Cost-efficient EM-driven size reduction of antenna structures by multi-fidelity simulation models

Design of antenna systems for emerging application areas such as the Internet of Things (IoT), fifth generation wireless communications (5G), or remote sensing, is a challenging endeavor. In addition to meeting stringent performance specifications concerning electrical and field properties, the structure has to maintain small physical dimensions. The latter normally requires searching for trade-off solutions because miniaturization has detrimental effects on antenna characteristics, including the impedance matching, gain, efficiency, or axial ratio bandwidth. Furthermore, explicit size reduction is more demanding than optimization with respect to other figures of merit. On the one hand, it is a constrained task with acceptance thresholds set on the bandwidth, gain, etc. On the other hand, optimum solutions are normally located at the boundary of the feasible region, traversing of which is a difficult problem by itself. The necessity of using full-wave electromagnetic (EM) analysis for antenna evaluation only aggravates the problem due to high computational costs associated with numerical optimization algorithms. This paper proposes a procedure for expedited optimization-based miniaturization of antenna structures involving trust-region gradient search and multi-fidelity EM simulations, as well as implicit handling of design constraints using a penalty function approach. The assumed model management scheme is associated with the convergence status of the optimization process with the lowest fidelity model employed at the early stages of the algorithm run and the discretization density of the structure gradually increased to reach the high-fidelity level towards the end of the run. This allows us to achieve a considerable computational speedup without compromising the reliability. Our methodology is demonstrated using two broadband microstrip antennas. The obtained CPU savings exceed seventy percent as compared to the reference procedure involving high-fidelity model only.