A novel, broadband, nonlinear behavioral model, based on support vector regression (SVR) is presented in this paper. The proposed model, distinct from existing SVR-based models, incorporates frequency information into its formalism, allowing the model to perform accurate prediction across a wide frequency band. The basic theory of the proposed model, along with model implementation and the model extraction procedure for radio frequency transistor devices is provided. The model is verified through comparisons with the simulation of an equivalent circuit model, as well as experimental measurements of a 10 W Gallium Nitride (GaN) transistor. It is seen that the efficiency prediction throughout the Smith chart, for varying fundamental and second harmonic loads, across a wideband frequency range, show excellent fidelity to the measured results. Device dc self-biasing is also modelled to allow prediction of power amplifier (PA) efficiency, which is shown to be highly accurate when compared with corresponding measured data. Finally, a class-J PA is constructed and measured across the frequency with a large-signal input tone. The resulting measured and modelled values of key PA performance figures are shown to be in excellent agreement, indicating the model is suitable for broadband PA design.

中文翻译：

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基于机器学习的宽带 GaN HEMT 行为模型应用于 J 类功率放大器设计

本文提出了一种基于支持向量回归 (SVR) 的新型宽带非线性行为模型。与现有的基于 SVR 的模型不同，所提出的模型将频率信息纳入其形式，使模型能够在宽频带上执行准确的预测。提供了所提出模型的基本理论，以及模型实现和射频晶体管器件的模型提取过程。该模型通过与等效电路模型仿真的比较以及 10 W 氮化镓 (GaN) 晶体管的实验测量得到验证。可以看出，整个史密斯圆图的效率预测，对于不同的基波和二次谐波负载，在宽带频率范围内，显示出对测量结果的极好保真度。还对器件直流自偏置进行了建模，以允许预测功率放大器 (PA) 效率，与相应的测量数据相比，该效率被证明是高度准确的。最后，构建一个 J 类 PA，并使用大信号输入音调在整个频率范围内进行测量。结果表明，关键 PA 性能数据的测量值和建模值非常一致，表明该模型适用于宽带 PA 设计。