ANTENNA PERFORMANCE PREDICTION USING MACHINE LEARNING

Abstract Antennas are fundamental components of modern communication systems, playing a vital role in wireless connectivity and data transmission. However, traditional antenna design and optimization rely heavily on repeated simulations and prototyping, which are both time- consuming and computationally expensive. To address these challenges, this project proposes the development of a machine learning-based model capable of predicting antenna performance parameters—such as gain, return loss, bandwidth, and radiation pattern—based on design and operating conditions. By leveraging artificial intelligence techniques, the proposed approach aims to significantly reduce design time and cost while improving prediction accuracy and efficiency. This research demonstrates the potential of machine learning as a powerful tool for accelerating antenna development and enhancing overall performance prediction capabilities in the rapidly evolving field of wireless communication. Keywords Antenna Design, Machine Learning, ArtificialIntelligence, Wireless Communication, Bandwidth, Radiation Pattren, Design Optimization ,Computional Effeciency