Nonlinear behavioral modeling of power amplifiers using radial-basis function neural networks

被引：8
|
作者：
Isaksson, M [1 ]
Wisell, D
Rönnow, D
机构：
[1] Univ Gavle, SE-80176 Gavle, Sweden
[2] Ericsson Telecom AB, SE-80006 Gavle, Sweden
来源：
2005 IEEE MTT-S International Microwave Symposium, Vols 1-4 | 2005年
关键词：
modeling; neural networks; nonlinear distortion; power amplifiers; radio transmitter;
D O I：
10.1109/MWSYM.2005.1517128
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A radial-basis function neural network (RBFNN) is proposed for modeling the dynamic nonlinear behavior of RF power amplifiers. In the model the signal's envelope is used. The model requires less training than a model using both IQ-data. Sampled input and output signals from a power amplifier for 3G were used in the identification and validation. The RBFNN is compared with a parallel Hammerstein model. For a memory depth of one sample the RBFNN gives a better model, in- and out-of-band; for three samples the RBFNN reduces the in-band error more while the Hammerstein model reduces the error out-of-band more.
引用
收藏
页码：1967 / 1970
页数：4
相关论文
共 50 条
  • [41] Hardware implementation of radial-basis neural networks with Gaussian activation functions on FPGA
    Volodymyr Shymkovych
    Sergii Telenyk
    Petro Kravets
    [J]. Neural Computing and Applications, 2021, 33 : 9467 - 9479
  • [42] Nonlinear function learning using optimal radial basis function networks
    Krzyzak, A
    [J]. NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS, 2001, 47 (01) : 293 - 302
  • [43] Nonlinear regression modeling via regularized radial basis function networks
    Ando, Tomohiro
    Konishi, Sadanori
    Imoto, Seiya
    [J]. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2008, 138 (11) : 3616 - 3633
  • [44] Augmented radial basis function neural network predistorter for linearisation of wideband power amplifiers
    Hui, Ming
    Liu, Taijun
    Zhang, Meng
    Ye, Yan
    Shen, Dongya
    Ying, Xiangyue
    [J]. ELECTRONICS LETTERS, 2014, 50 (12) : 877 - 878
  • [45] Performance of radial-basis function networks for direction of arrival estimation with antenna arrays
    ElZooghby, AH
    Christodoulou, CG
    Georgiopoulos, M
    [J]. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 1997, 45 (11) : 1611 - 1617
  • [46] Experience-consistent modeling for radial basis function neural networks
    Pedrycz, Witold
    Rai, Partab
    Zurada, Jozef
    [J]. INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2008, 18 (04) : 279 - 292
  • [47] Dynamical optimal training for behavioral modeling of nonlinear circuit elements based on radial basis function neural network
    Kuo, Ming-Jen
    Lin, Tsung-Chih
    [J]. 2008 ASIA-PACIFIC SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY AND 19TH INTERNATIONAL ZURICH SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY, VOLS 1 AND 2, 2008, : 670 - +
  • [48] Robust adaptive control of nonaffine nonlinear systems using radial basis function neural networks
    Karimi, B.
    Menhaj, M. B.
    Saboori, I.
    [J]. IECON 2006 - 32ND ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS, VOLS 1-11, 2006, : 270 - +
  • [49] Sliding mode control of nonlinear systems using Gaussian radial basis function neural networks
    Efe, MÖ
    Kaynak, O
    Yu, XH
    Wilamowski, BM
    [J]. IJCNN'01: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS, 2001, : 474 - 479
  • [50] NONLINEAR AND DISCONTINUITIES MODELING OF TIME SERIES USING ARTIFICIAL NEURAL NETWORK WITH RADIAL BASIS FUNCTION
    Tierra, Alfonso
    [J]. GEOGRAPHIA TECHNICA, 2016, 11 (02): : 102 - 112
12345