Skip to main content

Advertisement

  • Research Article
  • Open Access

Blind Adaptive Channel Equalization with Performance Analysis

  • 1 and
  • 2
EURASIP Journal on Advances in Signal Processing20062006:072879

https://doi.org/10.1155/ASP/2006/72879

  • Received: 4 March 2005
  • Accepted: 26 September 2005
  • Published:

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.

Keywords

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

Authors’ Affiliations

(1)
National Center for High Performance Computing, No. 21 Nan-Ke 3rd Road, Hsin-Shi, Tainan County, 744, Taiwan
(2)
Department of Electrical Engineering, National Chung-Hsing University, 250 Kuo-Kuang Road, Taichung, 402, Taiwan

References

  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. 10.1109/29.21704MathSciNetView ArticleGoogle Scholar
  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. 10.1109/78.80846View ArticleGoogle Scholar
  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. 10.1109/18.312157View ArticleGoogle Scholar
  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. 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. 10.1109/78.365293View ArticleGoogle Scholar
  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. 10.1109/78.348133View ArticleGoogle Scholar
  7. Liu H, Xu G: Closed-form blind symbol estimation in digital communications. IEEE Transactions on Signal Processing 1995, 43(11):2714–2723. 10.1109/78.482120View ArticleGoogle Scholar
  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. 10.1109/78.476442View ArticleGoogle Scholar
  9. Hua Y: Fast maximum likelihood for blind identification of multiple FIR channels. IEEE Transactions on Signal Processing 1996, 44(3):661–672. 10.1109/78.489039View ArticleGoogle Scholar
  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. 10.1109/78.622950View ArticleGoogle Scholar
  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. 10.1109/78.738240View ArticleGoogle Scholar
  12. Tsatsanis MK, Xu Z: Constrained optimization methods for direct blind equalization. IEEE Journal on Selected Areas in Communications 1999, 17(3):424–433. 10.1109/49.753728View ArticleGoogle Scholar
  13. Mannerkoski J, Taylor DP: Blind equalization using least-squares lattice prediction. IEEE Transactions on Signal Processing 1999, 47(3):630–640. 10.1109/78.747771View ArticleGoogle Scholar
  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. 10.1109/78.782179MathSciNetView ArticleGoogle Scholar
  15. Zhao Q, Tong LT: Adaptive blind channel estimation by least squares smoothing. IEEE Transactions on Signal Processing 1999, 47(11):3000–3012. 10.1109/78.796435View ArticleGoogle Scholar
  16. Compton RT Jr.: Adaptive Antennas, Concepts, and Performance. Prentice-Hall, Englewood Cliffs, NJ, USA; 1988.Google Scholar
  17. Monzingo R, Miller T: Introduction to Adaptive Arrays. John Wiley & Sons, New York, NY, USA; 1980.Google Scholar
  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. 10.1109/78.622953View ArticleGoogle Scholar
  19. Golub GH, