Adaptive Blind MMSE Equalization for SIMO Channel

  • Ahn, Kyung-Seung (Department of Electronic Engineering, Chonbuk National University) ;
  • Baik, Heung-Ki (Division of Electronics & Information Engineering, Electronics & Information Advanced Technologh Research Center, Chonbuk National University)
  • Published : 2002.08.01

Abstract

Blind equalization of transmission channel is important in communication areas and signal processing applications because it does not need training sequences, nor dose it require a priori channel information. In this paper, an adaptive blind MMSE channel equalization technique based on second-order statistics in investigated. We present an adaptive blind MMSE channel equalization using multichannel linear prediction error method for estimating cross-correlation vector. They can be implemented as RLS or LMS algorithms to recursively update the cross-correlation vector. Once cross-correlation vector is available, it can be used for MMSE channel equalization. Unlike many known subspace methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch. Performance of our algorithms and comparisons with existing algorithms are shown for real measured digital microwave channel.

Keywords

References

  1. J. G. Proakis, Digital Communication, 4th ed.New York: McGraw Hill, 2001
  2. Z. Ding, R. A. Kennedy, B. D. 0. Anderson,and C. R. Johnson, Jr., 'Ill-convergence of Godard blind equalizers in data communicationsystems,' IEEE Trans. Signal Processing, vol.39, no. 9, pp. 1313-1327, Sept. 1991
  3. Y. Li and Z. Ding, 'Global convergence offractionally-spaced Godard (CMA) adaptiveequalizers,' IEEE Trans. Signal Processing, vol.44, no. 4, pp. 818-826, Apr. 1996 https://doi.org/10.1109/78.492535
  4. L. Tong, G. Xu, and T. Kailath, 'Blindidentification and equalization based on Second-order statistics: A time domain approach,' IEEETrans. Inform. Theory, vol. 40, no. 2, pp.340-349, Mar. 1994 https://doi.org/10.1109/18.312157
  5. L. Tong, G. Xu, B. Hassibi, and T. Kailath,'Blind channel identification based on Second-order statistics: A frequency- domainapproach,' IEEE Trans. Inform. Theory, vol. 41,no. 1, pp. 329-334, Jan. 1995 https://doi.org/10.1109/18.370088
  6. E. Moulines, P. Duhamel, J. F. Cardoso, and S.Mayrargue, 'Subspace methods for the blindidentification of multichannel FIR filter,' IEEETrans Signal Processing, vol. 43, no. 2, pp.516-525, Feb. 1995 https://doi.org/10.1109/78.348133
  7. K. Abed-Meraim, E. Moulines, and P. Loubaton, 'Prediction error method for second-order blindidentification,' 1EEE Trans. Signal Processing,vol. 45, no. 3, pp. 694-704, Mar. 1997 https://doi.org/10.1109/78.558487
  8. C. B. Papadias and D. T. M. Slock,'Fractionally spaced equalization of linear polyphase channels and related blind techniquesbased on multichannel linear prediction,' IEEETrans. Signal Processing, vol. 47, no. 3, pp.641-653, Mar. 1999 https://doi.org/10.1109/78.747772
  9. J. Mannerkoski and D. P. Taylor, 'Blindequalization using least-squares lattice Predic-tion,' IEEE Trans. Signal Processing, vol. 47,no. 3, pp. 630-640, Mar. 1999 https://doi.org/10.1109/78.747771
  10. J. Mannerkoski, V. Koivunen, and D. P.Taylor, 'Performance bounds for multistepprediction-based blind equalization,' IEEETrans. Signal Processing, vol. 49, no. 1, pp.84-93, Jan. 2001
  11. D. T. Slock and C. B. Papadias, 'Further results on blind identification and equalization ofmultiple FIR channels,' in Proc. ICASSP, 1995, pp. 1964-1967
  12. J. Shen and Z. Ding, 'Direct blind MMSEchannel equalization based on second orderstatistics,' IEEE Trans. Signal Processg vol.48, no. 4, pp. 1015-1022, Apr. 2000 https://doi.org/10.1109/78.827535
  13. X. Li and H. Fan, 'Direct estimation of blindzero-forcing equalizers based on second-orderstatistics,' IEEE Trans. Signal Processing, vol.48, no. 8, pp. 2211-2218, Aug. 2000 https://doi.org/10.1109/78.852002
  14. S. Haykin, Adaptive Fitter theory, 3id ed.Upper Saddle River, NJ: Prentice-Hall, 1996
  15. G. H. Golub and C. F. Van Loan, Matrix Computations, 3rd ed. Baltimore, MD: JohnsHopkins Univ. Press, 1996
  16. J. Endres, S. D. Halfbrd, C. R. Johnson, and G.B. Giannakis, 'Simulated comparisons of blindequalization algorithms for cold startupapplications,' Int. J. Adaptive Contr. SignalProcess., vol. 12, no. 3, pp. 283-301, May 1998 https://doi.org/10.1002/(SICI)1099-1115(199805)12:3<283::AID-ACS479>3.0.CO;2-E
  17. T. R. Treichler, I. Fijalkow, and C. R. Johnson,'Fractionally spaced equalizers: how long shouldthey be?,' IEEE Signal Processing Mag., vol.13, no. 3, pp. 65-81, May 1996 https://doi.org/10.1109/79.489269
  18. G. B. Giannakis and S. D. Halford, 'Blind fractionally spaced equalization of noisy FIRchannels: direct and adaptive solutions,' IEEETrans. Sigial Processing, vol. 45, no. 9, pp.2277-2292, Sept. 1997 https://doi.org/10.1109/78.622950
  19. B. Yang, 'Projection approximation subspacetracking,' IEEE Trans. SignaI Processing, vol.43, no. 1, pp. 95-107, Jan. 1995 https://doi.org/10.1109/78.365290
  20. P. Common and G. H. Golub, 'Tracking a fewextreme singular values and vectors in signalprocessing,' Proc. IEEE, vol. 78, no. 8, pp.1327-1343, Aug. 1990 https://doi.org/10.1109/5.58320
  21. D. Gesbert and P. Duhamel, 'Robust blindchannel identification and equalization based onmulti-step predictors,' in Proc. ICASSP, 1997,pp. 3621-3624
  22. H. Luo and R. Liu, 'Blind eqoalizeis formultipath channels with best equalizationdelay,' in Proc. ICASSP, 1999, pp. 2511-2513
  23. T. K. Moon and W. C. Stirling, MathematicatMethods and Algorithms for Signal Processing.Upper Saddle River, NJ: Prentice-Hall, 2000
  24. K. S. Ahn, J. P. Cho, and H. K. Baik,'Multistep prediction-based blind equalizationand effcient adaptive implementation,' Journal of the KICS, vol. 26, no. 6B, pp. 776-783, June2001