改进BP神经网络的功放有记忆行为模型

提出了一种基于改进误差反向传播神经网络(IBPNN)的具有记忆效应功率放大器(PA)的行为模型。该模型在传统误差反向传播神经网络(BPNN)的基础上利用Levenberg-Marquardt(LM)学习算法和加入动量因子的训练算法更新BPNN的权值和阈值,与传统的BPNN相比只需要更少的训练次数就达到了更高的精度。20MHz带宽三载波WCDMA信号的时域和频域仿真都表明其具有良好的性能,并且由得到的功率放大器(PA)动态特性AM/AM和AM/PM可知,该模型可以很好地描述PA的记忆效应。最后,用16QAM调制的OFDM 20MHz带宽信号的实验证明了该模型具有普遍的适用性。

Behavioral Model of Power Amplifiers Based on Improved BP Neural Network Considering Memory Effects

This paper proposes a behavioral modeling of power amplifiers(PA) based on Improved Back-Propagation Neural Network(IBPNN) considering memory effects.In this model,Levenberg-Marquardt(LM) learning algorithm and training algorithm with momentum factors are adopted to update the weights and biases of BPNN.Comparing to the typical BPNN model,the proposed model can get higher accuracy with less training.Also,the time-and frequency-domain simulations of three-carrier WCDMA signal with 20MHz bandwidth using in this model exhibits good performance of the model.Moreover,the dynamic AM/AM and AM/PM characteristics obtained using the proposed model has demonstrated that the improved BPNN can track and describe the memory effects of the PAs well.Finally,an experiment with 16QAM OFDM signal and 20MHz bandwidth shows the universal of the proposed model.