Abstract
In this study, we present a modified Fuzzy C-Means (MFCM) algorithm for nonlinear blind channel equalization. The proposed MFCM searches the optimal channel output states of a nonlinear channel, based on the Bayesian likelihood fitness function instead of a conventional Euclidean distance measure. In its searching procedure, all of the possible desired channel states are constructed by the combinations of estimated channel output states. The desired state with the maximum Bayesian fitness is selected and placed at the center of a Radial Basis Function (RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with that of a hybrid genetic algorithm (GA augment by simulated annealing (SA), GASA). It is shown that a relatively high accuracy and fast search speed has been achieved.
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References
Biglieri, E., et al.: Adaptive cancellation of nonlinear intersymbol interference for voiceband data transmission. IEEE J. Selected Areas Commun. SAC-2(5), 765–777 (1984)
Proakis, J.G.: Digital Communications, 4th edn. McGraw-Hill, New York (2001)
Serpedin, E., Giannakis, G.B.: Blind channel identification and equalization with modulation-induced cyclostationarity. IEEE Trans. Signal Processing 46, 1930–1944 (1998)
Fang, Y., Chow, W.S., Ng, K.T.: Linear neural network based blind equalization. Signal Processing 76, 37–42 (1999)
Stathaki, T., Scohyers, A.: A constrained optimization approach to the blind estimation of Volterra kernels. In: Proc. IEEE Int. Conf. on ASSP 3, pp. 2373–2376. IEEE, Los Alamitos (1997)
Kaleh, G.K., Vallet, R.: Joint parameter estimation and symbol detection for linear or nonlinear unknown channels. IEEE Trans. Commun. 42, 2406–2413 (1994)
Erdogmus, D., et al.: Nonlinear channel equalization using multilayer perceptrons with information theoretic criterion. In: Proc. Of IEEE workshop Neural Networks and Signal Processing, MA, U.S.A, pp. 443–451. IEEE Computer Society Press, Los Alamitos (2001)
Santamaria, I., et al.: Blind Equalization of Constant Modulus Signals Using Support Vector Machines. IEEE Trans. Signal Processing 52, 1773–1782 (2004)
Lin, H., Yamashita, K.: Hybrid simplex genetic algorithm for blind equalization using RBF networks. Mathematics and Computers in Simulation 59, 293–304 (2002)
Han, S., Pedrycz, W., Han, C.: Nonlinear Channel Blind Equalization Using Hybrid Genetic Algorithm with Simulated Annealing. Mathematical and Computer Modeling 41, 697–709 (2005)
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York (1981)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
Lin, H., Yamashita, K.: Blind equalization using parallel Bayesian decision feedback equalizer. Mathematics and Computers in Simulation 56, 247–257 (2001)
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© 2007 Springer-Verlag Berlin Heidelberg
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Han, S., Park, S., Pedrycz, W. (2007). MFCM for Nonlinear Blind Channel Equalization. In: Melin, P., Castillo, O., RamÃrez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_10
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DOI: https://doi.org/10.1007/978-3-540-72432-2_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72431-5
Online ISBN: 978-3-540-72432-2
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