Intelligent System for Channel Prediction in the MIMO-OFDM Wireless Communications Using a Multidimensional Recurrent LS-SVM

  • Jerzy Martyna
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)


In order to resolve channel prediction in the multiple-input multiple-output orthogonal frequency division multiple (MIMO-OFDM) system used in wireless communication, a novel intelligent system based on least squares support vector machines (LS-SVMs) is proposed in this paper. To manipulate the iterative problem, the recurrent multidimensional version LS-SVM has been used. The proposed algorithm used in this system allows us to implement nonlinear decision regions in the channel prediction in MIMO-OFDM systems, and adaptively convergent to minimum mean squared error solutions. It is shown by simulation that the proposed method is able to provide accurate results in channel prediction in these systems. Moreover, this method can be also used in many signal degradations caused by multipath propagation, shadowing from obstacles, etc.


wireless channel estimation multidimensional recurrent least-squares support vector machine multiple-input multiple-output (MIMO) channel model OFDM system 


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  1. 1.
    3GPP: Tr25.869 tx diversity solutions for multiple antennas. v1.2.0 (2003)Google Scholar
  2. 2.
    Abraham, A., Corchado, E., Corchado, J.M.: Hybrid learning machines. Neurocomputing 72, 2729–2730 (2009)CrossRefGoogle Scholar
  3. 3.
    Biguesh, M., Gershman, A.: Training-based mimo channel estimation: A study of estimator tradeoffs and optimal training signals. IEEE Trans. on Signal Processing 54(3), 884–893 (2006)CrossRefGoogle Scholar
  4. 4.
    Cortes, C., Vapnik, V.: Support vector networks. Support vector networks. Machine Learning 20, 273–297 (1995)zbMATHGoogle Scholar
  5. 5.
    Corchado, E., Suasel, V., Sedano, J., Hassanien, A.E., Calvo-Rolle, J.L., Slezak, D.: Soft computing models in industrial and environmental applications. In: 6th Int. Conf. SOCO 2011 (2011)Google Scholar
  6. 6.
    Garcia, M.F.G., Rojo-Álvarez, J., Alonso-Atienzo, F., Martinez-Ramón, M.: Support vector machines for robust channel estimation in ofdm. IEEE Signal Processing Letters 13(7), 397–400 (2006)CrossRefGoogle Scholar
  7. 7.
    Ghogho, M., Swami, A.: Training design for multipath channel and frequency-offset estimation in mimo systems. IEEE Trans. on Signal Processing 54(6), 3957–3965 (2006)CrossRefGoogle Scholar
  8. 8.
    Hao, X., Chizhik, D., Huang, H., Valenzuela, R.: A generalized space-time multiple input multiple output (mimo) channel model. IEEE Trans. on Wireless Comm. 3, 966–975 (2004)CrossRefGoogle Scholar
  9. 9.
    Jindal, N., Vishwanath, S., Goldsmith, A.: On the duality of gaussian multiple-access and broadcast channels. IEEE Trans. on Inf. Theory 50(5), 768–783 (2004)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Shahtalebi, K., Bakhshi, G.R., Rad, H.: Full mimo channel estimation using a simple adaptive partial feedback method. (2007)Google Scholar
  11. 11.
    Ma, X., Yang, L., Giannakis, G.: Optimal training for mimo frequency-selective fading channels. IEEE Trans. on Wireless Comm. 4(2), 453–466 (2005)CrossRefGoogle Scholar
  12. 12.
    van Nee, R., Prasad, R.: OFDM for Wireless Multimedia Communications. Artech House Publishers, Boston (2000)Google Scholar
  13. 13.
    Rahman, S., Saito, M., Okada, M., Yamamoto, H.: An mc-cdma signal equalization and detection scheme based on support vector machines. In: Proc. 1st Int. Symp. Wireless Communication Systems, pp. 11–15 (2004)Google Scholar
  14. 14.
    Sánchez-Fernández, M., de Prado-Cumlido, M., Arenas-Garcia, J., Perez-Cruz, F.: Svm multiregression for nonlinear channel estimation in multiple-input multiple-output systems. IEEE Trans. on Signal Proc. 52(8), 2298–2307 (2004)CrossRefGoogle Scholar
  15. 15.
    Suykens, J., Gestel, T.V., Brabantter, J.D., Moor, B.D., Vandewalle, J.: Least Squares Support Vector Machines. World Sci. Pub. Co., Singapore (2002)zbMATHCrossRefGoogle Scholar
  16. 16.
    Suykens, J.A.K., Lukas, L., Vandewalle, J.: Sparse approximation using least squares support vector machines. In: Proc. of the IEEE Int. Symp. on Circuits and Systems (ISCAS 2000), vol. 2, pp. 757–760 (2000)Google Scholar
  17. 17.
    Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifier. Neural Processing Letters 9(3), 293–300 (1999)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Suykens, J.A.K., Vandewalle, J.: Recurrent least squares support vector machines. IEEE Trans. Circuits Systems - I: Fundamental Theory and Applications 47(7), 1109–1114 (2000)CrossRefGoogle Scholar
  19. 19.
    Vapnik, V.: Statistical Learning Theory. John Wiley and Sons, New York (1998)zbMATHGoogle Scholar
  20. 20.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Berlin (1995)zbMATHCrossRefGoogle Scholar
  21. 21.
    Zhang, J., He, Z., Wang, X., Huang, Y.: Tsk fuzzy approach to channel estimation for mimo-ofdm systems. Signal Processing Letters 14(6), 381–384 (2007)CrossRefGoogle Scholar
  22. 22.
    Zheng, Y., Xiao, C.: Improved models for the generation of multiple uncorrelated rayleigh fading waveforms. IEEE Trans. Commun. Letters 6(6), 256–258 (2002)CrossRefGoogle Scholar
  23. 23.
    Zhou, X., Wang, X.: Channel estimation for ofdm systems using adaptive radial basis function networks. IEEE Trans. on Veh. Tech. 52(1), 48–59 (2003)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jerzy Martyna
    • 1
  1. 1.Institute of Computer Science, Faculty of Mathematics and Computer ScienceJagiellonian UniversityCracowPoland

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