Skip to main content
SpringerLink
Log in

Algorithms for Adaptive Equalization in Wireless Applications

  • Chapter
Adaptive Signal Processing

Part of the book series: Signals and Communication Technology ((SCT))

  • 807 Accesses

Abstract

Since the introduction of adaptive equalizers in digital communication systems by Lucky [1], much progress has been made. Due to their particular constraints many new and different concepts in the wireless domain have been proposed. The wireless channel is typically time and frequency dispersive, making it difficult to use standard equalizer techniques. Also, due to its time varying nature, long transmission bursts may get corrupted and require a continuous tracking operation. Thus, transmission is often performed in short bursts, allowing only a limited amount of training data. Furthermore, quite recently, advantages of the multiple-input multiple-output character of wireless channels have been recognized. This chapter presents an overview of equalization techniques in use and emphasizes the particularities of wireless applications.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. R. W. Lucky, “Automatic equalization for digital communication,” Bell Syst. Tech. J, vol. 44, pp. 547 - 588, Apr. 1965.

    Google Scholar 

  2. G. Forney, “Maximum likelihood sequence estimation of digital sequences in the presence of intersymbol interference,” IEEE Trans. Information Theory, vol. 18, no. 3, pp. 363 - 378, 1972.

    Article  MathSciNet  MATH  Google Scholar 

  3. J. M. Cioffi, G. Dudevoir, M. Eyuboglu, and G. D. Forney, Jr, “MMSE decision feedback equalization and coding–Part I,” IEEE Trans. Commun, vol. 43, no. 10, pp. 2582 - 2594, Oct. 1995.

    MATH  Google Scholar 

  4. N. Al-Dhahir and J. M. Cioffi, “MMSE decision feedback equalizers: finite length results,” IEEE Trans. Information Theory, vol. 41, no. 4, pp. 961 - 975, July 1995.

    Article  MATH  Google Scholar 

  5. J. R. Treichler, I. Fijalkow, and C.R.Johnson, Jr, “Fractionally spaced equalizers,” IEEE Signal Processing Mag, pp. 65 - 81, May 1996.

    Google Scholar 

  6. J. K. Tugnait, L. Tong, and Z.Ding, “Single user channel estimation and equalization,” IEEE Signal Processing Mag, vol. 17, no. 3, pp. 17 - 28, May 2000.

    Article  Google Scholar 

  7. P. A. Fuhrman, A Polynomial Approach to Linear Algebra. Springer, N.Y., 1996.

    Book  Google Scholar 

  8. S. Haykin, Unsupervised Adaptive Filtering. Wiley-Interscience, N.Y., 2000.

    Google Scholar 

  9. T. Kailath, Linear Systems. Prentice Hall, Englewood Cliffs, N.J., 1980.

    MATH  Google Scholar 

  10. G. B. Giannnakis et al., Signal Processing Advances in Wireless and Mobile Communications. Prentice Hall, vol. 1, 2001.

    Google Scholar 

  11. P. W. Wolniansky, G. J. Foschini, G. D. Golden, and R. A. Valenzuela, “V-BLAST: an architecture for achieving very high data rates over rich-scattering wireless channels,” in Conf. Record of ISSSE, Pisa, Italy 98.

    Google Scholar 

  12. A. Burg, E. Beck, M. Rupp, D. Perels, N. Felber, and W. Fichtner, “FPGA implementation of a MIMO receiver front-end for UMTS,” in Conf. Record of Int. Zuerich Seminar on Broadband Commun, 2002, pp. 8 1-8 6.

    Google Scholar 

  13. R. Van Nee, A. van Zelst, and G. Awater, “Maximum likelihood decoding in a space division multiplexing system,” in Conf. Record of VTC Japan, 2000.

    Google Scholar 

  14. B. A. Bjerke and J. G. Proakis, “Equalization and decoding for multiple-input multiple-output wireless channels,” Eurasip Journal on Applied Signal Processing, vol. 3, pp. 249 - 266, 2002.

