Physics-Informed Graph Neural Networks for the Inverse Design of GHz Reconfigurable Antenna

Cindy Pan, Naveen Verma, James Sturm

Abstract:

Reconfigurable antennas, as a subclass of meta-
surfaces, offer innovative and dynamic capabilities for wireless
communication systems. Specifically, enabling radiation pat-
tern reconfigurability allows for flexible beam steering through
reverse-engineering of antenna parameters such as surface cur-
rent distributions. In this work, we present a physics-informed
machine learning model, leveraging fundamental physics such as
Kirchhoff’s current Law, to predict the switch configurations of
2-dimensional antenna arrays. We utilize a graph neural network
(GNN) to effectively capture the spatial relationships between
radio-frequency (RF) switches and antenna patches, closely
emulating the antenna topology. Simulation results demonstrate
that our approach successfully predicts switch configurations
needed to generate complex far-field radiation patterns.