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基于BPNN的OFDM系统的HPA预失真 被引量：2

A NN Predistortion Method for Nonlinear HPAs in OFDM Systems
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摘要 本文针对高功率放大器(HPA)的非线性失真导致OFDM(Orthogonal Frequency Division Multiplexing)系统传输性能下降问题,采用两个类似结构的单输入单输出BP神经网络串联后级联HPA实现其预失真,前一网络是HPA的AM-AM特性的逆模型,用来实现HPA的幅度预失真,后一网络是HPA的AM-PM特性模型,回避了其逆模型的建立,实现了HPA更高精度的相位预失真,提高了整体预失真效果。仿真结果显示了即使输入回退只有2.93dB,带外谱增长仍能降低约10dB,表明该方案能够方便高效地实现OFDM系统中HPA的自适应预失真,大大提高OFDM系统的传输性能。 To circumvent the transmission performance degradation of Orthogonal Frequency Division Multiplexing （OFDM） systems due to the nonlinear HPA, a new predistorter is presented to predistort HPA which consists of two similar single-input and single-output Neural Networks （NNs） in series. The former NN is the inverse model of HPA＇s AM-AM characteristics to compensate its amplitude and the latter the model of AM-PM characteristics to compensate its phase. The phase predistortion is implemented just by its model rather its inverse model, which ensures it of much higher precision. Simulation results show that even though input back-off is as low as 2.93dB, the spectral regrowth can be reduced about 10dB. The proposed scheme can adaptively predistort HPA effectively and simply, and improve transmission performance of OFDM system greatly.
作者 崔华
机构地区 长安大学信息工程学院
出处 《电子与信息学报》 EI CSCD 北大核心 2009年第6期1451-1454,共4页 Journal of Electronics & Information Technology
基金 国家"863"计划项目(2006AA01A116) 国家创新基金(06026225101735)资助课题
关键词 OFDM 高功率放大器 BP神经网络 预失真 OFDM High Power Amplifier （HPA） BP neural network Predistortion
分类号 TN919.72 [电子电信—通信与信息系统]
  • 相关文献

参考文献9

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  • 2Lim Sunmin and Eun Changsoo. Predistorter design for a memoryless nonlinear high power amplifier using the pth-order inverse method for OFDM systems. Proc IEEE on Intelligent Signal Processing and Communication Systems, Hong Kong, 2005: 217-220.
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同被引文献12

  • 1艾渤,张涛涛,潘长勇,杨知行,王勇,赵怀勋.高功率放大器的相位失真对系统性能的影响(英文)[J].系统仿真学报,2007,19(2):424-428. 被引量：10
  • 2HE Zhi-yong, GE Jian-hua, GENG Shu-jian, etal. An improved look-up table predistortion technique for HPA with memory effects in OFDM systems[J]. IEEE Transactions on Broadcasting, 2006, 52(1):87-91.
  • 3CHOI Sung-ho, JEONG Eui-rim, LEE Y H. Adaptive predistortion with direct learning based on piecewise linear approximation of amplifier nonlinearity [J]. IEEE Transactions on Microwave Theory and Techniques, 2009, 57(5):1119-1128.
  • 4HAMMI O, GHANNOUCHI F M. Twin nonlinear tow-box models for power amplifiers and transmitters exhibiting memory effects with application to digital predistortion[J]. IEEE Microwave and Wireless Components Letters, 2009, 19(8) 530-532.
  • 5RAWAT M, RAWAT K, GHANNOUCHI F M. Adaptive digital predistortion of wireless power amplifiers/ transmitters using dynamic real-valued focused time-delay line neural networks [J]. IEEE Transactions on Microwave Theory and Techniques, 2010, 58(1) :95-104.
  • 6DENNIS R M, MA Z, KIM J. A generalized memory polynomial model for digital predistortion of RF power amplifier [J]. IEEE Transactions on Signal Processing, 2006, 54(10) :3852-3860.
  • 7HAMMI O, GHANNOUCHI F M, VASSILAKIS B. A compact envelope-memory polynomial for RF transmitters modeling with application to baseband and RF-digital predistortion[J].IEEE Microwave and Wireless Components Letter, 2008, 18(5) : 359-361.
  • 8JARDIN P, BAUDOIN G. Filter lookup table method for power amplifier linearization [J]. IEEE Transactions on Vehicular Technology, 2007, 56 (3) : 1076-1087.
  • 9MKADEM F, BOUMAIZA S. Extended Hammerstein behavioral model using artificial neural networks [J].IEEE Transactions on Microwave Theory and Techniques, 2009, 57(4) : 745-751.
  • 10AI Bo, YANG Zhi-xing, Analysis on LUT based PAN Chang-yong, et al. predistortion method for HPA with memory [J ]. IEEE Transactions on Broadcasting, 2007, 53(1) . 127-131.

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电子与信息学报
2009年 第6期

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