Journal of Communications and Networks

The Korean Institute of Commucations and Information Sciences (한국통신학회)
권호 표지

A Trellis-based Technique for Blind Channel Estimation and Equalization

  • Cao, Lei (Department of Electrical Engineering, University of Mississippi) ;
  • Chen, Chang-Wen (Department of Electrical and Computer Engineering, florida Institute of Technology) ;
  • Orlik, Philip (Mitsubishi Electric Research Laboratories) ;
  • Zhang, Jinyun (Mitsubishi Electric Research Laboratories) ;
  • Gu, Daqing (Mitsubishi Electric Research Laboratories)
  • Published : 2004.03.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we present a trellis-based blind channel estimation and equalization technique coupling two kinds of adaptive Viterbi algorithms. First, the initial blind channel estimation is accomplished by incorporating the list parallel Viterbi algorithm with the least mean square (LMS) updating approach. In this operation, multiple trellis mappings are preserved simultaneously and ranked in terms of path metrics. Equivalently, multiple channel estimates are maintained and updated once a single symbol is received. Second, the best channel estimate from the above operation will be adopted to set up the whole trellis. The conventional adaptive Viterbi algorithm is then applied to detect the signal and further update the channel estimate alternately. A small delay is introduced for the symbol detection and the decision feedback to smooth the noise impact. An automatic switch between the above two operations is also proposed by exploiting the evolution of path metrics and the linear constraint inherent in the trellis mapping. Simulation has shown an overall excellent performance of the proposed scheme in terms of mean square error (MSE) for channel estimation, robustness to the initial channel guess, computational complexity, and channel equalization.

Keywords

References

  1. L. E. Baum, 'An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes,' Inequalities, vol. 3, pp. 1-8, Jan. 1972
  2. L. R. Rabiner, 'A tutorial on hidden Markov models and selected applications in speech recognition,' Proc. IEEE, vol. 77, no. 2, pp. 257-286, Feb. 1989 https://doi.org/10.1109/5.18626
  3. G.K. Kaleh and R. Vallet, 'Joint parameter estimation and symbol detection for linear or nonlinear unknown channels,' IEEE Trans. Commun., vol. 42, no. 7, pp. 240-2413, July 1994 https://doi.org/10.1109/8.277218
  4. M. Feder and J. A. Catipovic, 'Algorithms for joint channel estimation and data recovery - application to equalization in underwater communications,' IEEE J. Oceanic Eng., vol. 16, no. 1, pp. 42-55, Jan. 1991 https://doi.org/10.1109/48.64884
  5. B.-H. Juang and L. R. Rabiner, 'The segmental K-means algorithm for estimating parameters of hidden markov models,' IEEE Trans. Acoust., Speech, Signal Processing, vol. 38, no. 9, pp. 1639-1641, Sept. 1990 https://doi.org/10.1109/29.60082
  6. N. Seshadri, 'Joint data and channel estimation using blind trellis search techniques,' IEEE Trans. Commun., vol. 42, no. 2/3/4, pp.1000-1011, Feb./Mar./Apr. 1994 https://doi.org/10.1109/TCOMM.1994.580208
  7. E. Zervas, J. Proakis, and V. Eyuboglu, 'A 'quantized' channel approach to blind equalization,' in Proc. ICC'92, June, vol. 3, pp. 1539-1543
  8. I. G. Proakis, Digital Communications, McGraw-Hill, 4th ed., 2000
  9. J. G. D. Forney, 'Convolutional codes II: Maximum likelihood decoding,' Information and Control, vol. 25, pp. 222-266, July 1974 https://doi.org/10.1016/S0019-9958(74)90870-5