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http://dx.doi.org/10.6109/jkiice.2007.11.4.826

Training Algorithm of Recurrent Neural Network Using a Sigma Point for Equalization of Channels  

Kwon, Oh-Shin (군산대학교 전자정보공학부)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.11, no.4, 2007 , pp. 826-832 More about this Journal
Abstract
A recurrent neural network has been frequently used in equalizing the channel for fast communication systems. The existing techniques, however, have mainly dealt with time-invariant chamois. The modern environments of communication systems such as mobile ones have the time-varying feature due to fading. In this paper, powerful decision feedback - recurrent neural network is used as channel equalizer for nonlinear and time-varying system, and two kinds of algorithms, such as extended Kalman filter (EKF) and sigma-point Kalman filter (SPKF), are proposed; EKF is for fast convergence and good tracing function, and SPKF for overcoming the problems which can be developed during the process of first linearization for nonlinear system EKF.
Keywords
Sigma-Point Kalman Filter; Extended Kalman Filter; Recurrent Neural Network; Channel Equalization;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 S. Chen, B. Mulgrew, and S. Mclaughlin, 'Adaptive Bayesian equalizer with decision feedback,' IEEE Transactions on Signal Processing, Vol. 41, pp. 2918-2927, September 1993   DOI   ScienceOn
2 S. Y. Cho,W.S. CHoi, 'Improving the Performance of DR/GPS Integrated System for Land Navigation Using Sigma Point Based RHKF Filter,' Journal of Cont. and System Eng., Vol. 12, No.2, pp. 174-185, February 2006   과학기술학회마을   DOI
3 Rudolph van der Merwe & Eric Wan, 'Sigma-Point Kalman Filters for Probabilistic Inferencein Dynamic State-Space Models,' Journal of Oregon Health & Science University, USA, 2004
4 J. Choi, M. B. Bouchard, T. H. Yeap, O. Kwon, 'A Derivative-Free Kalman Filter for Parameter Estimation of Recurrent Neural Networks and Its Applications to Nonlinear Channel Equalization,' Eng. of Intelligent Systems EIS' 2004, Portugal, March, 2004
5 J. Elman, 'Finding structure in time,' Congnitive Science, Vol. 14,pp. 179-211, 1990   DOI   ScienceOn
6 S. Haykin, Adaptive Filter Theory, 4th Ed. Upper Saddle River, NJ: Prentice Hall, 2002
7 S. J. Julier and J. K. Uhlmann, 'A new extension of the Kalman filter to nonlinear systems,' in Proceeding of AeroSence: The 11th International Symposium on Aerospace/Defence Sensing, Simulation and Controls, 1997
8 H.S. Hong,C. G. Park, J.G. .Lee, 'An Extended Kalman Filter Robust to Linearization Error,' Journal of Cont. and System Eng.,Vol. 12, No.2, pp.93-100, February 2006   과학기술학회마을   DOI
9 S. Qureshi, 'Adaptive equalization,' Proceedings of the IEEE, Vol. 73,no.9, pp. 1349-1387, 1985   DOI   ScienceOn
10 S. Haykin,'Neural Networks: a Comprehensive Foundation, 2nd Ed. Upper Saddle River, NJ: Prentice Hall, 1999
11 J. Choi, O. Kwon, 'Equalization of Time-varying Channels using a Recurrent Network Trained with Kalman Filters,' Journal of Cont. and System Eng., Vol. 9, No.11,pp. 917-924, 2003   과학기술학회마을   DOI