Rachit Jain, Vandana Vikas Thakare, P.K. Singhal

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Machine learning (ML) will be heavily used in the future generation of wireless communication networks. The development of diverse communication-based applications is expected to boost coverage and spectrum efficiency in relation to conventional systems. ML may be employed to develop solutions in a wide range of domains, such as antennas. This article describes the design and optimization of a circular patch antenna. The optimization is done through ML algorithms. Six ML models, Decision Tree, Random Forest, XG-Boost Regression, K-Nearest Neighbour (KNN), Gradient Boosting Regression (GBR), and Light Gradient Boosting Regression (LGBR), were employed in this work to predict the antenna's return loss (S11). The findings show that all of these models work well, with KNN having the highest accuracy in predicting return loss of 98.5%. The antenna design & optimization process can be accelerated with the support of ML. These developments allow designers to push beyond the limits of antenna technology, optimize performance, and offer novel solutions for emerging applications such as 5G, 6G, IoT, and flexible wireless communication systems).


Circular patch antenna, Machine Learning (ML), Return Loss (S11), KNN, Decision Tree, Random Forest, XG Boost, GBR, LGBR

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