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Probabilistic Tangent Subspace Method for M-QAM Signal Equalization in Time-Varying Multipath Channels

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Advances in Intelligent Computing (ICIC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3645))

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Abstract

A new machine learning method called probabilistic tangent subspace is introduced to improve the performance of the equalization for the M-QAM modulation signals in wireless communication systems. Due to the mobility of communicator, wireless communication channels are time variant. The uncertainties in the time-varying channel’s coefficients cause the amplitude distortion as well as the phase distortion of the M-QAM modulation signals. On the other hand, the Probabilistic Tangent Subspace method is designed to encode the pattern variations. Therefore, we are motivated to adopt this method to develop a classifier as an equalizer for time-varying channels. Simulation results show that this equalizer performs better than those based on nearest neighbor method and support vector machine method for Rayleigh fading channels.

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References

  1. Lee, J.G., Wang, J.D., Zhang, C.S., Bian, Z.Q.: Probabilistic Tangent Subspace: a Unified View. In: Proc. 21st Intl. Confs. on Machine Learing (ICML 2004), Banff, Alberta, Canada (2004)

    Google Scholar 

  2. Benelli, G., Gastellini, G., Re, E.D., Fantacci, R., Pierucci, L., Pagliani, L.: Design of a Digital MLSE Receiver for Mobile Radio Communications. In: IEEE Proc. Globecom, pp. 1469–1473 (1991)

    Google Scholar 

  3. Veciana, G.D., Zakhor, A.: Neural Netobased Continous Phase Modulation Receivers. IEEE Trans. Communication 40, 1392–1408 (1992)

    Google Scholar 

  4. Kechriotix, G., Zervas, E., Manolakos, E.S.: Using Recurrent Neural Network for Adaptive Communication Channel Equalization. IEEE Trans. Neural Networks 5, 267–278 (1994)

    Article  Google Scholar 

  5. Parisi, R., Di Claudio, E.D., Orlandi, G., Rao, B.D.: Fast Adaptive Digital Equalization by Recurrent Neural Network. IEEE Trans. Signal Processing 45, 2731–2739 (1997)

    Article  Google Scholar 

  6. Savazzi, P., Favalli, L., Costamagna, E., Mecocci, A.: A Suboptimal Approach to Channel Equalization Based on the Nearest Neighbor Rule. IEEE Journal on Selected Areas in Communications 16, 1640–1648 (1998)

    Article  Google Scholar 

  7. Sebald, D.J., Bucklew, J.A.: Support Vector Machine Techniques for Nonlinear Equalization. IEEE Trans. Signal Processing 48, 3217–3226 (2000)

    Article  Google Scholar 

  8. Liang, Q.L., Mendel, J.M.: Equalization of Nonlinear Time-varying Channels Using Type-2 Fuzzy Adaptive Filters. IEEE Trans. Fuzzy Systems 8, 551–563 (2000)

    Article  Google Scholar 

  9. Proakis, J.G.: Digital Comunications, 3rd edn. McGraw-Hill, New York (1995)

    Google Scholar 

  10. Hastie, T., Simard, P., Saeckinger, E.: Learning prototype models for Tangent Distance. Advances in Neural Information Processing Systems 7 (NIPS 7)

    Google Scholar 

  11. Simard, P., LeCun, Y., Denker, J., Victorri, B.: Transformation Invariance in Pattern Recognition - Tangent Distance and Tangent Propagation. Inernational Journal of Imaging System and Technology 11, 181–194 (2001)

    Article  Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Yang, J., Xu, Y., Zou, H. (2005). Probabilistic Tangent Subspace Method for M-QAM Signal Equalization in Time-Varying Multipath Channels. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538356_98

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  • DOI: https://doi.org/10.1007/11538356_98

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28227-3

  • Online ISBN: 978-3-540-31907-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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