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
