Novel adaptive nonlinear predistorters based on the direct learning algorithm
Novel adaptive nonlinear predistorters based on the direct learning algorithm
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DOI：
10.1109/tsp.2006.882058
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发表时间：
2007-01-01
影响因子：
5.4
通讯作者：
DeBrunner, Victor E.
中科院分区：
文献类型：
--
作者：
Zhou, Dayong;DeBrunner, Victor E.
自适应非线性预失真器是补偿数字通信和控制系统中存在的非线性失真的一种有效技术。然而，使用间接学习的可用自适应非线性预失真器对测量噪声敏感并且不能最佳地执行。其他可用的类型要么收敛速度慢，结构复杂，计算昂贵，或者不考虑非线性系统中的记忆效应，如高功率放大器（HPA）。在本文中，我们首先提出了几种新的自适应非线性预失真器的基础上直接学习算法的非线性滤波-x RLS（NFXRLS）算法，非线性伴随LMS（NALMS）算法，和非线性伴随RLS（NARLS）算法。使用这些新的学习算法，我们设计了自适应非线性预失真器的HPA与记忆效应或HPA以下的线性系统。由于直接学习算法，这些新的自适应预失真器优于非线性预失真器，是基于间接学习方法的意义上的归一化均方误差（NMSE），误码率（BER），和频谱再生。此外，我们开发的自适应非线性预失真器的计算效率和/或收敛速度快，相比其他自适应非线性预失真器，使用直接学习，而且可以很容易地实现。我们进一步简化我们提出的算法，探索我们提出的算法的鲁棒性，以及通过检查我们所谓的“瞬时等效线性”（IEL）滤波器的统计特性。仿真结果证实了我们提出的算法的有效性。
The adaptive nonlinear predistorter is an effective technique to compensate for the nonlinear distortion existing in digital communication and control systems. However, available adaptive nonlinear predistorters using indirect learning are sensitive to measurement noise and do not perform optimally. Other available types are either slow to converge, complicated in structure and computationally expensive, or do not consider the memory effects in nonlinear systems such as a high power amplifier (HPA). In this paper, we first propose several novel adaptive nonlinear predistorters based on direct learning algorithms-the nonlinear filtered-x RLS (NFXRLS) algorithm, the nonlinear adjoint LMS (NALMS) algorithm, and the nonlinear adjoint RLS (NARLS) algorithm. Using these new learning algorithms, we design adaptive nonlinear predistorters for an HPA with memory effects or for an HPA following a linear system. Because of the direct learning algorithm, these novel adaptive predistorters outperform nonlinear predistorters that are based on the indirect learning method in the sense of normalized mean square error (NMSE), bit error rate (BER), and spectral regrowth. Moreover, our developed adaptive nonlinear predistorters are computationally efficient and/or converge rapidly when compared to other adaptive nonlinear predistorters that use direct learning, and furthermore can be easily implemented. We further simplify our proposed algorithms by exploring the robustness of our proposed algorithm as well as by examining the statistical properties of what we call the "instantaneous equivalent linear" (IEL) filter. Simulation results confirm the effectiveness of our proposed algorithms.