    MathSciNet  Google Scholar 

  15. J. Salz, “Optimum mean-square decision feedback equalization,” Bell Syst. Tech. J, vol. 52, no. 8, Oct. 1973.

    Google Scholar 

  16. C. B. Papadias and A. J. Paulraj, “Unbiased decision feedback equalization,” in Conf. Record of the IEEE Intern. Symp. on IT, 1998, pp. 448.

    Google Scholar 

  17. C. B. Papadias and M. Rupp, “Performance analysis of finite-length DFE receivers based on a polyphase representation,” in Conf. Record of the 32nd. Asilomar Conf. of Signals, Systems and Computers, 1998, pp. 374 - 378.

    Google Scholar 

  18. J. G. Proakis, Digital Communications. McGraw Hill, Fourth edition, 2001.

    Google Scholar 

  19. I. Ghauri and D. T. M. Slock, “Linear receivers for the DS-CDMA downlink exploiting orthogonality of spreading sequences,” in Conf. Record of the 32nd. Asilomar Conf. of Signals, Systems and Computers, 1998.

    Google Scholar 

  20. M. Rupp, “Normalization and convergence of gradient-based algorithms for adaptive IIR filters,” Signal Processing, vol. 46, no. 1, pp. 15 - 30, Sept. 1995.

    Article  MATH  Google Scholar 

  21. S. Haykin, Adaptive Filter Theory. Fourth Edition, Prentice Hall, 2001.

    Google Scholar 

  22. M. Rupp, “On the learning behavior of decision feedback equalizers,” in Conf. Record of the 33rd. Asilomar Conf. of Signals, Systems and Computers, vol. 1, 1999, pp. 514 - 518.

    Google Scholar 

  23. A. P. Liavas and P. A. Regalia, “On the numerical stability and accuracy of the conventional recursive least squares algorithm,” IEEE Trans. Signal Processing, pp. 88 - 96, Jan. 1999.

    Google Scholar 

  24. H. Mohamad, S. Weiss, M. Rupp, and L. Hanzo, “A fast converging fractionally spaced equalizer,” in Conf. Record of the 35th Asilomar Conf. of Signals, Systems and Computers, 2001.

    Google Scholar 

  25. H. Mohamad, S. Weiss, M. Rupp, and L. Hanzo, “Fast adaptation of fractionally spaced equalizers,” Electronic Letters, vol. 38, no. 2, pp. 96 - 98, Jan. 17, 2002.

    Google Scholar 

  26. S. L. Gay, “An efficient, fast converging adaptive filter for network echo cancellation,” in Conf. Record of the 32nd. Asilomar Conf. of Signals, Systems and Computers, 1998, pp. 394 - 398.

    Google Scholar 

  27. M. Rupp and J. Cezanne, “Robustness conditions of the LMS algorithm with time-variant matrix step-size,” Signal Processing, vol. 80, no. 9, pp. 1787 - 1794, Sept. 2000.

    Article  MATH  Google Scholar 

  28. M. Rupp and A. H. Sayed, “Robustness and convergence of adaptive schemes in blind equalization,” in Conf. Record of the 30th. Asilomar Conf. on Signals, Systems and Computers, vol. 1, 1996, pp. 271 - 275.

    Google Scholar 

  29. A. Bahai and M. Rupp, “Adaptive DFE algorithms for IS-136 based TDMA cellular phones,” in Conf. Record of IEEE International Conf. on Acoustics, Speech, and Signal Processing, vol. 3, 1997, pp. 2489 - 2492.

    Google Scholar 

  30. J. Balakrishnan and C. R. Johnson, Jr., “Time-reversal diversity in decision feedback equalization,” in Conf. Record of Allerton Conf. on Communication, Control and Computing, ( Monticello, IL ), 2000.

    Google Scholar 

  31. S. N. Crozier, D. D. Falconer, and S. A. Mahmoud, “Least sum of squared errors channel estimation,” IEE Proc. F, vol. 138, no. 4, pp. 371 - 378, Aug. 1991.

    Google Scholar 

  32. M. Rupp, “Fast implementation of the LMS algorithm,” in Conf. Record of Eusipco, Tampere, 2000.

    Google Scholar 

  33. J. Balakrishnan, M. Rupp, and H. Vishwanatan, “Optimal channel training for multiple antenna systems,” in Conf. Record of Multiaccess, Mobility and Teletraffic for Wireless Communications, 2000.

