Abstract
This paper discusses a blind equalization technique for FIR channel system, that might be minimum phase or not, in digital communication. The proposed techniques consist of two parts. One is to estimate the original channel coefficients based on fourth-order cumulants of the channel output, the other is to employ RBF neural network to model an inverse system for the original channel. Here, the estimated channel is used as a reference system to train the RBF neural network. The proposed RBF equalizer provides fast and easy learning, due to the structural efficiency and excellent recognition-capability of RBF neural network. Throughout the simulation studies, it was found that the proposed blind RBF equalizer performed favorably better than the blind MLP equalizer, while requiring the relatively smaller computation steps in training.
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References
Sato, Y.: A Method of Self-Recovering Equalization for Multilevel Amplitude Modulation Systems. IEEE Trans. Commun. COM-23, 679–682 (1975)
Benveniste, A., Goursat, Ruget, G.: Robust Identification of a Nonminimum Phase System: Blind Adjustment of a Linear Equalizer in Data Communications. IEEE Trans. Automat. Contr. AC-25, 385–398 (1980)
Mendel, J.M.: Tutorial on Higher-Order Statistics (Spectra) in Signal Processing and System Theory: Theoretical Results and Some Applications. In: Proceedings, IEEE, March 1991, pp. 278–305 (1991)
Ueng, F.B., Su, Y.T.: Adaptive Blind Equalization Using Second and Higher Order Statistics. IEEE J. Select. Areas Commun. 13, 132–140 (1995)
Mo, S., Shafai, B.: Blind Equalization Using Higher Order Cumulants and Neural Network. IEEE Trans. Signal Processing 42, 3209–3217 (1994)
Chen, S., Gibson, G.J., Cowan, C.F.N., Grant, P.M.: Adaptive Equalization of Finite Non-Linear Channels Using Multilayer Perceptrons. Signal Processing 20, 107–119 (1990)
Gibson, G.J., Siu, S., Cowan, C.F.N.: Application of Multilayer Perceptrons as Adaptive Channel Equalizers. In: ICASSP, Glasgow, Scotland, pp. 1183–1186 (1989)
Chen, S., Mulgrew, B., Grant, P.M.: A Clustering Technique for Digital Communications Channel Equalization Using Radial Basis Function Networks. IEEE Trans. Neural Networks 4, 570–579 (1993)
Lee, J., Beach, C., Tepedelenlioglu, N.: Practical Radial Basis Function Equalizer. IEEE Trans. on Neural Networks, 450–455 (March 1999)
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© 2003 Springer-Verlag Berlin Heidelberg
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Lee, JS., Kim, JH., Jee, DK., Hwang, JJ., Lee, JH. (2003). Blind Equalization Using RBF and HOS. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_59
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DOI: https://doi.org/10.1007/978-3-540-45080-1_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40550-4
Online ISBN: 978-3-540-45080-1
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