Blind equalization algorithm based on complex support vector regression

Ling YANG; Liang CHEN; Bin ZHAO; Guolong ZHANG; Yuan LI

doi:10.11959/j.issn.1000-436x.2019199

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Blind equalization algorithm based on complex support vector regression

Correspondences | 更新时间：2024-06-05

- Blind equalization algorithm based on complex support vector regression
- Journal on Communications Vol. 40, Issue 10, Pages: 180-188(2019)
- 作者机构：
  
  兰州大学信息科学与工程学院，甘肃兰州 730000
- 作者简介：
- 基金信息：
  
  The Fundamental Research Fund for the Central Universities(lzujbky-2019-91);The Natural Science Foundation of Gansu Province(20180322)
- DOI：10.11959/j.issn.1000-436x.2019199
  CLC： TN911.5
- Online First：2019-10，
  
  Published：25 October 2019
- 稿件说明：
移动端阅览
Ling YANG, Liang CHEN, Bin ZHAO, et al. Blind equalization algorithm based on complex support vector regression[J]. Journal on Communications, 2019, 40(10): 180-188.
DOI：

Ling YANG, Liang CHEN, Bin ZHAO, et al. Blind equalization algorithm based on complex support vector regression[J]. Journal on Communications, 2019, 40(10): 180-188. DOI： 10.11959/j.issn.1000-436x.2019199.

摘要

基于复数支持向量回归机（CSVR）的框架，提出了一种针对复数信号的新的盲均衡算法，将多模算法的误差函数代入CSVR的惩罚项构造代价函数，利用广泛线性估计建立回归关系，并采用迭代重加权最小二乘方法确定均衡器系数。不同于支持向量回归机对复数信号的实数化处理方式，CSVR利用Wirtinger微积分，将复数信号直接在复数再生核希尔伯特空间进行解析。仿真实验表明，针对QPSK调制信号，在线性信道和非线性信道下，与基于SVR的盲均衡算法相比，通过选取合适的核函数和迭代优化方法，所提算法的均衡性能显著提升。

Abstract

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.

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Keywords

references

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