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JCSIS

Optimization of Antenna Design Using Machine Learning Algorithms in IoT-Based Applications

Khadija ShazlyLima HongouHakan Khan
Volume 4

Abstract

As Internet of Things (IoT) devices continue to proliferate, the demand for efficient and compact antenna designs has grown significantly to support reliable wireless communication. This study explores the application of machine learning algorithms in optimizing antenna performance parameters such as gain, bandwidth, radiation pattern, and efficiency for IoT-based applications. By leveraging supervised and unsupervised learning models, including support vector machines, decision trees, and genetic algorithms, the design process is accelerated and guided toward optimal configurations. The methodology involves training models on a dataset comprising various geometric and material properties, allowing predictive analysis and iterative refinement of antenna prototypes. The results demonstrate that machine learning techniques can significantly reduce design time, improve performance outcomes, and enable adaptive tuning in dynamic IoT environments. The integration of intelligent algorithms into antenna design offers a transformative approach to meeting the evolving connectivity needs of smart devices.

Keywords

Antenna design, machine learning, IoT, optimization, wireless communication, smart devices

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