Design and optimization of microstrip patch antenna for sub-6 ghz 5g communication based on machine learning

The significant surge in demand for fifth-generation wireless systems in the sub-6 GHz frequency range requires small, wideband, high-efficiency antennas. Microstrip patch antennas are low profile and easy to incorporate, but are limited in bandwidth and efficiency. This thesis studies a machine-learning-based methodology for the design and optimization of inset-fed slotted rectangular MPAs for fifth-generation applications. More than 200,000 HFSS simulations were used to study the non-linear relationship between the return loss and the antenna design characteristics. A reproducible machine learning pipeline with integrated feature engineering, cross-validation, and hyperparameter tuning ensured robust prediction. Gradient Boosting achieved excellent accuracy and generalization, and was used for bandwidth-aware MPA optimization. The optimized MPA displayed matched bands at 3.56 and 6.03 GHz, an S11 of -18 dB, fractional bandwidths of 3.2% and 4.2%, and an efficiency of 84%. Validation with HFSS simulation showed a near match. The proposed ML-guided design procedure achieves an order-of-magnitude reduction in design time and demonstrates the potential for sustainable, scalable antenna design.