Blind equalization algorithm based on complex support vector regression

被引：0
|
作者：
Yang L. [1 ]
Chen L. [1 ]
Zhao B. [1 ]
Zhang G. [1 ]
Li Y. [1 ]
机构：
[1] School of Information Science and Engineering, Lanzhou University, Lanzhou
来源：
Tongxin Xuebao/Journal on Communications | 2019年 / 40卷 / 10期
关键词：
Blind equalization; Complex support vector regression; Hilbert space; Kernel function; Multi-modulus algorithm;
D O I：
10.11959/j.issn.1000-436x.2019199
中图分类号：
学科分类号：
摘要：
A new blind equalization algorithm for complex valued signals was proposed based on the framework of complex support vector regression(CSVR). In the proposed algorithm, the error function of multi-modulus algorithm (MMA) was substituted into CSVR to construct the cost function, and the regression relationship was established by widely linear estimation, and the equalizer coefficients were determined by the iterative re-weighted least square (IRWLS) method. Different from spliting the complex valued signals into real valued signals used in support vector regression, the Wirtinger's calculus was used in complex support vector regression to analyze the complex signals directly in the complex regenerative kernel Hilbert space. Simulation experiments show that for QPSK modulated signals, compared with the blind equalization algorithm based on support vector regression, the equalization performance of the proposed algorithm is significantly improved in linear channel and nonlinear channel by choosing appropriate kernel function and iterative optimization method. © 2019, Editorial Board of Journal on Communications. All right reserved.
引用
收藏
页码：180 / 188
页数：8
相关论文
共 23 条
  • [1] Sato Y., A method of self-recovering equalization for multilevel amplitude-modulation systems, IEEE Transactions on Communications, 23, 6, pp. 679-682, (1975)
  • [2] Benveniste A., Goursat M., Blind equalizers, IEEE Transactions on Communications, 32, 8, pp. 871-883, (1984)
  • [3] Yang J., Werner J.J., Dumont G.A., The multimodulus blind equalization algorithm, 13th International Conference on Digital Signal Processing, pp. 127-130, (1997)
  • [4] Yang J., Werner J.J., Dumont G.A., The multimodulus blind equalization and its generalized algorithms, IEEE Journal on Selected Areas in Communications, 20, 5, pp. 997-1015, (2002)
  • [5] Ma S.J., Peng H., Wang B., Sparse adaptive normal mode blind equalization algorithm for sparse multipath channels, Journal on Communications, 38, 1, pp. 149-157, (2017)
  • [6] Altay Y.A., Kremlev A.S., Gandalipov E.R., Et al., On the use of the statistical methods for biomedical signals and data processing, 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 1129-1134, (2019)
  • [7] Vapnik V.N., The Nature of Statistical Learning Theory, (2000)
  • [8] Steven M.K., Fundamentals of Statistical Signal Processing: Estimation Theory, 1, (2014)
  • [9] Bouboulis P., Theodoridis S., Mavroforakis C., Et al., Complex support vector machines for regression and quaternary classification, IEEE Transactions on Neural Networks and Learning Systems, 26, 6, pp. 1260-1274, (2015)
  • [10] Santamar'Ia I., Pantaleon C., Vielva L., Et al., Blind equalization of constant modulus signals using support vector machines, IEEE Transactions on Signal Processing, 52, 6, pp. 1773-1782, (2004)
123