A multi-objective optimization based doherty power amplifier and its matching network optimization method

In the actual design process of traditional power amplifiers, there is a problem of being cumbersome and unable to simultaneously meet low power and saturation modes. Therefore, an improved multi-objective optimization algorithm proposed by decomposition is introduced to optimize its matching network to achieve overall optimization design of power amplifiers. The algorithm, matching network, and optimized power amplifier performance are simulated and verified. The experimental outcomes denote that on the logic function with Zener diode transistor, the proposed algorithm has a mean generation distance index of 5.03E-3, which is lower than most algorithms. Its overall comprehensive performance is better than the comparison algorithm, and compared to the comparison algorithm, it converges more quickly in the early stage of iteration on 1 and 2, and tends to stabilize in the 40th generation, and completes convergence in the 80th generation. In addition, the optimal solution has already begun to appear around the 25th generation and reached saturation around the 70th generation. At the same time, in the actual working bandwidth, the optimized power amplifier saturation efficiency reaches 51.5%~61.9%, and the efficiency at 6dB power backoff is about 44.4%~56.5%. Overall, the algorithm proposed in the study is effective in optimizing power amplifiers and their matching networks, effectively solving the problem of insufficient efficiency in low power modes in traditional designs.