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http://dx.doi.org/10.5762/KAIS.2013.14.3.1416

Euclidean Distance of Biased Error Probability for Communication in Non-Gaussian Noise  

Kim, Namyong (School of Electronic, Info. & Comm. Engineering, kangwon National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.14, no.3, 2013 , pp. 1416-1421 More about this Journal
Abstract
In this paper, the Euclidean distance between the probability density functions (PDFs) for biased errors and a Dirac-delta function located at zero on the error axis is proposed as a new performance criterion for adaptive systems in non-Gaussian noise environments. Also, based on the proposed performance criterion, a supervised adaptive algorithm is derived and applied to adaptive equalization in the shallow-water communication channel distorted by severe multipath fading, impulsive and DC-bias noise. The simulation results compared with the performance of the existing MEDE algorithm show that the proposed algorithm yields over 5 dB of MSE enhancement and the capability of relocating the mean of the error PDF to zero on the error axis.
Keywords
Euclidean distance; Biased error probability; Dirac-delta; non-Gaussian noise; Equalizer;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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