基于改进型径向基函数网络的功放非线性建模

Nonlinear modeling of power amplifier based on improved radial basis function networks

针对功率放大器(PA)的非线性建模,提出了改进型径向基函数神经网络(RBFNN)模型。首先,在该模型的输入端加入延迟交叉项和输出反馈项,利用正交最小二乘法提取模型的权值以及隐含层的中心;然后,采用15MHz带宽的宽带码分多址(WCDMA)三载波信号对Doherty功放进行测试,其归一化均方误差(NMSE)可以达到-45dB;最后,通过逆F类功放对模型的普遍适用性进行验证。仿真结果表明,该模型能够更加真实地拟合功率放大器的特性。

Aiming at the nonlinear modeling of Power Amplifier （PA）, an improved Radial Basis Function Neural Networks （RBFNN） model was proposed. Firstly, time-delay of cross terms and output feedback were added in the input. Parameters （ weigths and centers） of the proposed model were extracted using the Orthogonal Least Square （OLS） algorithm. Then Doherty PA was trained and validated successfully by 15 MHz three-carrier Wideband Code Division Multiple Access （WCDMA） signal, and the Normalized Mean Square Error （NMSE） can reach -45 dB. Finally, the inverse class F power amplifier was used to test the universality of the model. The simulation results show that the model can more truly fit characteristics of power amplifier.