Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Improved Soft Iterative Channel Estimation for Turbo Equalization of Time Varying Frequency Selective Channels

  • 108 Accesses

  • 8 Citations

Abstract

In this paper, we present computationally efficient iterative channel estimation algorithms for Turbo equalizer-based communication receiver. Least Mean Square (LMS) and Recursive least Square (RLS) algorithms have been widely used for updating of various filters used in communication systems. However, LMS algorithm, though very simple, suffers from a relatively slow and data dependent convergence behaviour; while RLS algorithm, with its fast convergence rate, finds little application in practical systems due to its computational complexity. Variants of LMS algorithm, Variable Step Size Normalized LMS (VSSNLMS) and Multiple Variable Step Size Normalized LMS algorithms, are employed through simulation for updating of channel estimates for turbo equalization in this paper. Results based on the combination of turbo equalizer with convolutional code as well as with turbo codes alongside with iterative channel estimation algorithms are presented. The simulation results for different normalized fade rates show how the proposed channel estimation based-algorithms outperformed the LMS algorithm and performed closely to the well known Recursive least square (RLS)-based channel estimation algorithm.

This is a preview of subscription content, log in to check access.

References

  1. 1.

    Berrou, C., Glavieus, A., & Thitimajshima, P. (1993). Near Shannon limit error correcting coding and decoding: Turbo-codes. In IEEE International Conference on Communications (pp. 1064–1070).

  2. 2.

    Berrous C., Glavieux A. (1996) Near optimum error correcting coding and decoding: Turbo codes. IEEE Transactions on Communications 44: , 1261–1271

  3. 3.

    Douillard C., Jézéquel M., Berrou C., Picart A., Didier P., Glavieux A. (1995) Iterative correction of intersymbol interference: Turbo- equalization. European Transactions on Telecommunications 6: 507–511

  4. 4.

    Proakis J.G. (1995) communications, 3rd edn. McGraw-Hill, New York

  5. 5.

    Tuchler M., Koetter R., Singer A.C. (2002) Turbo equalization: Principles and new results unknown. IEEE Transactions on Communications 50: 754–767

  6. 6.

    Otnes R., Tuchler M. (2004) Iterative channel estimation for turbo equalization of time varying frequency selective channel. IEEE Transactions on Communications 3(6): 1918–1923

  7. 7.

    Otnes, R., & Tuchler, M. (2002). Soft iterative channel estimation for turbo equalization: Comparison of channel algorithms. In Proceedings of 8th IEEE International Conference on Communication System, ICCC 2002, Singapore, pp. 72–76.

  8. 8.

    Sandell, M., Luschi, C., Strauch, P., & Yan, R. Iterative channel estimation using soft decision feedback. In Proceedings on IEEE Global Telecommunications Conference, GLOBECOM 98.

  9. 9.

    Otnes, R., & Tüchler, M. (2002). Low-complexity turbo equalization for time-varying channels. In Proceedings on 55th IEEE VTS Vehicular Technology Conference, VTC 2002 Spring, Vol. 1, Birmingham, AL, USA, pp. 140–144.

  10. 10.

    Haykin S. (1996) Adaptive filter theory. 3rd edn. Upper Saddle River, Prentice Hall

  11. 11.

    Furman, W. N., & Nieto, J. W. (2001). Understanding HF channel simulator requirements in order to reduce HF modem performance measurement variability. In Proceedings on 6th Nordic Shortwave Conference HF, Fårö, Sweden, pp. 6.4.1–6.4.13.

  12. 12.

    Mathews V.J., Xie Z. (1993) A stochastic gradient adaptive filter with gradient adaptive step-size. IEEE Transactions on Signal Processing 41(6): 2075–2087

  13. 13.

    Aboulnasr T. (1997) A Robust variable step-size LMS-type algorithm: Analysis and simulation. IEEE Transactions on Signal Processing 45(3): 631–639

  14. 14.

    Pazaitis D.I., Constantinides A.G. (1999) A novel kurtosis driven variable step-size adaptive algorithm. IEEE Transactions on Signal Processing 47(3): 864–872

  15. 15.

    Shin, Y. K., & Lee, J. G. (1985). A study on the fast convergence algorithm for the LMS adaptive filter design. In Proceedings on KIEE, Vol. 19(5), pp. 12–19.

  16. 16.

    Tarrab M., Feuer A. (1988) Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data. IEEE Transactions on Information Theory 34(4): 680–691

  17. 17.

    Schafhuber D., Matz G. (2005) MMSE and adaptive prediction of time-varying channels for OFDM systems. IEEE Transactions on Wireless Communications 4(2): 593–602

  18. 18.

    Tuchler, M., Singer, A. C., & Koetter, R. (2002). Minimum mean squared error equalization using a priori information. IEEE Transactions on Signal Processing, 50, pp. 673–683.

  19. 19.

    Komninakis, C. (2003). A fast and accurate Rayleigh fading simulator. IEEE Global Communications Conference, GLOBECOM 2003, GC01-8, San Francisco, Vol. 6, pp. 3306–3310.

  20. 20.

    Tuchler M., Koetter R., Singer A.C. (2002) Turbo equalization: Principles and new results unknown. IEEE Transactions on Communications 50: 754–767

  21. 21.

    Raphaeli, D., & Zarai, Y. (1998). Combined turbo equalization and turbo decoding. IEEE Communication Letters, 2(4), pp. 107–109.

Download references

Author information

Correspondence to Stanley H. Mneney.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Oyerinde, O.O., Mneney, S.H. Improved Soft Iterative Channel Estimation for Turbo Equalization of Time Varying Frequency Selective Channels. Wireless Pers Commun 52, 325 (2010). https://doi.org/10.1007/s11277-008-9650-7

Download citation

Keywords

  • Soft iterative channel estimation
  • Turbo equalization
  • Turbo code
  • Convolutional code
  • Turbo decoding
  • Variable step size NLMS algorithm