A Broadband Asymmetric Doherty Power Amplifier Design Based on Multiobjective Bayesian Optimization: Theoretical and Experimental Validation

In this paper, a new multi-objective Bayesian optimization (BO) algorithm is applied to design a broadband gallium nitride (GaN) based Doherty power amplifier (DPA). The optimization process can be automatically implemented by combing the proposed methodology with a commercial simulation software. The performance of the DPA optimized by the proposed method is compared with those obtained with the initial designed DPA, the DPA optimized by existing BO method, and DPA optimized by the optimizers built-in using the commercial software advanced design system (ADS). The comparison results reveal that the DPA designed with the proposed method can achieve better performance with less optimization time than other optimization methods. The measured results show that the optimized DPA with the proposed method achieves a 9-dB back-off efficiency of 44.6%-65% and a saturated efficiency of 60.5%-78% from 2.0 GHz to 2.6 GHz, with saturated output power varying from 43.8 dBm to 45.2 dBm.

Automated summary

This paper applies a new multi-objective Bayesian optimization algorithm to design a broadband GaN-based Doherty power amplifier. The proposed method can be automatically implemented by combining it with a commercial simulation software. Compared with other optimization methods, the DPA designed with the proposed method achieves better performance in a shorter optimization time.