Table of Contents Author Guidelines Submit a Manuscript
Journal of Electrical and Computer Engineering
Volume 2010, Article ID 191808, 8 pages
Research Article

Relevance Vector Machines for Enhanced BER Probability in DMT-Based Systems

1School of Electrical Engineering, Princess Sumaya University for Technology, Amman 11941, Jordan
2Electrical and Computer Engineering Department, University of Patras, Rio 26500, Greece

Received 15 December 2009; Revised 15 March 2010; Accepted 27 April 2010

Academic Editor: Cyril Leung

Copyright © 2010 Ashraf A. Tahat and Nikolaos P. Galatsanos. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Broadband Forum, “Next generation broadband access white paper,” Marketing Report 185, August 2009. View at Google Scholar
  2. E. Dahlman, S. Parkvall, and P. Beming, 3G Evolution: HSPA and LTE for Mobile Broadband, Academic Press, Oxford, UK, 2007.
  3. S. B. Weinstein and P. M. Ebert, “Data transmission by frequency- division multiplexing using the discrete Fourier transform,” IEEE Transactions on Communications, vol. 19, no. 5, part 1, pp. 628–634, 1971. View at Google Scholar
  4. The European Telecommunications Standards Institute, “Digital video broadcasting (DVB); framing structure, channel coding and modulation for terrestrial digital terrestrial television,” Tech. Rep. EN 300 744 V1.4.1, ETSI, 2001. View at Google Scholar
  5. The Institute of Electrical and Electronics Engineers, “Wireless LAN medium access control (MAC) and physical layer (PHY) specification,” IEEE standard 802.11a, 1999.
  6. The European Telecommunications Institute, “Broadband radio access networks (BRAN); high performance radio local area networks (HIPERLAN) type 2; system overview,” Tech. Rep. 101 683 V1.1.1, ETSI, 2000. View at Google Scholar
  7. S. Glisic, Advanced Wireless Communications: 4G Technologies, John Wiley & Sons, Chechester, England, 2004.
  8. P. J. Kyees, R. C. McConnell, and K. Sistanizadeh, “ADSL. A new twisted-pair access to the information highway,” IEEE Communications Magazine, vol. 33, no. 4, pp. 52–59, 1995. View at Publisher · View at Google Scholar
  9. H. Dedieu, Fundamentals of DSL Technology, Auerbach, Boca Raton, Fla, USA, 2006.
  10. S. Baig and N. D. Gohar, “A discrete multitone transceiver at the heart of the PHY layer of an in-home power line communication local area network,” IEEE Communications Magazine, vol. 41, no. 4, pp. 48–53, 2003. View at Publisher · View at Google Scholar
  11. Z. Wang and G. B. Giannakis, “Wireless multicarrier communications: where Fourier meets Shannon,” IEEE Signal Processing Magazine, vol. 17, no. 3, pp. 29–48, 2000. View at Google Scholar
  12. F. J. Simois and J. I. Acha, “Linear IIR precoding for non-redundant transmission over minimum-phase channels in wireline multitone systems,” IEEE Transactions on Communications, vol. 57, no. 5, pp. 1496–1504, 2009. View at Publisher · View at Google Scholar
  13. L. de Clercq, M. Peeters, S. Schelstraete, and T. Pollet, “Mitigation of radio interference in xDSL transmission,” IEEE Communications Magazine, vol. 38, no. 3, pp. 168–173, 2000. View at Google Scholar
  14. C.-C. Li and Y.-P. Lin, “Receiver window designs for radio frequency interference suppression in DMT systems,” IET Signal Processing, vol. 3, no. 1, pp. 33–39, 2009. View at Publisher · View at Google Scholar
  15. M. E. Tipping, “Sparse Bayesian learning and the relevance vector machine,” Journal of Machine Learning Research, vol. 1, no. 3, pp. 211–244, 2001. View at Publisher · View at Google Scholar
  16. J. van de Beek, O. Edfors, M. Sandell, S. K. Wilson, and P. O. Borjesson, “On channel estimation in OFDM systems,” in Proceedings of the IEEE 45th Vehicular Technology Conference, pp. 815–819, July 1995.
  17. T. Pollet, M. Peeters, M. Moonen, and L. Vandendorpe, “Equalization for DMT-based broadband modems,” IEEE Communications Magazine, vol. 38, no. 5, pp. 106–113, 2000. View at Google Scholar
  18. G. Golub and C. Van Loan, Matrix Computations, The John Hopkins University Press, Baltimore, Md, USA, 3rd edition, 1996.
  19. A. A. Tahat and N. P. Galatsanos, “Relevance vector machines for DMT based systems,” in Proceedings of the 20th International Conference on Electronics Communications and Computers (CONIELECOMP '10), pp. 31–36, February 2010. View at Publisher · View at Google Scholar
  20. C. Bishop, Pattern Recognition and Machine Learning, Springer, Berlin, Germany, 2006.
  21. J. Berger, Statistical Decision Theory and Bayesian Analysis, Springer, New York, NY, USA, 1985.
  22. A. Faul and M. Tipping, “Analysis of sparse Bayesian learning,” in Proceedings of the Neural Information Processing Systems: Natural and Synthetic (NIPS '01), pp. 383–389, December 2001.
  23. J. Proakis, Digital Communications, McGraw-Hill, New York, NY, USA, 3rd edition, 1995.
  24. A. Papoulis, Signal Analysis, McGraw Hill, New York, NY, USA, 1977.
  25. D. J. Love, R. W. Heath Jr., V. K. N. Lau, D. Gesbert, B. D. Rao, and M. Andrews, “An overview of limited feedback in wireless communication systems,” IEEE Journal on Selected Areas in Communications, vol. 26, no. 8, pp. 1341–1365, 2008. View at Publisher · View at Google Scholar