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Blind linear/nonlinear equalization for heavy noise-corrupted channels  

Han, Soo- Whan (Department of Multimedia Eng., Dong-Eui University)
Park, Sung-Dae (Department of Digital Culture and Contents Eng., Dong-Eui University)
Publication Information
Journal of information and communication convergence engineering / v.7, no.3, 2009 , pp. 383-391 More about this Journal
Abstract
In this paper, blind equalization using a modified Fuzzy C-Means algorithm with Gaussian Weights (MFCM_GW) is attempted to the heavy noise-corrupted channels. The proposed algorithm can deal with both of linear and nonlinear channels, because it searches for the optimal channel output states of a channel instead of estimating the channel parameters in a direct manner. In contrast to the common Euclidean distance in Fuzzy C-Means (FCM), the use of the Bayesian likelihood fitness function and the Gaussian weighted partition matrix is exploited in its search procedure. The selected channel states by MFCM_GW are always close to the optimal set of a channel even the additive white Gaussian noise (AWGN) is heavily corrupted in it. Simulation studies demonstrate that the performance of the proposed method is relatively superior to existing genetic algorithm (GA) and conventional FCM based methods in terms of accuracy and speed.
Keywords
Fuzzy C-Means; Gaussian Weighted Partition Matrix; Bayesian Equalizer; Blind Equalization; Linear/Nonlinear Channel;
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