Wang Jiayi, Zhou Shaohua
School of Micro-Nano Electronics, Zhejiang University, Hangzhou 310058, China.
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310058, China.
Micromachines (Basel). 2023 Aug 27;14(9):1673. doi: 10.3390/mi14091673.
Machine learning methods, such as support vector regression (SVR) and gradient boosting, have been introduced into the modeling of power amplifiers and achieved good results. Among various machine learning algorithms, XGBoost has been proven to obtain high-precision models faster with specific parameters. Hyperparameters have a significant impact on the model performance. A traditional grid search for hyperparameters is time-consuming and labor-intensive and may not find the optimal parameters. To solve the problem of parameter searching, improve modeling accuracy, and accelerate modeling speed, this paper proposes a PA modeling method based on CS-GA-XGBoost. The cuckoo search (CS)-genetic algorithm (GA) integrates GA's crossover operator into CS, making full use of the strong global search ability of CS and the fast rate of convergence of GA so that the improved CS-GA can expand the size of the bird nest population and reduce the scope of the search, with a better optimization ability and faster rate of convergence. This paper validates the effectiveness of the proposed modeling method by using measured input and output data of 2.5-GHz-GaN class-E PA under different temperatures (-40 °C, 25 °C, and 125 °C) as examples. The experimental results show that compared to XGBoost, GA-XGBoost, and CS-XGBoost, the proposed CS-GA-XGBoost can improve the modeling accuracy by one order of magnitude or more and shorten the modeling time by one order of magnitude or more. In addition, compared with classic machine learning algorithms, including gradient boosting, random forest, and SVR, the proposed CS-GA-XGBoost can improve modeling accuracy by three orders of magnitude or more and shorten modeling time by two orders of magnitude, demonstrating the superiority of the algorithm in terms of modeling accuracy and speed. The CS-GA-XGBoost modeling method is expected to be introduced into the modeling of other devices/circuits in the radio-frequency/microwave field and achieve good results.
机器学习方法，如支持向量回归（SVR）和梯度提升，已被引入功率放大器建模并取得了良好效果。在各种机器学习算法中，XGBoost已被证明在特定参数下能更快地获得高精度模型。超参数对模型性能有重大影响。传统的超参数网格搜索既耗时又费力，而且可能找不到最优参数。为了解决参数搜索问题、提高建模精度并加快建模速度，本文提出了一种基于布谷鸟搜索（CS）-遗传算法（GA）-XGBoost的功率放大器建模方法。布谷鸟搜索（CS）-遗传算法（GA）将GA的交叉算子集成到CS中，充分利用了CS强大的全局搜索能力和GA的快速收敛速度，使得改进后的CS-GA能够扩大鸟巢种群规模并缩小搜索范围，具有更好的优化能力和更快的收敛速度。本文以2.5GHz氮化镓E类功率放大器在不同温度（-40°C、25°C和125°C）下的实测输入和输出数据为例，验证了所提建模方法的有效性。实验结果表明，与XGBoost、GA-XGBoost和CS-XGBoost相比，所提的CS-GA-XGBoost能将建模精度提高一个数量级以上，并将建模时间缩短一个数量级以上。此外，与包括梯度提升、随机森林和SVR在内的经典机器学习算法相比，所提的CS-GA-XGBoost能将建模精度提高三个数量级以上，并将建模时间缩短两个数量级，证明了该算法在建模精度和速度方面的优越性。CS-GA-XGBoost建模方法有望被引入射频/微波领域其他器件/电路的建模中并取得良好效果。
