Machine learning-driven design and performance analysis of microstrip antennas for sub-6 GHz/mm Wave 5G networks

Abstract

In the realm of modern communication systems, antennas are crucial components, with the microstrip patch antenna being particularly notable for its low profile and seamless integration. Despite its widespread use, designing this antenna involves complex simulations to optimize parameters, requiring significant expertise and consuming considerable time and energy. To streamline this process, machine learning (ML) algorithms are being utilized. This paper introduces an innovative approach that employs ML techniques to design a rectangular microstrip patch antenna operating within the sub-6 GHz frequency range (1-6 GHz) and the millimeter frequency range (28-40 GHz). The antenna design maintains consistent patch dimensions positioned strategically at the center, with a thorough examination of patch length and width to enhance performance. Datasets are meticulously prepared, covering output parameters such as beam area, directivity, gain, and radiation efficiency across the specified frequency ranges. By employing various ML algorithms, this study conducts a comprehensive analysis to identify the most effective algorithm for accurately predicting antenna characteristics. The K-nearest neighbor (KNN) algorithm achieved high accuracy across all parameters: gain at 94.23% under sub-6 GHz and 95.93% under millimeter frequency range, directivity at 99.02% and 98.59%, radiation efficiency at 93.94% and 94.28%, and beam area at 99.07% and 98.59% respectively. These results optimize microstrip antenna designs and enhance understanding of the relationship between design parameters and performance outcomes with ML.