Skip to main content

Equalization of Sparse Intersymbol-Interference Channels Revisited

Abstract

Sparse intersymbol-interference (ISI) channels are encountered in a variety of communication systems, especially in high-data-rate systems. These channels have a large memory length, but only a small number of significant channel coefficients. In this paper, equalization of sparse ISI channels is revisited with focus on trellis-based techniques. Due to the large channel memory length, the complexity of maximum-likelihood sequence estimation by means of the Viterbi algorithm is normally prohibitive. In the first part of the paper, a unified framework based on factor graphs is presented for complexity reduction without loss of optimality. In this new context, two known reduced-complexity trellis-based techniques are recapitulated. In the second part of the paper a simple alternative approach is investigated to tackle general sparse ISI channels. It is shown that the use of a linear filter at the receiver renders the application of standard reduced-state trellis-based equalization techniques feasible without significant loss of optimality.

[1234567891011121314151617181920212223242526272829]

References

  1. Rappaport TS: Wireless Communications—Principles and Practice. Prentice-Hall, Upper Saddle River, NJ, USA; 1996.

    MATH  Google Scholar 

  2. Lee FKH, McLane PJ: Design of nonuniformly spaced tapped-delay-line equalizers for sparse multipath channels. IEEE Transactions on Communications 2004,52(4):530–535. 10.1109/TCOMM.2004.826351

    Article  Google Scholar 

  3. Fevrier IJ, Gelfand SB, Fitz MP: Reduced complexity decision feedback equalization for multipath channels with large delay spreads. IEEE Transactions on Communications 1999,47(6):927–937. 10.1109/26.771349

    Article  Google Scholar 

  4. Cotter SF, Rao BD: The adaptive matching pursuit algorithm for estimation and equalization of sparse time-varying channels. Proceedings of the 34th Asilomar Conference on Signals, Systems and Computers, November 2000, Pacific Grove, Calif, USA 2: 1772–1776.

  5. Haratsch EF, Blanksby AJ, Azadet K: Reduced-state sequence estimation with tap-selectable decision-feedback. Proceedings of IEEE International Conference on Communications (ICC '00), June 2000, New Orleans, La, USA 1: 372–376.

  6. Rontogiannis AA, Berberidis K: Efficient decision feedback equalization for sparse wireless channels. IEEE Transactions on Wireless Communications 2003,2(3):570–581. 10.1109/TWC.2003.811189

    Article  Google Scholar 

  7. Benvenuto N, Marchesani R: The Viterbi algorithm for sparse channels. IEEE Transactions on Communications 1996,44(3):287–289. 10.1109/26.486320

    Article  MATH  Google Scholar 

  8. McGinty NC, Kennedy RA, Hoeher PA: Parallel trellis Viterbi algorithm for sparse channels. IEEE Communications Letters 1998,2(5):143–145. see also: N. C. McGinty, R. A. Kennedy, and P. A. Hoeher, "Equalization of sparse ISI channels using parallel trellises," in Proceedings of 7th Communication Theory Mini-Conference in conjunction with IEEE Globecom '98, pp. 65–70, 1998 10.1109/4234.673661

    Article  Google Scholar 

  9. McGinty NC: Reduced complexity equalization for data communication, Ph.D. dissertation. , Canberra, Australia; 1997.

    Google Scholar 

  10. Lee FKH, McLane PJ: Iterative parallel-trellis MAP equalizers with nonuniformly-spaced prefilters for sparse multipath channels. Proceedings of 56th IEEE Vehicular Technology Conference (VTC '02), September 2002, Vancouver, BC, Canada 4: 2201–2205.

  11. Forney GD Jr.: Maximum-likelihood sequence estimation of digital sequences in the presence of intersymbol interference. IEEE Transactions on Information Theory 1972,18(3):363–378. 10.1109/TIT.1972.1054829

    MathSciNet  Article  MATH  Google Scholar 

  12. Bahl LR, Cocke J, Jelinek F, Raviv J: Optimal decoding of linear codes for minimizing symbol error rate. IEEE Transactions on Information Theory 1974,20(2):284–287.

    MathSciNet  Article  MATH  Google Scholar 

  13. Badri-Hoeher S: Digitale Empfängeralgorithmen für TDMA-Mobilfunksysteme mit besonderer Berücksichtigung des EDGE-Systems, Ph.D. dissertation. , Erlangen, Germany; 2001.

