representation of the structure under design (the surrogate), which can be based F IGUR E 1 The concept of design space segmentation (illustrated for three-dimensional design space): (A) 2 design objectives: 3 cases shown with no segementation, 2-fold, and 3-fold segmentation, (B) 3 design objectives: 2 cases shown with no segmentation and 2-fold segmentation. The overall volume of the segments is smaller than the volume of the original space and the benefits increase with the number of segments.…”