Microstrip Antenna Design Supported by Generative Adversarial Networks

Abstract

We report on the effectiveness of using generative neural networks in an antenna design. We considered the modeling of microstrip antennas as they have significant advantages, such as a low profile, lightness, and ease of manufacture, which make them versatile for various applications. We designed, trained, and analyzed generative models applied to the modeling of these antennas without losing the generalizability of the application of these models to any antenna. We started with a Generative Adversarial Network (GAN), which was trained with data related to the antenna models for operation within the frequency range of 1 to 30 GHz. Using the synthetic data produced by the GAN resulted in antenna designs with dimensions and electromagnetic properties that were very close to the expected values. Next, a model was developed using a Conditional GAN (CGAN), which was trained to generate antenna characteristic data conditioned on an arbitrary central frequency, i.e., 2.4 GHz (generally used for Bluetooth, Wi-Fi, and ZigBee technologies), to enable better control over the process of generating these synthetic data. The CGAN model could satisfactorily generate synthetic data for this frequency range, simultaneously considering substrates with different dielectric permittivities. This study reveals that both generative models could produce synthetic data that were very close to the expected data, as evidenced by the low error values. Additionally, in terms of application, the models could provide both geometries and more than one antenna characteristic (resonance, bandwidth, and quality factor), which is very useful for direct application to practical designs.

Keywords

• generative adversarial network
• convolutional generative adversarial network
• microstrip antenna