Akademik Veri Yönetim Sistemi
Electronics (Switzerland), cilt.12, sa.7, ss.1-13, 2023 (SCI-Expanded, Scopus)
In this work, a computationally efficient method based on data-driven
surrogate models is proposed for the design optimization procedure of a Frequency
Selective Surface (FSS)-based filtering antenna (Filtenna). A Filtenna acts as
a module that simultaneously pre-filters unwanted signals, and enhances the
desired signals at the operating frequency. However, due to a typically large
number of design variables of FSS unit elements, and their complex
interrelations affecting the scattering response, FSS optimization is a
challenging task. Herein, a deep-learning-based algorithm,
Modified-Multi-Layer-Perceptron (M2LP), is developed to render an accurate behavioral
model of the unit cell. Subsequently, the M2LP model is applied to optimize FSS
elements being parts of the Filtenna under design. The exemplary device
operates at 5 GHz to 7 GHz band. The numerical results demonstrate that the
presented approach allows for an almost 90% reduction of the computational cost
of the optimization process as compared to direct EM-driven design. At the same
time, physical measurements of the fabricated Filtenna prototype corroborate
the relevance of the proposed methodology. One of the important advantages of
our technique is that the unit cell model can be re-used to design FSS and
Filtenna operating various operating bands without incurring any extra
computational expenses.