Inverse design of an ultra-wideband endfire grooved half-mode waveguide (G-HMWG) antenna based on the CNN approach

Abstract This study presents an AI-assisted inverse design methodology for a compact and ultra-wideband grooved half-mode waveguide (G-HMWG) end-fire antenna. A parametric dataset was generated using CST full-wave simulations, and the corresponding radiation-pattern vectors were used to train a one-dimensional convolutional neural network (1D-CNN) for predicting the optimal geometrical parameters. The optimized multi-unit-cell antenna achieves stable end-fire radiation across 6–10 GHz, with |S₁₁| < –10 dB, peak gain above 11 dBi, and sidelobe suppression better than –13 dB, while reducing the physical length by 34% compared to conventional designs. A fabricated prototype was measured and showed excellent agreement with simulations, validating the effectiveness of the AI-driven optimization approach. The results demonstrate that integrating deep learning with electromagnetic modeling enables rapid, accurate, and scalable development of compact high-performance end-fire antennas for next-generation wireless, radar, and sensing applications.