    Google Scholar 

  34. T. S. Rappaport, Wireless Communications. Prentice Hall, 1996.

    Google Scholar 

  35. L. Greenstein and B. Czekaj, “Modeling multipath fading responses using multitone probing signals and polynomial approximation,” Bell Syst. Tech. J, vol. 60, pp. 193 - 214, 1981.

    Google Scholar 

  36. M. K. Tsatsanis and G. B. Giannakis, “Modeling and equalization of rapidly fading channels,” Int. J. Adaptive Control Signal Processing, vol. 10, pp. 159– 176, 1996.

    Google Scholar 

  37. A. Duel-Hallen, S. Hu, and H. Hallen, “Long range prediction of fading signals,” IEEE Signal Processing Mag, vol. 17, no. 3, pp. 62 - 75, May 2000.

    Article  Google Scholar 

  38. R. A. Iltis and A. W. Fuxjaeger, “A digital DS spread-spectrum receiver with joint channel and Doppler shift estimation,” IEEE Trans. Commun, vol. 39, no. 8, Aug. 1991,

    Google Scholar 

  39. W. C. Jakes, Microwave Mobile Communication. IEEE Press, 1974.

    Google Scholar 

  40. T. Kailath, A. H. Sayed, and B. Hassibi, Linear Estimation. Prentice Hall, 1999.

    Google Scholar 

  41. L. Lindbom, M. Sternad, and A. Ahlen, “Tracking of time-varying mobile radio channels: part I. The Wiener LMS algorithm,” IEEE Trans. Commun, pp. 2207 - 2217, Dec. 2001.

    Google Scholar 

  42. L. Lindbom, A. Ahlen, M. Sternad, and M. Falkenstrom, “Tracking of time-varying mobile radio channels: part II. A case study,” IEEE Trans. Commun, pp. 156 - 167, Jan. 2002.

    Google Scholar 

  43. M. C. Chiu and C. Chao, “Analysis of LMS-adaptive MLSE equalization on multipath fading channels,” IEEE Trans. Commun, pp. 1684 - 1692, Dec. 1996.

    Google Scholar 

  44. A. J. Viterbi, “Error bounds for convolutional codes and an asymptotically optimum decoding algorithm,” IEEE Trans. Information Theory, vol. IT-13, Apr. 1967.

    Google Scholar 

  45. E. A. Lee and D. G. Messerschmitt, Digital Communication. Kluwer, 2nd edition, 1994.

    Google Scholar 

  46. H. L. Lou, “Implementing the Viterbi algorithm,” IEEE Signal Processing Mag, vol. 12, no. 5, pp. 42 - 52, Sept. 1995.

    Article  Google Scholar 

  47. M. Eyuboglu and S. Qureshi, “Reduced-State sequence estimation for coded modulation on intersymbol interference channels,” IEEE Journal Sel. Areas Commun, vol. 7, pp. 989 - 995, Aug. 1989.

    Article  MATH  Google Scholar 

  48. E. F. Haratsch, A. J. Blanksby, and K. Azadet, “Reduced state sequence estimation with tab-selective decision feedback,” in IEEE Int. Conf. on Commun, vol. 1, 2000, pp. 372 - 376.

    Google Scholar 

  49. H. L. Lou, M. Rupp, R. L. Urbanke, H. Viswanatan, and R. Krishnamoorthy, “Efficient implementation of parallel decision feedback decoders for broadband applications,” in Conf. Record of the 6th IEEE Int. Conf. on Electronics, Circuits and Systems, vol. 3, pp. 1475 - 1478, 1999.

    Google Scholar 

  50. J. Bakkoury, D. Roviras, M. Ghogho, and F. Castanie, “Adaptive MLSE receiver over rapidly fading channels,” Signal Processing, vol. 80, pp. 1347 - 1360, 2000.

    Article  MATH  Google Scholar 

  51. S. Haykin, Blind Deconvolution. Prentice Hall, Englewood Cliffs, N.J., 1994.

    Google Scholar 

  52. H. Liu and G. B. Giannakis, “Deterministic approaches for blind equalization of time-varying channels with antenna arrays,” IEEE Trans. Signal Processing, vol. 46, no. 11, pp. 3003 - 3013, Nov. 1998.

