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Blind Adaptive Channel Equalization with Performance Analysis

Abstract

A new adaptive multiple-shift correlation (MSC)-based blind channel equalizer (BCE) for multiple FIR channels is proposed. The performance of the MSC-based BCE under channel order mismatches due to small head and tail channel coefficient is investigated. The performance degradation is a function of the optimal output SINR, the optimal output power, and the control vector. This paper also proposes a simple but effective iterative method to improve the performance. Simulation examples are demonstrated to show the effectiveness of the proposed method and the analyses.

References

  1. 1.

    Giannakis GB, Mendel JM: Identification of nonminimum phase systems using higher order statistics. IEEE Transactions on Acoustics, Speech, and Signal Processing 1989, 37(3):360–377. 10.1109/29.21704

    MathSciNet  Article  Google Scholar 

  2. 2.

    Porat B, Friedlander B: Blind equalization of digital communication channels using high-order moments. IEEE Transactions on Signal Processing 1991, 39(2):522–526. 10.1109/78.80846

    Article  Google Scholar 

  3. 3.

    Tong L, Xu G, Kailath T: Blind identification and equalization based on second-order statistics: a time domain approach. IEEE Transactions on Information Theory 1994, 40(2):340–349. 10.1109/18.312157

    Article  Google Scholar 

  4. 4.

    Slock DTM: Blind fractionally-spaced equalization, perfect-reconstruction filter banks and multichannel linear prediction. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '94), April 1994, Adelaide, SA, Australia 4: 585–588.

    Google Scholar 

  5. 5.

    Gurelli M, Nikias CL: EVAM: an eigenvector-based algorithm for multichannel blind deconvolution of input colored signals. IEEE Transactions on Signal Processing 1995, 43(1):134–149. 10.1109/78.365293

    Article  Google Scholar 

  6. 6.

    Moulines E, Duhamel P, Cardoso J-F, Mayrargue S: Subspace methods for the blind identification of multichannel FIR filters. IEEE Transactions on Signal Processing 1995, 43(2):516–525. 10.1109/78.348133

    Article  Google Scholar 

  7. 7.

    Liu H, Xu G: Closed-form blind symbol estimation in digital communications. IEEE Transactions on Signal Processing 1995, 43(11):2714–2723. 10.1109/78.482120

    Article  Google Scholar 

  8. 8.

    Xu G, Liu H, Tong L, Kailath T: A least squares-approach to blind channel identification. IEEE Transactions on Signal Processing 1995, 43(12):2982–2993. 10.1109/78.476442

    Article  Google Scholar 

  9. 9.

    Hua Y: Fast maximum likelihood for blind identification of multiple FIR channels. IEEE Transactions on Signal Processing 1996, 44(3):661–672. 10.1109/78.489039

    Article  Google Scholar 

  10. 10.

    Giannakis GB, Halford SD: Blind fractionally spaced equalization of noisy FIR channels: direct and adaptive solutions. IEEE Transactions on Signal Processing 1997, 45(9):2277–2292. 10.1109/78.622950

    Article  Google Scholar 

  11. 11.

    Giannakis GB, Tepedelenlioglu C: Direct blind equalizers of multiple FIR channels: a deterministic approach. IEEE Transactions on Signal Processing 1999, 47(1):62–74. 10.1109/78.738240

    Article  Google Scholar 

  12. 12.

    Tsatsanis MK, Xu Z: Constrained optimization methods for direct blind equalization. IEEE Journal on Selected Areas in Communications 1999, 17(3):424–433. 10.1109/49.753728

    Article  Google Scholar 

  13. 13.

    Mannerkoski J, Taylor DP: Blind equalization using least-squares lattice prediction. IEEE Transactions on Signal Processing 1999, 47(3):630–640. 10.1109/78.747771

    Article  Google Scholar 

  14. 14.

    Tong LT, Zhao Q: Joint order detection and blind channel estimation by least squares smoothing. IEEE Transactions on Signal Processing 1999, 47(9):2345–2355. 10.1109/78.782179

    MathSciNet  Article  Google Scholar 

  15. 15.

    Zhao Q, Tong LT: Adaptive blind channel estimation by least squares smoothing. IEEE Transactions on Signal Processing 1999, 47(11):3000–3012. 10.1109/78.796435

    Article  Google Scholar 

  16. 16.

    Compton RT Jr.: Adaptive Antennas, Concepts, and Performance. Prentice-Hall, Englewood Cliffs, NJ, USA; 1988.

    Google Scholar 

  17. 17.

    Monzingo R, Miller T: Introduction to Adaptive Arrays. John Wiley & Sons, New York, NY, USA; 1980.

    Google Scholar 

  18. 18.

    Gesbert D, Duhamel P, Mayrargue S: On-line blind multichannel equalization based on mutually referenced filters. IEEE Transactions on Signal Processing 1997, 45(9):2307–2317. 10.1109/78.622953

    Article  Google Scholar 

  19. 19.

    Golub GH, Van Loan CF: Matrix Computations. Johns Hopkins University Press, Baltimore, Md, USA; 1983.

    Google Scholar 

  20. 20.

    Hoel PG, Port SC, Stone CJ: Introduction to Probability Theory. Houghton Mifflin, Boston, Mass, USA; 1971.

    Google Scholar 

  21. 21.

    Baggeroer AB: Confidence intervals for regression (MEM) spectral estimates. IEEE Transactions on Information Theory 1976, 22(5):534–545. 10.1109/TIT.1976.1055612

    MathSciNet  Article  Google Scholar 

  22. 22.

    Liavas AP, Regalia PA, Delmas J-P: Blind channel approximation: effective channel order determination. IEEE Transactions on Signal Processing 1999, 47(12):3336–3344. 10.1109/78.806077

    Article  Google Scholar 

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Correspondence to Shiann-Jeng Yu.

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Yu, S., Ueng, F. Blind Adaptive Channel Equalization with Performance Analysis. EURASIP J. Adv. Signal Process. 2006, 072879 (2006). https://doi.org/10.1155/ASP/2006/72879

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Keywords

  • Information Technology
  • Output Power
  • Performance Analysis
  • Iterative Method
  • Control Vector