Artificial Intelligence and Machine Learning in Antenna Design Optimization: A Comprehensive Review

Abstract

The field of antenna design and optimization has undergone significant transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. This comprehensive review provides an in-depth overview of the integration of AI and ML in antenna design, highlighting their importance, relevance, and the objectives of the study. It discusses the traditional methods of antenna design, such as analytical models and empirical formulas, and explores how AI and ML techniques enhance design efficiency, improve performance metrics, and enable novel design exploration. The review also covers the application of various AI and ML algorithms, including genetic algorithms, neural networks, support vector machines, particle swarm optimization, and reinforcement learning, to specific antenna design challenges. Case studies and practical implementations of AI and ML in real-world scenarios are presented, along with a discussion on integrating these techniques with traditional electromagnetic simulation tools. The review concludes by addressing current challenges, limitations, and future research directions in AI-driven antenna design, emphasizing the synergistic combination of advanced computational methods, traditional electromagnetic theory, and human expertise. This interdisciplinary approach promises to drive innovation in antenna technology, enabling the development of next-generation communication systems, radar applications, and satellite communications.