基于简化Volterra级数的射频功率放大器建模与辨识

Modeling and identification of RF power amplifiers based on simplified Volterra series

目前的射频功率放大器模型通常将其看作无记忆器件,但在宽带通信系统中,功率放大器的记忆效应明显,无记忆模型无法准确描述其输入输出特性。本文对功率放大器的Volterra级数模型进行了简化,在考虑记忆效应的同时,也有效克服了一般Volterra级数模型参数数量过于庞大的缺点。采用观测矩阵维数固定的递推最小二乘算法对模型参数进行辨识,不仅能实时跟踪射频功率放大器非线性特性的变化,而且减小了参数辨识所需的运算量和数据存储空间。

Most existing behavioral models of RF power amplifiers （PAs） assume that PAs are memoryless devices. But in wideband communication systems, PA memory effects are typically observed and memoryless models can no longer accurately depict the input-output relationship of PAs. A novel behavioral model based on simplified Volterra series is proposed to treat memory effects and reduce the complexity of general Volterra model. Since the characteristics of amplifiers change during transmission time, a recursive least squares algorithm with size-fixed observation matrices is developed to update the parameters of the proposed PA model. The identification algorithm can decrease computational complexity and data storage space and facilitate real-time online identification.