Antenna simulation typically involves modeling and optimization, which are time-consuming and labor-intensive, slowing down antenna analysis and design. This paper presents a prototype of a large language model (LLM)-based antenna design system (LADS) to assist in antenna simulation. LADS generates antenna models with textual descriptions and images extracted from academic papers, patents, and technical reports (either one or multiple), and it interacts with engineers to iteratively refine the designs. After that, LADS configures and runs an optimizer to meet the design specifications. The effectiveness of LADS is demonstrated by a monopole slotted antenna generated from images and descriptions from the literature. To improve gain stability across the 3.1-10.6 GHz ultra-wide band, LADS modifies the cross-slot into an H-slot and changes substrate material, followed by parameter optimization. As a result, the gain variation is reduced while maintaining the same gain level. The LLM-enabled antenna modeling (LEAM) is available at: https://github.com/TaoWu974/LEAM.
@article{arxiv.2504.18271,
title = {Large Language Model-Based Intelligent Antenna Design System},
author = {Tao Wu and Kexue Fu and Qiang Hua and Xinxin Liu and Bo Liu},
journal= {arXiv preprint arXiv:2504.18271},
year = {2025}
}
Comments
Code are available: https://github.com/TaoWu974/LEAM. Accepted by and will be presented in EuCAP 2026, Dublin