Circularly Polarized Fabry-Perot Cavity Sensing Antenna Design using Generative Model

TL;DR

This paper introduces a deep generative adversarial network to efficiently evaluate and optimize circularly polarized Fabry-Pérot cavity antennas for S-band sensing, significantly reducing computational costs.

Contribution

A novel GAN-based surrogate model for rapid evaluation and optimization of FPC antenna designs in S-band sensing applications.

Findings

Achieved an antenna with 0.4 dB axial ratio, 7.5 dB gain, and 269 MHz bandwidth.

Reduced computational effort in antenna design evaluation.

Demonstrated effective design optimization using deep learning.

Abstract

We consider the problem of designing a circularly polarized Fabry-P'erot cavity (FPC) antenna for S-band sensing applications such as satellite navigation and communication. The spatial distribution of peripheral roughness of the unit cell of FPC's partially reflecting surface (PRS) serves as an important design optimization criterion. However, the evaluation of each candidate design using a full-wave solver is computationally expensive. To this end, we propose a deep generative adversarial network (GAN) for realizing a surrogate model that is trained with input-output pairs of antenna designs and their corresponding patterns. Using the GAN framework, we quickly evaluate the characteristics of a large volume of candidate designs and choose the antenna design with an axial ratio of 0.4 dB, a gain of 7.5 dB, and a bandwidth of 269 MHz.