Blind equalization using the support vector regression via PDF error function

被引：0
|
作者：
Wang, Yang [1 ]
Yang, Ling [1 ]
Wang, Fang [1 ]
Bai, Lu [1 ]
机构：
[1] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou, Peoples R China
来源：
2016 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC), VOL. 2 | 2016年
关键词：
blind equalization; SVR; PDF; SISO; ISI;
D O I：
10.1109/IHMSC.2016.150
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a new blind equalization is addressed based on support vector regression (SVR) for single-input single-out (SISO) channels, which combines the conventional cost function of the SVR with probability density function (PDF) error function. Based on the iterative re-weighted least square (IRWLS) it solves the equalizer coefficients. Simulation performances show that the proposed equalization method performs better than the traditional algorithms such as constant-modulus algorithm (CMA), PDF algorithm and previous SVR algorithm (SVM-Godard, SVR-Sato) under the intersymbol interference (ISI) standard.
引用
收藏
页码：212 / 216
页数：5
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