Power amplifier design optimization method based on PSO and Bayesian optimization

In recent years, computer algorithms are widely used in the field of communication equipment design optimization. Aiming at the problem of poor optimization effect of power amplifier matching network method in the design process, the research combines particle swarm algorithm and Bayesian algorithm to construct improved particle swarm algorithm. And according to this algorithm to build class AB power amplifier design optimization method. The improved PSO’s performance is compared and analyzed with other algorithms, and it is found that the accuracy and checking rate of the algorithm are 97.84% and 98.69%, respectively. Subsequently, the optimization method is analyzed with other methods for the actual optimization effect, and the results demonstrate that the actual output power of the proposed optimization method is higher than 41.23 dBm, the power additive efficiency is higher than 69.14%, and the operating frequency is in the range of 2.3 GHz-3.2 GHz, which are better than the comparison methods. The above results show that the improved PSO and optimization method proposed in the study are effective and practical. It can effectively improve the optimization effect of the power amplifier matching network method in the design process, and provide a theoretical basis for the research related to power amplifier design.