Advanced Search
Volume 28 Issue 4
Aug.  2010
Turn off MathJax
Article Contents
Xiang Zheng, Zhang Tai-yi, Sun Jian-cheng . Prediction Algorithm for Fast Fading Channels Based on Recurrent Least Squares Support Vector Machines[J]. Journal of Electronics & Information Technology, 2006, 28(4): 671-674.
Citation: Xiang Zheng, Zhang Tai-yi, Sun Jian-cheng . Prediction Algorithm for Fast Fading Channels Based on Recurrent Least Squares Support Vector Machines[J]. Journal of Electronics & Information Technology, 2006, 28(4): 671-674.

Prediction Algorithm for Fast Fading Channels Based on Recurrent Least Squares Support Vector Machines

  • Received Date: 2005-06-24
  • Rev Recd Date: 2006-01-11
  • Publish Date: 2006-04-19
  • An new method for fast fading channel prediction using Recurrent Least Squares Support Vector Machines (RLS-SVM) combined with reconstructed embedding phase space is investigated. This algorithm is based on the chaotic behavior of the mobile multipath fading channel.The phase space of these mobile multipath fading channel coefficients are reconstructed by the theory of time delays. Based on the stability and the fractal of the chaotic attractor, the fast fading channel coefficients are predicted in their phase space based on the RLS-SVM.The proposed algorithm is a better candidate for long range prediction of the fading channel in the noise context . The experiment is carried out by utilizing fading channel data which spanes 63.829 ms. The simulation results show that the better prediction performance is acquired than the AR method when the signal to noise ratio is 15dB.
  • loading
  • 胡刚, 朱世华, 谢波. 基于混沌、分形理论的多径衰落分析[J].电子学报,2003,31(7): 1039-1042.[2]Tannous C, Davies R, Angus A. Strange attractors in multipath propagation [J].IEEE Trans. on Comm.1991, 39(5):629-631[3]Eyceoz T, Duel-Hallen A, Hallen H. Prediction of fast fading parameters by resolving the interference pattern. Proceedings of the 31st ASILOMAR Conference on Signals, Systems, and Computers[C]. Pacific Grove, CA, 1997: 167-171.[4]Ekman T, Kubin G.Nonlinear prediction of mobile radio channels: Measurements and MARS model designs, In Proc. Int. Conf. Acoust. Speech Sig. Process[C]. Phoenix, AZ, March 1999: 2667-2670.[5]Gao X M, Tanskanen J M A, Ovaska S J. Comparison of linear and neural network-based power prediction schemes for mobile DS/CDMA systems.VTC96[C]. Atlanta: IEEE press,1996: 61-65.[6]Vapnik V. The Nature of Statistical Learning Theory[M]. New York: Springer, 1995: 91-108.[7]Wang L P(Ed.). Support Vector Machines: Theory and Application[M]. New York, Berlin, Heidelberg: Springer, 2005: 51-123.[8]Vapnik V. The Nature of Statistical Learning Theory[M]. Translated by Zhang Xuegong. Beijing: Tsinghua University Press, 2000: 91-108.[9]Suykens J A K, Vandewalle J. Least squares support vector machines[J].Neurel Processing Letters.1999, 9(3):293-300[10]Suykens J A K, Vandewalle J. Recurrent least squares support sector machines[J].IEEE Trans. on Circuits and System-I: Fundamental Theory and Applications.2000, 47(7):1109-1114[11]Takens F . Detecting strange attractors in fluid turbulence. In D. Rand and L.S.Young, editors, Dynamical systems and Turbulence [M]. Berlin: Springer-Verlag, 1981: 366-381.[12]Jakes W C. Microwave Mobile Communications[M]. Piscataway, USA: IEEE Press, 1974, chapter1: 13-77.[13]Cao L. Practical method for determining the minimum embedding dimension of a scalar time series[J].Physcai D.1997, 110(7):43-50[14]Grassberger P, Procaccia I. Characterization of strange attractors[J].Physical Review Letters.1983, 50(5):346-349[15]Wolf A, Swift J B, Swinney H L. Determining Lyapunov exponents from a time series[J].Physica D.1985, 16(2):285-317
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (2273) PDF downloads(984) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return