Design of Cavity Backed Slotted Antenna using Machine Learning Regression Model

TL;DR

This paper presents a machine learning regression model that rapidly predicts cavity backed slotted antenna dimensions across 1-8 GHz, reducing design costs and improving accuracy for military and aviation communication systems.

Contribution

It introduces a novel regression-based machine learning approach for antenna design that predicts optimal configurations from reflection coefficient data, streamlining the design process.

Findings

Model accurately predicts antenna dimensions across 1-8 GHz.

Reduces need for physical testing and manual adjustments.

Demonstrates versatility in multi-frequency resonance prediction.

Abstract

In this paper, a regression-based machine learning model is used for the design of cavity backed slotted antenna. This type of antenna is commonly used in military and aviation communication systems. Initial reflection coefficient data of cavity backed slotted antenna is generated using electromagnetic solver. These reflection coefficient data is then used as input for training regression-based machine learning model. The model is trained to predict the dimensions of cavity backed slotted antenna based on the input reflection coefficient for a wide frequency band varying from 1 GHz to 8 GHz. This approach allows for rapid prediction of optimal antenna configurations, reducing the need for repeated physical testing and manual adjustments, may lead to significant amount of design and development cost saving. The proposed model also demonstrates its versatility in predicting multi frequency resonance across 1 GHz to 8 GHz. Also, the proposed approach demonstrates the potential for leveraging machine learning in advanced antenna design, enhancing efficiency and accuracy in practical applications such as radar, military identification systems and secure communication networks.