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http://dx.doi.org/10.7472/jksii.2018.19.3.49

Performance Enhancement of Algorithms based on Error Distributions under Impulsive Noise  

Kim, Namyong (Division of Electronic, Information Comm. Eng. Kangwon National Univ.)
Lee, Gyoo-yeong (Division of Electronic, Information Comm. Eng. Kangwon National Univ.)
Publication Information
Journal of Internet Computing and Services / v.19, no.3, 2018 , pp. 49-56 More about this Journal
Abstract
Euclidean distance (ED) between error distribution and Dirac delta function has been used as an efficient performance criterion in impulsive noise environmentsdue to the outlier-cutting effect of Gaussian kernel for error signal. The gradient of ED for its minimization has two components; $A_k$ for kernel function of error pairs and the other $B_k$ for kernel function of errors. In this paper, it is analyzed that the first component is to govern gathering close together error samples, and the other one $B_k$ is to conduct error-sample concentration on zero. Based upon this analysis, it is proposed to normalize $A_k$ and $B_k$ with power of inputs which are modified by kernelled error pairs or errors for the purpose of reinforcing their roles of narrowing error-gap and drawing error samples to zero. Through comparison of fluctuation of steady state MSE and value of minimum MSE in the results of simulation of multipath equalization under impulsive noise, their roles and efficiency of the proposed normalization method are verified.
Keywords
Components; Error distribution; Delta function; Euclidean distance; Impulsive noise;
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Times Cited By KSCI : 2  (Citation Analysis)
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