An Intelligent Antenna Synthesis Method Based on Random Forest Machine Learning Model

Using antennas in telecommunications and signal processing has undergone a paradigm shift with implementing Machine Learning (ML) methods and techniques. Implementing ML in antenna design addresses the challenges of performance enhancement, design parameter optimization, and adaptive functionalities. This paper evaluates the transformative impact of ML on antenna systems, emphasizing the use of the random forest regression algorithm in the proposed design. A major challenge in this process is the requirement for extensive training datasets, necessitating the simulation of designs using tools like High-Frequency Structure Simulator (HFSS). These simulations must consider various parameters, such as the dimensions of the substrate and patch. Despite these challenges, integrating ML techniques has optimized the design process, resulting in superior antenna performance. Experimental validations are presented to demonstrate the efficacy of ML-driven antenna design methodologies across different frequency bands and application scenarios. The paper highlights the need for interpretable ML models and scalable optimization for complex antenna systems. Ultimately, the research includes the optimization of antenna Design Parameters.