The Least Squares SVM Approach for a Non-linear Channel Prediction in the MIMO System

1. Pedersen, K.I., Andersen, J.B., Kermoal, J.P., Mogensen, P.: A Stochastic Multiple-Input Multiple-Output Radio Channel Model for Evaluation of Space-Time Coding Algorithms. In: IEEE Vehicular Technology Conference, vol. 2, pp. 893–897 (2000)

   Google Scholar 

2. Rappaport, T.S.: Wireless Communications: Principles and Practice, 2nd edn. Prentice-Hall Inc., Englewood Cliffs (2002)

   MATH  Google Scholar 

3. 3GPP TR25.869 Tx Diversity Solutions for Multiple Antennas, v1.2.0 (August 2003)

   Google Scholar 

4. van Nee, R., Prasad, R.: OFDM for Wireless Multimedia Communications. Artech House Publishers, Boston (2000)

   Google Scholar 

5. Jindal, N., Vishwanath, S., Goldsmith, A.: On the Duality of Gaussian Multiple-Access and Broadcast Channels. IEEE Trans. on Information Theory 50(5), 768–783 (2004)

   Article  MathSciNet  MATH  Google Scholar 

6. Hao, X., Chizhik, D., Huang, H., Valenzuela, R.: A Generalized Space-Time Multiple-Input Multiple Output (MIMO) Channel Model. IEEE Trans. on Wireless Communications 3(3), 966–975 (2004)

   Article  Google Scholar 

7. Gesbert, D., Shafi, M., Shin, D.-S., Smith, P.J., Naguib, A.: From Theory to Practice: An Overview of MIMO Space-Time Coded Wireless Systems. IEEE Journal on Selected Areas in Communications 21(3), 281–302 (2003)

   Article  Google Scholar 

8. Shahtalebi, K., Bakhshi, G.R., Rad, H.S.: Full MIMO Channel Estimation Using a Simple Adaptive Partial Feedback Method (2007)

   Google Scholar 

9. 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)

   Article  Google Scholar 

10. Biguesh, M., Gershman, A.B.: Training-Based MIMO Channel Estimation: A Study of Estimator Tradeoffs and Optimal Training Signals. IEEE Trans. on Signal Processing 54(3), 884–893 (2006)

    Article  Google Scholar 

11. Cortes, C., Vapnik, V.: Support Vector Networks. Machine Learning 20, 273–297 (1995)

    MATH  Google Scholar 

12. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)

    MATH  Google Scholar 

13. Schölkopf, B., Burges, C., Smola, A.: Advances in Kernel Methods Support Vector Learning. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

14. Vapnik, V.: Statistical Learning Theory. John Wiley and Sons, New York (1998)

    MATH  Google Scholar 

15. 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 

16. Sánchez-Fernández, M.P., 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 Processing 52(8), 2298–2307 (2004)

    Article  MathSciNet  Google Scholar 

17. Fernández-Getino Garcia, M.J., Rojo-Álvarez, J.L., Alonso-Atienzo, F., Martinez-Ramón, M.: Support Vector Machines for Robust Channel Estimation in OFDM. IEEE Signal Processing Letters 13(7), 397–400 (2006)

    Article  Google Scholar 

18. Suykens, J.A.K., Van Gestel, T., De Brabantter, J., De Moor, B., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific Pub. Co., Singapore (2002)

    Book  MATH  Google Scholar 

19. Rappaport, T.S.: Wireless Communications. Cambridge University Press, New York (2005)

    Google Scholar 

20. Zheng, Y., Xiao, C.: Improved Models for the Generation of Multiple Uncorrelated Rayleigh Fading Waveforms. IEEE Trans. Commun. Letters 6(6), 256–258 (2002)

    Article  Google Scholar 

21. Suykens, J.A.K., Vandewalle, J.: Recurrent Least Squares Vector Machines. IEEE Trans. Circuits Systems and Systems I 47(7), 11109–11114 (2000)

    Google Scholar 

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