Optimization of 5G Antenna Using Machine Learning

This paper presents a machine learning (ML)-based approach to optimize 5G base station patch antennas operating in the 3.3-4.2 GHz range. Traditional antenna design methods are often complex and time-consuming, particularly as the demand for high-speed communication is increasing, motivating the use of ML. Due to the lack of existing data for training the ML models, a custom dataset was created to be used in training and testing of the ML models. The optimized antenna achieved a return loss less than -10 dB and a resonant frequency of 3.5 GHz. An array of the optimized antennas was also designed and analyzed for base station applications. To select the best ML model, the project involved training and testing various models. Random Forest was identified as the best model for the single antenna, while Random Tree performed best for the antenna array.