# Optimization of compact fractal monopole antenna with partial fractal ground using machine learning approach for multiband applications

## TL;DR

This paper uses machine learning to optimize a compact fractal antenna design for multiband performance, reducing computational effort.

## Contribution

A novel machine learning approach for optimizing compact fractal monopole antennas with partial fractal ground is introduced.

## Key findings

- GPR outperformed SVR in prediction accuracy with an MSE of 0.15 and a score of 0.98.
- The optimized antenna design achieved multiband operation across VHF, UHF, L, S, and C bands.
- Simulated and measured results showed close agreement, validating the effectiveness of the machine learning approach.

## Abstract

In this research, we investigate the integration of machine learning techniques, in particular Gaussian Process Regression (GPR) and Support Vector Regression (SVR), into the optimization of compact microstrip antenna design. Multiband operation with a significant miniaturization is achieved by proposing a unique circular radiating structure with decorative slots and a central star shaped patch. GPR and SVR models were used to predict and optimize critical antenna parameters such as resonant frequency, slot dimensions and patch dimensions. GPR gave better prediction accuracy with an MSE of 0.15, a score of 0.98 and takes longer wall time to converge, while compared to SVR model it converged faster with an MSE of 0.20, and a score of 0.95. The results were validated by close agreement between simulated and measured results, and the optimized design exhibited multiband performance across VHF, UHF, L, S, and C bands. These findings show that machine learning can offer a scalable and efficient alternative to the traditional methods in antenna design. With this approach, it is possible to lower the level of computational effort needed in traditional design methods.