DocumentCode :
1613533
Title :
A Practical Wavelet Domain LMK Algorithm for Predicting Multimedia Traffic
Author :
Zhao, Hong
Author_Institution :
Embry Riddle Aeronaut. Univ., Prescott, AZ
fYear :
2008
Firstpage :
515
Lastpage :
519
Abstract :
Wavelet transform is an emerging technique that has a significant advantage in analyzing time domain signals. When combined with LMS (Least Mean Square), wavelet based predictor can achieve better performance than time domain predictor for VBR (Variable Bit Rate) video traffic. However the computational complexity in predicting each wavelet coefficient is high. In this paper, first, the Least Mean Kurtosis (LMK) which uses the negated kurtosis of the error signal as the cost function, is proposed to estimate wavelet coefficients; then by analyzing the wavelet coefficients of two consecutive data sets, a fast WLMK is proposed to reduce the computational complexity. Simulation results show that the fast WLMK not only incurs smaller prediction error but also reduces the computational complexity greatly.
Keywords :
adaptive filters; computational complexity; higher order statistics; least mean squares methods; multimedia communication; prediction theory; telecommunication traffic; time-domain analysis; video communication; wavelet transforms; adaptive filter; computational complexity; cost function; higher order statistics; least mean Kurtosis algorithm; least mean square method; multimedia traffic prediction; time domain signal analysis; variable bit rate video traffic; wavelet transform; Bit rate; Computational complexity; Least squares approximation; Prediction algorithms; Signal analysis; Time domain analysis; Wavelet analysis; Wavelet coefficients; Wavelet domain; Wavelet transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications, 2008. ICC '08. IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2075-9
Electronic_ISBN :
978-1-4244-2075-9
Type :
conf
DOI :
10.1109/ICC.2008.102
Filename :
4533138
Link To Document :
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