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Detection of Markov Chains with Unknown Parameters

  • Bernard C. Levy
Chapter

Keywords

Markov Chain Parameter Vector Markov Chain Model Survivor Path Channel Impulse Response 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    T. Kailath, “Correlation detection of signals perturbed by a random channel,” IRE Trans. on Informat. Theory, vol. 6, pp. 361–366, June 1960.CrossRefMathSciNetGoogle Scholar
  2. 2.
    R. A. Iltis, “A Bayesian maximum-likelihood sequence estimation algorithm for a priori unknown channels and symbol timing,” IEEE J. Selected Areas in Commun., vol. 10, pp. 579–588, Apr. 1992.CrossRefGoogle Scholar
  3. 3.
    N. Seshadri, “Joint data and channel estimation using blind trellis search techniques,” IEEE Trans. Commun., vol. 42, pp. 1000–1011, Feb. Mar. Apr. 1994.CrossRefGoogle Scholar
  4. 4.
    R. Raheli, A. Polydoros, and C. Tzou, “Per-survivor-processing: A general approach to MLSE in uncertain environments,” IEEE Trans. Commun., vol. 43, pp. 354–364, Feb. Apr. 1995.CrossRefGoogle Scholar
  5. 5.
    K. M. Chugg and A. Polydoros, “MLSE for an unknown channel – Part I: Optimality considerations,” IEEE Trans. Commun., vol. 44, pp. 836–846, July 1996.CrossRefMATHGoogle Scholar
  6. 6.
    K. M. Chugg, “Blind acquisition characteristics of PSP-based sequence detectors,” IEEE J. Selected Areas in Commun., vol. 16, pp. 1518–1529, Oct. 1998.CrossRefGoogle Scholar
  7. 7.
    L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proc. IEEE, vol. 77, pp. 257–286, Feb. 1989.Google Scholar
  8. 8.
    Y. Ephraim and N. Merhav, “Hidden Markov processes,” IEEE Trans. Informat. Theory, vol. 48, pp. 1518–1569, June 2002.CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    L. Rabiner and B.-H. Juang, Fundamentals of Speech Recognition. Englewood Cliffs, NJ: Prentice Hall, 1993.Google Scholar
  10. 10.
    A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. Royal Stat. Society, Series B, vol. 39, no. 1, pp. 1–38, 1977.MATHMathSciNetGoogle Scholar
  11. 11.
    H. Nguyen, The Expectation-Maximization Viterbi algorithm for blind channel identification and equalization. PhD thesis, Dept. of Electrical and Computer Engineering, Univ. California, Davis, Aug. 2003.Google Scholar
  12. 12.
    H. Nguyen and B. C. Levy, “Blind and semi-blind equalization of CPM signals with the EMV algorithm,” IEEE Trans. Signal Proc., vol. 51, pp. 2650–2664, Oct. 2003.Google Scholar
  13. 13.
    H. Nguyen and B. C. Levy, “The expectation-maximization Viterbi algorithm for blind adaptive channel equalization,” IEEE Trans. Commun., vol. 53, pp. 1671–1678, Oct. 2005.CrossRefGoogle Scholar
  14. 14.
    A. Sayed, Fundamentals of Adaptive Filtering. New York: Wiley Interscience/IEEE Press, 2003.Google Scholar
  15. 15.
    P. Kovintavewat, J. R. Barry, M. F. Erden, and E. Kurtas, “Per-survivor timing recovery for uncoded partial response channels,” in Proc. 2004 IEEE Internat. Conf. on Communications, vol. 5, (Paris, France), pp. 2715–2719, June 2004.Google Scholar
  16. 16.
    Z. Ding and Y. Li, Blind Equalization and Identification. New York, NY: Marcel Dekker, 2001.Google Scholar
  17. 17.
    I. W. J. Weber, “Differential encoding for multiple amplitude and phase-shift-keying systems,” IEEE Trans. Commun., vol. 26, pp. 385–391, May 1978.CrossRefGoogle Scholar
  18. 18.
    J. G. Proakis, Digital Communications, Fourth Edition. New York: McGraw-Hill, 2000.Google Scholar
  19. 19.
    L. E. Baum, T. Petrie, G. Soules, and N. Weiss, “A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains,” Annals Mathematical Statistics, vol. 41, pp. 164–171, Feb. 1970.CrossRefMATHMathSciNetGoogle Scholar
  20. 20.
    T. K. Moon, “The Expectation-Maximization algorithm,” IEEE Signal Proc. Magazine, pp. 47–60, Nov. 1996.Google Scholar
  21. 21.
    V. Krishnamurthy and J. B. Moore, “On-line estimation of hidden Markov model parameters based on the Kullback-Leibler information measure,” IEEE Trans. Signal Proc., vol. 41, Aug. 1993.Google Scholar
  22. 22.
    M. Feder and J. Catipovic, “Algorithms for joint channel estimation and data recovery– applications to equalization in underwater acoustics,” IEEE J. Ocean Eng., vol. 16, pp. 42–55, Jan. 1991.CrossRefGoogle Scholar
  23. 23.
    K. H. Chang, W. S. Yuan, and C. N. Georghiades, “Block-by-block channel and sequence estimation for ISI/fading channels,” in Signal Processing in Telecommunications: Proceedings of the 7th Thyrrhenian Workshop on Digital Communications, Viareggio, Italy, sept. 10–14, 1995 (E. Biglieri and M. Luise, eds.), pp. 153–170, Berlin, Germany: Springer Verlag, 1996.Google Scholar
  24. 24.
    X.-L. Meng and D. van Dyk, “The EM algorithm – an old folk-song sung to a fast new tune,” J. Royal Stat. Soc., Series B, vol. 59, no. 3, pp. 511–567, 1997.CrossRefMATHGoogle Scholar
  25. 25.
    C. F. J. Wu, “On the convergence properties of the EM algorithm,” Annals Statistics, vol. 11, pp. 95–103, 1983.CrossRefMATHGoogle Scholar
  26. 26.
    G. Ferrari, G. Colavolpe, and R. Raheli, Detection Algorithms for Wireless Communications With Applications to Wired and Storage Systems. Chichester, England: J. Wiley & Sons, 2004.Google Scholar
  27. 27.
    C. N. Georghiades, “Optimum delay and sequence estimation from incomplete data,” IEEE Trans. Informat. Theory, vol. 36, pp. 202–208, Jan. 1990.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Bernard C. Levy
    • 1
  1. 1.University of CaliforniaDavisUSA

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