Open Access

Iterative Refinement Methods for Time-Domain Equalizer Design

  • Güner Arslan1,
  • Biao Lu2,
  • Lloyd D. Clark3, 4 and
  • Brian L. Evans5
EURASIP Journal on Advances in Signal Processing20062006:043154

https://doi.org/10.1155/ASP/2006/43154

Received: 1 December 2004

Accepted: 2 August 2005

Published: 2 March 2006

Abstract

Commonly used time domain equalizer (TEQ) design methods have been recently unified as an optimization problem involving an objective function in the form of a Rayleigh quotient. The direct generalized eigenvalue solution relies on matrix decompositions. To reduce implementation complexity, we propose an iterative refinement approach in which the TEQ length starts at two taps and increases by one tap at each iteration. Each iteration involves matrix-vector multiplications and vector additions with matrices and two-element vectors. At each iteration, the optimization of the objective function either improves or the approach terminates. The iterative refinement approach provides a range of communication performance versus implementation complexity tradeoffs for any TEQ method that fits the Rayleigh quotient framework. We apply the proposed approach to three such TEQ design methods: maximum shortening signal-to-noise ratio, minimum intersymbol interference, and minimum delay spread.

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Authors’ Affiliations

(1)
Silicon Laboratories, Corporate Headquarters
(2)
Schlumberger Sugar Land Product Center
(3)
Schlumberger Austin Systems Center
(4)
TICOM Geomatics
(5)
Department of Electrical and Computer Engineering, The University of Texas

References

  1. ANSI T1.413-1995, Network and customer installation interfaces: Asymmetrical digital subscriber line (ADSL) metallic interface printed from: Digital Subscriber Line Technology by T. Starr, J. M. Cioffi, and P. J. Silverman, Prentice-Hall, 1999Google Scholar
  2. Martin RK, Vanbleu K, Ding M, et al.: Unification and evaluation of equalization structures and design algorithms for discrete multitone modulation systems. IEEE Transactions Signal Processing 2005, 53(10, part 1):3880-3894.MathSciNetView ArticleGoogle Scholar
  3. Chow JS, Cioffi JM: A cost-effective maximum likelihood receiver for multicarrier systems. Proceedings of IEEE International Conference on Communications (ICC '92), June 1992, Chicago, Ill, USA 2: 948-952.Google Scholar
  4. Van Acker K, Leus G, Moonen M, van de Wiel O, Pollet T: Per tone equalization for DMT-based systems. IEEE Transactions on Communications 2001, 49(1):109-119. 10.1109/26.898255View ArticleMATHGoogle Scholar
  5. Ding M, Shen Z, Evans BL: An achievable performance upper bound for discrete multitone equalization. Proceedings of IEEE Global Telecommunications Conference (GLOBECOM '04), November–December 2004, Dallas, Tex, USA 4: 2297-2301.View ArticleGoogle Scholar
  6. Melsa PJW, Younce RC, Rohrs CE: Impulse response shortening for discrete multitone transceivers. IEEE Transactions on Communications 1996, 44(12):1662-1672. 10.1109/26.545896View ArticleGoogle Scholar
  7. Arslan G, Evans BL, Kiaei S: Equalization for discrete multitone transceivers to maximize bit rate. IEEE Transactions Signal Processing 2001, 49(12):3123-3135. 10.1109/78.969519View ArticleGoogle Scholar
  8. Schur R, Speidel J: An efficient equalization method to minimize delay spread in OFDM/DMT systems. Proceedings of IEEE International Conference on Communications (ICC '01), June 2001, Helsinki, Finland 5: 1481-1485.Google Scholar
  9. Martin RK, Vanbleu K, Ding M, et al.: Implementation complexity and communication performance tradeoffs in discrete multitone modulation equalizers. to appear in IEEE Trans. Signal Processing, http://www.ece.utexas.edu/~bevans/papers/2005/equalizationII to appear in IEEE Trans. Signal Processing,
  10. Ding M, Evans BL, Wong I: Effect of channel estimation error on bit rate performance of time domain equalizers. Proceedings of 38th IEEE Asilomar Conference on Signals, Systems and Computers, November 2004, Pacific Grove, Calif, USA 2: 2056-2060.Google Scholar
  11. Golub GH, Van Loan CF: Matrix Computation. 3rd edition. John Hopkins University Press, Baltimore, Md, USA; 1996.MATHGoogle Scholar
  12. Yin C, Yue G: Optimal impulse response shortening for discrete multitone transceivers. IEE Electronics Letters 1998, 34(1):35-36. 10.1049/el:19980011View ArticleGoogle Scholar
  13. Demmel JW: Applied Numerical Linear Algebra. SIAM, Philadelphia, Pa, USA; 1997.View ArticleMATHGoogle Scholar
  14. Lu B, Clark LD, Arslan G, Evans BL: Fast Time-Domain Equalization for Discrete Multitone Modulation Systems. Proceedings of IEEE Digital Signal Processing Workshop, October 2000, Hunt, Tex, USAGoogle Scholar
  15. Arslan G, Ding M, Lu B, Milosevic M, Shen Z, Evans BL: MATLAB DMTTEQ Toolbox 3.1. 2003.http://www.ece.utexas.edu/~bevans/projects/adsl/dmtteq/dmtteq.html Available at:Google Scholar
  16. Al-Dhahir N, Cioffi JM: A bandwidth-optimized reduced-complexity equalized multicarrier transceiver. IEEE Transactions on Communications 1997, 45(8):948-956. 10.1109/26.618299View ArticleGoogle Scholar

Copyright

© Güner Arslan et al. 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.