DocumentCode :
160457
Title :
Blind equalization of short burst signals based on twin support vector regressor and data-reusing method
Author :
Ling Yang ; Yanping Fu ; Zhifen Yang ; Yanyan Wei
Author_Institution :
Sch. of Inf. Sci. & Eng., Lanzhou Univ., Lanzhou, China
fYear :
2014
fDate :
11-13 July 2014
Firstpage :
1
Lastpage :
6
Abstract :
In this paper, blind equalization of short burst signals is formulated with the twin support vector regressor (TSVR) framework. The proposed algorithm combine the conventional cost function of TSVR with classical error function applied to blind equalization: the Godard´s error function that describes the relationship between the input signals and the desired output signals of a blind equalizer is contained in the penalty terms of TSVR, and the iterative re-weighted least square (IRWLS) algorithm is used for twin support vector regressor to achieve fast convergence. In addition, it utilizes the data-reusing method for small amounts of data samples to reach stable convergence. Simulation experiments for constant modulus signals are done to prove the feasibility and validity of the proposed algorithm.
Keywords :
blind equalisers; iterative methods; least squares approximations; regression analysis; support vector machines; telecommunication computing; Godard error function; IRWLS algorithm; TSVR framework; blind equalization; classical error function; constant modulus signals; conventional cost function; data-reusing method; desired output signals; input signals; iterative re-weighted least square algorithm; short-burst signals; twin support vector regressor; Blind equalizers; Convergence; Cost function; Educational institutions; Support vector machines; Vectors; blind equalization; data-reusing; iterative re-weighted least square solution; short burst signals; twin support vector regressor;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computing, Communication and Networking Technologies (ICCCNT), 2014 International Conference on
Conference_Location :
Hefei
Print_ISBN :
978-1-4799-2695-4
Type :
conf
DOI :
10.1109/ICCCNT.2014.6963089
Filename :
6963089
Link To Document :
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