Radial Basis Function Neural Network Receiver for Wireless Channels

Pedro Henrique Gouvêa Coelho, Fabiana Mendes Cesario

Abstract

Artificial Neural Networks have been widely used in several decision devices systems and typical signal processing applications. This paper proposes an equalizer for wireless channels using radial basis function neural networks. An equalizer is a device used in communication systems for compensating the non-ideal characteristics of the channel. The main motivation for such an application is their capability to form complex decision regions which are of paramount importance for estimating the transmitted symbols efficiently. The proposed equalizer is trained by means of an extended Kalman filter guaranteeing a fast training for the radio basis function neural network. Simulation results are presented comparing the proposed equalizer with traditional ones indicating the efficiency of the scheme.

References

  1. Proakis, J. G., 2001. Digital Communications. Fourth Edition, McGraw-Hill.
  2. Mulgrew, B., 1996. Applying Radial Basis Functions. IEEE Signal Processing Magazine, vol. 13, pp. 5065.
  3. Burse K., Yadav R. N., and Shrivastava S. C., 2010. Channel Equalization Using Neural Networks: A Review. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 40, No. 3.
  4. Assaf, R., El Assad, S., Harkouss, Y., 2005. Adaptive equalization for digital channels RBF neural network. In The European Conference on Wireless Technology, pp. 347-350.
  5. Lee J., Beach C., and Tepedelenlioglu N., 1999. A practical radial basis function equalizer. IEEE Trans. Neural Netw., vol. 10, no. 2, pp. 450-455.
  6. Gan Q., Saratchandran P., Sundararajan N., and Subramaniam K. R., 1999.A complex valued RBF network for equalization of fast time varying channels. IEEE Trans. Neural Netw., vol. 10, no. 4, pp. 958- 960.
  7. Kumar C. P., Saratchandran P., and Sundararajan N., 2000. Nonlinear channel equalization using minimal radial basis function neural networks. Proc. Inst. Electr. Eng. Vis., Image, Signal Process., vol. 147, no. 5, pp. 428-435.
  8. Xie N. and Leung H., 2005. Blind equalization using a predictive radial basis function neural network. IEEE Trans. Neural Netw., vol. 16, no. 3,pp. 709-720.
  9. Tan Y., Wang J., and. Zurada J. M, 2001. Nonlinear blind source separation using a radial basis function network. IEEE Trans. Neural Netw., vol. 12,no. 1, pp. 124-134.
  10. Uncini A. and Piazza F., 2003. Blind signal processing by complex domain adaptive spline neural networks. IEEE Trans. Neural Netw., vol. 14,no. 2, pp. 399-412.
  11. Oyang, Y.J., Hwang, S.C., Ou, Y.Y., Chen, C.Y., Chen, Z.W., 2005. Data classification with radial basis function networks based on a novel kernel density estimation algorithm. IEEE Trans. Neural Netw., 16, 225-236.
  12. Fu, X., Wang, L., 2003. Data dimensionality reduction with application to simplifying rbf network structure and improving classification performance. IEEE Trans. Syst. Man Cybern. Part B, 33, 399-409..
  13. Devaraj, D., Yegnanarayana, B., Ramar, K., 2002. Radial basis function networks for fast contingency ranking. Electric. Power Energy Syst., 24, 387-395.
  14. Du, J.X., Zhai, C.M., 2008. A hybrid learning algorithm combined with generalized rls approach for radial basis function neural networks. Appl. Math. Comput., 208, 908-915.
  15. Han, M., Xi, J., 2004. Efficient clustering of radial basis perceptron neural network for pattern recognition. Pattern Recognit, 37, 2059-2067.
  16. Liu, Y., Zheng, Q.; Shi, Z., Chen, J., 2004. Training radial basis function networks with particle swarms. Lect. Note. Comput. Sci., 3173, 317-322.
  17. Simon, D., 2002. Training radial basis neural networks with the extended Kalman filter. Neurocomputing, 48, 455-475.
  18. Karayiannis, N.B., 1999. Reformulated radial basis neural networks trained by gradient descent. IEEE Trans. Neural Netw., 3, 2230-2235.
  19. Barreto, A.M.S., Barbosa, H.J.C., Ebecken, N.F.F., 2002. Growing Compact RBF Networks Using a Genetic Algorithm. In Proceedings of the 7th Brazilian Symposium on Neural Networks, Recife, Brazil, pp. 61-66.
  20. De Castro, L.N., Von Zuben, F.J., 2001. An Immunological Approach to Initialize Centers of Radial Basis Function Neural Networks. In Proceedings of Brazilian Conference on Neural Networks, Rio de Janeiro, Brazil, pp. 79-84.
  21. Yu, B., He, X., 2006.Training Radial Basis Function Networks with Differential Evolution. In Proceedings of IEEE International Conference on Granular Computing, Atlanta, GA, USA, 369-372.
  22. Karaboga, D., Akay, B., 2007. Artificial Bee Colony (ABC) Algorithm on Training Artificial Neural Networks. In Proceedings of 15th IEEE Signal Processing and Communications Applications, Eskisehir, Turkey.
  23. Qureshi, S.,1985. Adaptive equalization. Proceedings of The IEEE - PIEEE, vol.73, no. 9, pp. 1349-1387.
  24. Molisch, A. S., 2011. Wireless Communications.Second Edition. John Wiley & Sons.
Download


Paper Citation


in Harvard Style

Henrique Gouvêa Coelho P. and Mendes Cesario F. (2015). Radial Basis Function Neural Network Receiver for Wireless Channels . In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-096-3, pages 658-663. DOI: 10.5220/0005470106580663


in Bibtex Style

@conference{iceis15,
author={Pedro Henrique Gouvêa Coelho and Fabiana Mendes Cesario},
title={Radial Basis Function Neural Network Receiver for Wireless Channels},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2015},
pages={658-663},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005470106580663},
isbn={978-989-758-096-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Radial Basis Function Neural Network Receiver for Wireless Channels
SN - 978-989-758-096-3
AU - Henrique Gouvêa Coelho P.
AU - Mendes Cesario F.
PY - 2015
SP - 658
EP - 663
DO - 10.5220/0005470106580663