    Google Scholar 

  14. Gerstacker WH, Obernosterer F, Meyer R, Huber JB: On prefilter computation for reduced-state equalization. IEEE Transactions on Wireless Communications 2002,1(4):793–800. 10.1109/TWC.2002.804159

    Article  Google Scholar 

  15. Badri-Hoeher S, Hoeher PA: Fast computation of a discrete-time whitened matched filter based on Kalman filtering. IEEE Transactions on Wireless Communications 2004,3(6):2417–2424. 10.1109/TWC.2004.833442

    Article  Google Scholar 

  16. Hagenauer J: A soft-in/soft-out list sequential (LISS) decoder for turbo schemes. Proceedings of IEEE International Symposium on Information Theory (ISIT '03), June-July 2003, Kanagawa, Japan 382.

  17. Kuhn C: A bidirectional list-sequential (BI-LISS) equalizer for turbo schemes. Proceedings of the 14th IST Mobile & Wireless Communications Summit, June 2005, Dresden, Germany paper no. 306

  18. Liu L, Leung WK, Ping L: Simple iterative chip-by-chip multiuser detection for CDMA systems. Proceedings of 57th IEEE Vehicular Technology Conference (VTC '03), October 2003, Orlando, Fla, USA 3: 2157–2161.

  19. Loeliger H-A: An introduction to factor graphs. IEEE Signal Processing Magazine 2004,21(1):28–41. 10.1109/MSP.2004.1267047

    Article  Google Scholar 

  20. Colavolpe G, Germi G: On the application of factor graphs and the sum-product algorithm to ISI channels. IEEE Transactions on Communications 2005,53(5):818–825. 10.1109/TCOMM.2005.847129

    Article  Google Scholar 

  21. Douillard C, Jezequel M, Berrou C, Picart A, Didier P, Glavieux A: Iterative correction of intersymbol interference: turbo-equalization. European Transactions on Telecommunications and Related Technologies 1995,6(5):507–511. 10.1002/ett.4460060506

    Article  Google Scholar 

  22. Park J, Gelfand SB: Turbo equalizations for sparse channels. Proceedings of IEEE Wireless Communications and Networking Conference (WCNC '04), March 2004, Atlanta, Ga, USA 4: 2301–2306.

  23. Cusani R, Mattila J: Equalization of digital radio channels with large multipath delay for cellular land mobile applications. IEEE Transactions on Communications 1999,47(3):348–351. 10.1109/26.752812

    Article  Google Scholar 

  24. Eyuboǧlu MV, Qureshi SUH: Reduced-state sequence estimation with set partitioning and decision feedback. IEEE Transactions on Communications 1988,36(1):13–20. 10.1109/26.2724

    Article  Google Scholar 

  25. Duel-Hallen A, Heegard C: Delayed decision-feedback sequence estimation. IEEE Transactions on Communications 1989,37(5):428–436. 10.1109/26.24594

    Article  Google Scholar 

  26. Kammeyer K-D: Time truncation of channel impulse responses by linear filtering: a method to reduce the complexity of Viterbi equalization. AEÜ International Journal of Electronics and Communications 1994,48(5):237–243.

    Google Scholar 

  27. Haykin S: Adaptive Filter Theory. 4th edition. Prentice Hall, Upper Saddle River, NJ, USA; 2002.

    MATH  Google Scholar 

  28. Al-Dhahir N, Cioffi JM: Efficiently computed reduced-parameter input-aided MMSE equalizers for ML detection: a unified approach. IEEE Transactions on Information Theory 1996,42(3):903–915. 10.1109/18.490553

    Article  MATH  Google Scholar 

  29. Proakis JG: Digital Communications. 4th edition. McGraw-Hill, New York, NY, USA; 2001.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jan Mietzner.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Mietzner, J., Badri-Hoeher, S., Land, I. et al. Equalization of Sparse Intersymbol-Interference Channels Revisited. J Wireless Com Network 2006, 029075 (2006). https://doi.org/10.1155/WCN/2006/29075

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1155/WCN/2006/29075

Keywords

  • Communication System
  • Significant Loss
  • Unify Framework
  • Complexity Reduction
  • Equalization Technique