A new Volterra predistorter based on the indirect learning architecture
A new Volterra predistorter based on the indirect learning architecture
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DOI：
10.1109/78.552219
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发表时间：
1997
期刊：
影响因子：
--
通讯作者：
Changsoo Eun;E. Powers
中科院分区：
文献类型：
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作者：
Changsoo Eun;E. Powers
非线性补偿技术正变得越来越重要。我们提出了一种新的基于Volterra的预失真器，它利用间接学习结构来避免与预失真器相关的一个经典问题，即期望输出事先未知。我们利用间接学习结构和递归最小二乘(RLS)算法。具体地说，我们提出了一种间接Volterra级数模型预失真器，该预失真器独立于特定的非线性模型来补偿系统。16相移键控(PSK)和16正交幅度调制(QAM)都被用来验证新方法的有效性。
Nonlinear compensation techniques are becoming increasingly important. We present a new Volterra-based predistorter, which utilizes the indirect learning architecture to circumvent a classical problem associated with predistorters, namely that the desired output is not known in advance. We utilize the indirect learning architecture and the recursive least square (RLS) algorithm. Specifically, we propose an indirect Volterra series model predistorter which is independent of a specific nonlinear model for the system to be compensated. Both 16-phase shift keying (PSK) and 16-quadrature amplitude modulation (QAM) are used to demonstrate the efficacy of the new approach.