Optimizing the Gain and Directivity of a Microstrip Antenna with Metamaterial Structures by Using Artificial Neural Network Approach

The purpose of this study is to improve the gain and directivity of the microstrip patch antenna by means of metamaterial (MTM) structures. As it is known, antennas have power densities in a certain direction, and the radiation curves of the antennas are shaped by orienting them according to this power density. Based on this feature of the antennas, it is aimed to improve the gain and directivity of microstrip patch antennas with the most suitable structures thanks to metamaterial (MTM) structures. The MTM structures designed are interacted with the antenna. Then, the frequency, gain and directivity of the antenna are trained by the artificial neural network technique to estimate the most appropriate values. As a result, the gain and directivity of the antenna are improved by the interaction of microstrip patch antenna and MTM structures and the results are interpreted on the figures.

Automated summary

This study aims to enhance the gain and directivity of microstrip patch antennas by utilizing metamaterial (MTM) structures. The designed MTM structures interact with the antenna to train its frequency, gain, and directivity using artificial neural network technique. The results show improvement in antenna performance through the interaction with MTM structures.