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Wireless Personal Communications

, Volume 10, Issue 3, pp 319–339 | Cite as

Joint Data and Kalman Estimation for Rayleigh Fading Channels

  • M.J. Omidi
  • S. Pasupathy
  • P.G. Gulak
Article

Abstract

Channel estimation is an essential part of many detection techniques proposed for data transmission over fading channels. For the frequency selective Rayleigh fading channel an autoregressive moving average representation is proposed based on the fading model parameters. The parameters of this representation are determined based on the fading channel characteristics, making it possible to employ the Kalman filter as the best estimator for the channel impulse response. For IS-136 formatted data transmission the Kalman filter is employed with the Viterbi algorithm in a Per-Survivor Processing (PSP) fashion and the ove rall bit error rate performance is shown to be superior to that of detection techniques using the RLS and LMS estimators. To allow more than one channel estimation per symbol interval, Per-Branch Processing (PBP) method is introduced as a general case of PSP and its effect on performance is evaluated. The sensitivity of performance to parameters such as fading model order and vehicle speed is also studied.

fading channel channel estimation Kalman filtering MLSE-based 

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References

  1. 1.
    Q. Dai and E. Shwedyk, "Detection of Bandlimited Signals over Frequency Selective Rayleigh Fading Channels", IEEE Transaction on Communications, Vol. 42, pp. 941–950, 1994.Google Scholar
  2. 2.
    J. Lin, F. Ling and J.G. Proakis, "Joint Data and Channel Estimation for TDMA Mobile Channels", International Journal of Wireless Information Networks, Vol. 1, No. 4, pp. 229–238, 1994.Google Scholar
  3. 3.
    R. Raheli, A. Polydoros and C. Tzou, "Per-Survivor Processing: A General Approach to MLSE in Uncertain Environments", IEEE Transaction on Communications, Vol. 43, pp. 354–364, 1995.Google Scholar
  4. 4.
    R. Fukawa and H. Suzuki, "Adaptive Equalization with RLS-MLSE for Frequency-Selective Fast Fading Mobile Radio Channels", Globecom '91, pp. 16.6.1–16.6.5, 1991.Google Scholar
  5. 5.
    B.D.O. Anderson and J.B. Moore, "Optimal Filtering", Prentice-Hall, 1979.Google Scholar
  6. 6.
    A.H. Sayed and T. Kailath, "A State-Space Approach to Adaptive RLS Filtering", IEEE Signal Processing Magazine, Jul. 1994.Google Scholar
  7. 7.
    M.E. Rollins and S.J. Simmons, "Error Performance Analysis of MLSE for Frequency-Selective Rayleigh Fading Channels with Kalman Channel Estimation", Proceedings of ICC '94, Vol. 1, pp. 312–326, 1994.Google Scholar
  8. 8.
    E.A. Lee and D.G. Messerschmitt, Digital Communication, Kluwer, second edition, 1994.Google Scholar
  9. 9.
    R.H. Clarke, "A Statistical Theory of Mobile-Radio Reception", Bell System Tech. J., Vol. 47, pp. 957–1000, 1968.Google Scholar
  10. 10.
    W.C. Jakes, Microwave Mobile Communications, Wiley, 1974.Google Scholar
  11. 11.
    G.A. Arredondo and W.H. Chriss, "A Multipath Fading Simulator for Mobile Radio", IEEE Transaction on Vehicular Technology, Vol. 22, pp. 241–244, Nov. 1973.Google Scholar
  12. 12.
    J.D. Parsons, The Mobile Radio Propagation Channel, Pentech Press, 1992.Google Scholar
  13. 13.
    S.M. Kay, Modern Spectral Estimation: Theory and Application, Prentice Hall, 1988.Google Scholar
  14. 14.
    J.G. Proakis, Digital Communications, McGraw-Hill, 3rd edition, 1995.Google Scholar
  15. 15.
    J.H. Lodge and M.J. Moher, "Maximum Likelihood Sequence Estimation of CPM Signals Transmitted over Rayleigh Flat-Fading Channels", IEEE Transaction on Communications, Vol. 38, pp. 787–794, Jun. 1990.Google Scholar
  16. 16.
    G.M. Vitetta and D.P. Taylor, "Maximum Likelihood Decoding of Uncoded and Coded PSK Signal Sequences Transmitted over Rayleigh Flat-Fading Channels", IEEE Transaction on Communications, Vol. 43, pp. 2750–2758, Nov. 1995.Google Scholar
  17. 17.
    P. Rao and M.A. Bayoumi, "An Efficient VLSI Implementation of Real-Time Kalman Filter", IEEE International Symposium on Circuit and Systems", Vol. 3, pp. 2353–2356, 1990.Google Scholar
  18. 18.
    P. Rao and M.A. Bayoumi, "An Algorithm Specific VLSI Parallel Architecture for Kalman Filter", VLSI Signal Processing IV, IEEE Press, pp. 264–273, 1991.Google Scholar
  19. 19.
    M.A. Bayoumi, P. Rao and B. Alhalabi, "VLSI Parallel Architecture for Kalman Filter an Algorithm Specific Approach", Journal of VLSI Signal Processing, Vol. 4, pp. 147–163, Kluwer Academic Publishers, 1992.Google Scholar

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • M.J. Omidi
    • 1
  • S. Pasupathy
    • 2
  • P.G. Gulak
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity ofTorontoCanada
  2. 2.Department of Electrical and Computer EngineeringUniversity ofTorontoCanada

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