Machine Learning Assisted Beam-Steering Microstrip Patch Array Antenna Design

Abstract

The rapid evolution of wireless communication technologies, notably the transi tion from 4G to 5G, has driven the need for advanced antenna systems to meet growing demands for seamless connectivity and enhanced performance. This the sis focuses on designing and optimizing beam-steering microstrip patch antennas for 5G communication, with specific emphasis on the sub-6 GHz frequency band and millimeter-wave applications. A comprehensive methodology is presented, in tegrating traditional antenna design principles with machine learning-based pre dictive modeling and optimization techniques. The initial phases of the research involve designing a 2 × 2 microstrip array antenna, simulated for beam-steering applications at 3.5 GHz. A dataset is curated through parametric studies, and eight machine learning models, including Random Forest, Decision Tree Regres sion, XGBoost, and Gaussian Process Regression, have been trained to predict antenna performance metrics such as return loss (S11) and bandwidth. Among these, Decision Tree Regression emerged as the best overall performer, offering the most accurate predictions, while Random Forest also demonstrated close alignment with the actual values. Optimization strategies refined the antenna design by combining machine learning predictions with advanced algorithms like L-BFGS-B, Genetic Algorithms, and Simulated Annealing. The optimized S11 parameter improved significantly, reaching at-51.41 dB compared to the initial-45.21 dB, indicating the superior impedance matching at 3.5 GHz. Similarly, the optimized S22 value reduced to-53.09 dB from-49.57 dB, further enhancing performance. The gain of the antenna increased from 12.82 dBi to 13.58 dBi, and the directivity improved from 12.89 dBi to 13.69 dBi, demonstrating substantial performance enhancements. Port isolation has been maintained with S12 and S21 values below-10 dB. Additionally, the array antenna achieved a beam switching capacity ranging from −17◦ to +17◦, making it highly effective for beam-steering applications. This research contributes to the field by showcasing the potential of machine learning-assisted antenna design and optimization, addressing chal lenges such as computational complexity and design accuracy. The findings hold promise for 5G networks and satellite communications applications, paving the way for more efficient and reliable antenna systems