Low-Complexity Stochastic Optimization-Based Model Extraction for Digital Predistortion of RF Power Amplifiers
Low-Complexity Stochastic Optimization-Based Model Extraction for Digital Predistortion of RF Power Amplifiers
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DOI：
10.1109/tmtt.2016.2547383
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发表时间：
2016-04
影响因子：
4.3
通讯作者：
N. Kelly;A. Zhu
中科院分区：
文献类型：
--
作者：
N. Kelly;A. Zhu
提出了一种基于随机优化的低复杂度射频功率放大器（PA）数字预失真模型系数提取方法。所提出的方法使用了一个闭环提取架构，并取代传统的最小二乘（LS）训练与修改版本的同时扰动随机逼近（SPSA）算法，需要一个非常低的数值运算每次迭代，从而大大降低了硬件实现的复杂性。实验结果表明，基于完全闭环随机优化的系数提取方法具有良好的线性化精度，同时避免了传统LS方法中复杂的矩阵运算.
This paper introduces a low-complexity stochastic optimization-based model coefficients extraction solution for digital predistortion of RF power amplifiers (PAs). The proposed approach uses a closed-loop extraction architecture and replaces conventional least squares (LS) training with a modified version of the simultaneous perturbation stochastic approximation (SPSA) algorithm that requires a very low number of numerical operations per iteration, leading to considerable reduction in hardware implementation complexity. Experimental results show that the complete closed-loop stochastic optimization-based coefficient extraction solution achieves excellent linearization accuracy while avoiding the complex matrix operations associated with conventional LS techniques.