    Article  Google Scholar 

  53. Y. Sato, “A method of self-recovering equalization for multi level amplitude modulation,” IEEE Trans. Commun, vol. COM-23, pp. 679 - 682, June 1975.

    Google Scholar 

  54. D. N. Godard, “Self-recovering equalization and carrier tracking in two-dimensional data communication systems,” IEEE Trans. Commun, vol. COM28, pp. 1867 - 1875, Nov. 1980.

    Google Scholar 

  55. G. J. Foschini, “Equalization without altering or detecting data,” ATandT Tech. Journal, vol. 64, pp. 1885 - 1911, 1985.

    Google Scholar 

  56. M. Rupp and A. H. Sayed, “On the convergence of blind adaptive equalizers for constant modulus signals,” IEEE Trans. Comm, vol. 48, no. 5, pp. 795 - 803, May 2000.

    Article  Google Scholar 

  57. J. Mai and A. H. Sayed, “A feedback approach to the steady-state performance of fractionally spaced blind equalizers,” IEEE Trans. Signal Processing, vol. 48, no. 1, pp. 80 - 91, Jan. 2000.

    Article  Google Scholar 

  58. J. Treichler and C. R. Johnson, Jr., “Blind fractionally spaced equalization of digital cable TV,” in Conf. Record of the 7th IEEE DSP Workshop, 1996, pp. 122 - 130.

    Google Scholar 

  59. O. Shalvi and E. Weinstein, “New criteria for blind deconvolution of nonminimum phase systems (channels),” IEEE Trans. Information Theory, vol. IT-39, pp. 292 - 297, Jan. 1990.

    Google Scholar 

  60. L. Tong, G. Xu, and T. Kailath, “A new approach to blind identification and equalization of multipath channels,” in Conf. Record of the 25th. Asilomar Conf. of Signals, Systems and Computers, 1991.

    Google Scholar 

  61. L. Tong and S. Perreau, “Multichannel blind channel estimation: from subspace to maximum likelihood methods,” Proc. of IEEE, vol. 86, pp. 1951 - 1968, Oct. 1998.

    Article  Google Scholar 

  62. H. Artes and F. Hlawatsch, “Blind equalization of MIMO channels using deterministic precoding,” in Conf. Record of IEEE Intern. Conf. on Acoustics, Speech, and Signal Processing, vol. 4, 2001, pp. 21530 - 2156.

    Google Scholar 

  63. H. Artes, F. Hlawatsch, and G. Matz “Efficient POCS algorithms for deterministic blind equalization of time-varying channels,” in Conf. Record of IEEE Globecom, 2000, pp. 1031 - 1035.

    Google Scholar 

  64. H. Artes and F. Hlawatsch, “Blind multiuser equalization equalization for time-varying channels,” in Conf. Record of third IEEE Signal Processing Workshop SPAWC, 2001, pp. 102 - 105.

    Google Scholar 

  65. J. Laurila, R. Tschofen, and E. Bonek, “Semi-blind space-time estimation of co-channels signals using least squares projections,” in Conf. Record of the 50th IEEE Vehicular Technology, vol. 3, 1999, pp. 1310 - 1315.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Communication and RF Engineering, TU Wien, Gusshausstr. 25/389, A-1040, Vienna, Austria

    Markus Rupp

  2. Integrated Systems Laboratory, ETH Zurich, Gloriastr. 35, CH-8092, Zurich, Switzerland

    Andreas Burg

Authors
  1. Markus Rupp
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andreas Burg
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Bell Labs, Lucent Technologies, 700 Mountain Avenue, 07974-0636, Murray Hill, NJ, USA

    Jacob Benesty  & Yiteng Huang  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Rupp, M., Burg, A. (2003). Algorithms for Adaptive Equalization in Wireless Applications. In: Benesty, J., Huang, Y. (eds) Adaptive Signal Processing. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-11028-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-11028-7_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05507-2

  • Online ISBN: 978-3-662-11028-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation