Comparison of direct learning and indirect learning predistortion architectures
Comparison of direct learning and indirect learning predistortion architectures
复制标题
DOI：
10.1109/iswcs.2008.4726067
复制
发表时间：
2008-12
期刊：
影响因子：
--
通讯作者：
H. Paaso;A. Mämmelä
中科院分区：
文献类型：
--
作者：
H. Paaso;A. Mämmelä
通信系统中的功率放大器本质上是非线性的。数字预失真器可以补偿这些非线性效应。本文比较了两种记忆多项式预失真器，包括直接和间接学习结构。据我们所知，没有类似的比较发表。这两种结构都是自校正控制的特殊情况。利用Matlab软件对预失真器进行了建模，分析了功放的非线性效应及其数字补偿。仿真结果表明，记忆多项式模型在大幅度下存在收敛问题，也存在表示精度问题。我们观察到，补偿的结果也取决于振幅，而不仅仅是频率。线性化的结果表明，直接学习架构实现了更好的性能，在几乎所有的情况下。
Power amplifiers in a communication system are inherently nonlinear. Digital predistorters can compensate these nonlinearity effects. In this paper, two memory polynomial predistorters including direct and indirect learning architectures are compared with each other. To the best of our knowledge, no similar comparisons have been published. Both of these architectures are special cases of the self-tuning control. We have modeled predistorters and analysed nonlinear effects of a power amplifier and their digital compensation by using Matlab¿. Simulation results show that the memory polynomial model has convergence problems at large amplitudes and also problems of accuracy of representation. We observed that the results of the compensation depend also on the amplitude, not only on the frequency. The results of the linearisation show that the direct learning architecture achieves a better performance in almost all cases.