Gain Bandwidth Enhancement and Sidelobe Level Stabilization of mmWave Lens Antennas Using AI-Driven Optimization
Rahabu Mwang’amba, Peng Mei, Mobayode O. Akinsolu, Bo Liu, Shuai Zhang
Abstract
This paper explores the transformative potential of artificial intelligence (AI) techniques in optimizing the phase distributions of a lens antenna to significantly enhance the gain bandwidth and stabilize the sidelobe levels at the millimeter-wave band. Through an AI-driven antenna design method (self-adaptive Bayesian neural network surrogate-model-assisted differential evolution for antenna optimization (SB-SADEA), specifically), this work obtains a phase distribution that provides a wide gain bandwidth and stable sidelobe levels from 24 to 33 GHz. A lens antenna with 20 × 20 unit cells is implemented based on the phase distribution. Results show a 1-dB bandwidth of 28.2% and the sidelobe levels have also been lowered compared to the reference design. The optimized lens antenna shows a stable gain with a range of 20.13 dB to 22.16 dB from 24 to 33 GHz, in comparison to the reference design that has a gain range of 16.70 dB to 26.43 dB over the same frequency spectrum. The measured results align well with the simulated results, verifying the effectiveness of the AI-driven antenna design optimization technique in enhancing the performance of a lens antenna.