Machine learning-based theoretical optimization of antenna design

Abstract

The changing communication era demands higher precision and more efficient antenna designs, which require more automated processing methods and more powerful data storage techniques. Currently, machine learning can optimize solutions at high speed and has shown powerful performance in various fields, attracting much attention. This paper summarizes the application of machine learning in antenna design, analyzing its basic concepts and relationship with artificial intelligence and neural networks. This paper also compares the methods and effects produced in each application by analyzing the results of electromagnetic characteristic curves. The results show that machine learning reduces computational errors and computation time and is able to predict the next input and correct the antenna behavior with high accuracy. The key outcomes are quickening the antenna design procedure, decreased mistakes, and wasted time, and increased antenna design accuracy. Machine learning algorithms can assist antenna design in the future to improve antenna accuracy and design efficiency.

User Effects on Mobile Phone Antennas: Review and Potential Future Solutions

“…The optimization algorithm used in this work is based on the Adam algorithm [39], which is a popular optimization technique in deep learning. Tong [40] proposed a machine learning-based theoretical optimization method for antenna design. This method uses neural networks to predict the electromagnetic properties of antennas, which could theoretically be used to optimize antenna design to account for user effects.…”