Iterative Refinement Methods for Time-Domain Equalizer Design

  • Güner Arslan
  • Biao Lu
  • Lloyd D. Clark
  • Brian L. Evans
Open Access
Research Article
Part of the following topical collections:
  1. Advanced Signal Processing for Digital Subscriber Lines


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 Open image in new window 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.


Objective Function Communication Performance Delay Spread Minimum Delay Matrix Decomposition 


  1. 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. 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.MathSciNetCrossRefGoogle Scholar
  3. 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. 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. 10.1109/26.898255CrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. 6.
    Melsa PJW, Younce RC, Rohrs CE: Impulse response shortening for discrete multitone transceivers. IEEE Transactions on Communications 1996, 44(12):1662–1672. 10.1109/26.545896CrossRefGoogle Scholar
  7. 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. 10.1109/78.969519CrossRefGoogle Scholar
  8. 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. 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, to appear in IEEE Trans. Signal Processing,
  10. 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. 11.
    Golub GH, Van Loan CF: Matrix Computation. 3rd edition. John Hopkins University Press, Baltimore, Md, USA; 1996.zbMATHGoogle Scholar
  12. 12.
    Yin C, Yue G: Optimal impulse response shortening for discrete multitone transceivers. IEE Electronics Letters 1998, 34(1):35–36. 10.1049/el:19980011MathSciNetCrossRefGoogle Scholar
  13. 13.
    Demmel JW: Applied Numerical Linear Algebra. SIAM, Philadelphia, Pa, USA; 1997.CrossRefGoogle Scholar
  14. 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. 15.
    Arslan G, Ding M, Lu B, Milosevic M, Shen Z, Evans BL: MATLAB DMTTEQ Toolbox 3.1. 2003. Available at:Google Scholar
  16. 16.
    Al-Dhahir N, Cioffi JM: A bandwidth-optimized reduced-complexity equalized multicarrier transceiver. IEEE Transactions on Communications 1997, 45(8):948–956. 10.1109/26.618299CrossRefGoogle Scholar

Copyright information

© 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.

Authors and Affiliations

  • Güner Arslan
    • 1
  • Biao Lu
    • 2
  • Lloyd D. Clark
    • 3
    • 4
  • Brian L. Evans
    • 5
  1. 1.Silicon LaboratoriesCorporate HeadquartersAustinUSA
  2. 2.Schlumberger Sugar Land Product CenterSugar LandUSA
  3. 3.Schlumberger Austin Systems CenterAustinUSA
  4. 4.TICOM GeomaticsAustinUSA
  5. 5.Department of Electrical and Computer EngineeringThe University of TexasAustinUSA

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