Microstrip Patch Antenna Parameter Optimization Prediction Model using Machine Learning Techniques

Abstract

Microstrip patch antenna (MPA) plays key role in the wireless communication. The research is continuing going to design and optimization of the antenna for various advance application such as 5G and IOT. Artificial intelligence based techniques such as machine learning is also capable to optimize the parameter values and make prediction model based on the given dataset. This research paper shows the machine learning based techniques to optimize the microstrip patch antenna parameters with the performance improvement in terms of accuracy, Mean Squared Error, and Mean Absolute Error. The antenna optimization process may be greatly accelerated using this data-driven simulation technique. Additionally, the advantages of evolutionary learning and dimensionality reduction methods in antenna performance analysis are discussed. To analyze the antenna bandwidth and improve the performance parameters is the main concern of this work.

Design of Circular Microstrip Patch Antenna-Array for 5G Wi-Fi Network

“…With this data-driven simulation approach, the antenna optimization process might potentially be sped up significantly. The benefits of evolutionary learning and dimensionality reduction techniques are also explored, as are their applications in analyzing antenna performance [21].…”