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基于自适应扩展卡尔曼滤波与神经网络的HPA预失真算法

吴林煌 苏凯雄 郭里婷 吴子静

吴林煌, 苏凯雄, 郭里婷, 吴子静. 基于自适应扩展卡尔曼滤波与神经网络的HPA预失真算法. 自动化学报, 2016, 42(1): 122-130. doi: 10.16383/j.aas.2016.c150240
引用本文: 吴林煌, 苏凯雄, 郭里婷, 吴子静. 基于自适应扩展卡尔曼滤波与神经网络的HPA预失真算法. 自动化学报, 2016, 42(1): 122-130. doi: 10.16383/j.aas.2016.c150240
WU Lin-Huang, SU Kai-Xiong, GUO Li-Ting, WU Zi-Jing. HPA Predistortion Algorithm Based on Adaptive Extended Kalman Filter and Neural Network. ACTA AUTOMATICA SINICA, 2016, 42(1): 122-130. doi: 10.16383/j.aas.2016.c150240
Citation: WU Lin-Huang, SU Kai-Xiong, GUO Li-Ting, WU Zi-Jing. HPA Predistortion Algorithm Based on Adaptive Extended Kalman Filter and Neural Network. ACTA AUTOMATICA SINICA, 2016, 42(1): 122-130. doi: 10.16383/j.aas.2016.c150240

基于自适应扩展卡尔曼滤波与神经网络的HPA预失真算法

doi: 10.16383/j.aas.2016.c150240
基金项目: 

国家自然科学基金 61401099, 61401100

详细信息
    作者简介:

    苏凯雄 福州大学物理与信息工程学院教授.1988年获得中国科学技术大学通信与电子系统专业硕士学位.主要研究方向为微波通信与图像处理.E-mail:skx@fzu.edu.cn

    郭里婷 福州大学物理与信息工程学院副教授.2006年获得中国科学技术大学通信与信息系统专业博士学位.主要研究方向为数字基带信号的信道编码与调制.E-mail:guoliting@fzu.edu.cn

    吴子静 福州大学物理与信息工程学院博士研究生.2010年获得福州大学物理与信息工程学院学士学位.主要研究方向为LDPC编解码.E-mail:wuzj87@163.com

    通讯作者:

    吴林煌 福州大学物理与信息工程学院助理研究员,博士研究生.2009年获得福州大学物理与信息工程学院硕士学位.主要研究方向为数字预失真与神经网络优化.本文通信作者.E-mail:wlh173@163.com

HPA Predistortion Algorithm Based on Adaptive Extended Kalman Filter and Neural Network

Funds: 

National Natural Science Foundation of China 61401099, 61401100

More Information
    Author Bio:

    Professor at the College of Physics and Information Engineering, Fuzhou University. He received his master degree from University of Science and Technology of China in 1988. His research interest covers microwave communication and image processing

    GUO Li-Ting Associate professor at the College of Physics and Information Engineering, Fuzhou University. She received her Ph. D. degree from University of Science and Technology of China in 2006. Her research interest covers channel coding and modulation of digital baseband signal

    Ph. D. candidate at the College of Physics and Information Engineering, Fuzhou University. She received her bachelor degree from Fuzhou University in 2010. Her research interest covers LDPC coding and decoding

    Corresponding author: WU Lin-Huang Assistant researcher and Ph. D. candidate at the College of Physics and Information Engineering, Fuzhou University. He received his master degree from Fuzhou University in 2009. His research interest covers digital predistortion and neural network optimization. Corresponding author of this paper
  • 摘要: 针对强记忆功放的非线性问题,提出一种基于自适应扩展卡尔曼滤波与神经网络的高功放(High power amplifier, HPA)预失真算法.采用实数固定延时神经网络(Real-valued focused time-delay neural network, RVFTDNN)对间接学习结构预失真系统中的预失真器和逆估计器进行建模,扩展卡尔曼滤波(Extended Kalman filter, EKF)算法训练神经网络,从理论上指出Levenberg-Marquardt(LM)算法是EKF算法的特殊情况,并用李亚普诺夫稳定性理论分析EKF算法的稳定收敛条件,推导出测量误差矩阵的自适应迭代公式.结果表明:自适应EKF算法的训练误差和泛化误差均比LM算法更低,预失真后的邻道功率比(Adjacent channel power ratio, ACPR)比LM算法改善了2dB.
  • 图  1  三层实数固定延时神经网络结构

    Fig.  1  Real-valued focused time-delay neural network architecture with three layers

    图  2  间接学习结构的神经网络预失真系统

    Fig.  2  Indirect learning architecture of neural network predistortion system

    图  3  LM算法和自适应EKF算法的训练误差比较

    Fig.  3  Comparison of training errors using LM algorithm and adaptive EKF algorithm

    图  4  样本量为3000时的泛化误差比较

    Fig.  4  Comparison of generalization errors with 3000 sample data

    图  5  样本量为1000时的泛化误差比较

    Fig.  5  Comparison of generalization errors with 1000 sample data

    图  6  LM和EKF预失真后的信号功率谱密度图比较

    Fig.  6  Comparison of signal power spectrum density with LM and EKF predistortion

    表  1  LM算法和自适应EKF算法的训练误差和泛化误差比较

    Table  1  Comparison of training errors and generalization errors using LM algorithm and adaptive EKF algorithm

    训练误差(样本数3000) 泛化误差(样本数3000) 训练误差(样本数1000) 泛化误差(样本数1000)
    LM算法 $7.5877×10^{-7}$ $1.7229×10^{-6}$ $4.9250×10^{-6}$ $9.4585×10^{-6}$
    自适应EKF算法 $5.4270×10^{-8}$ $7.4936×10^{-7}$ $2.4373×10^{-6}$ $6.8848×10^{-6}$
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  • 收稿日期:  2015-04-22
  • 录用日期:  2015-09-14
  • 刊出日期:  2016-01-01

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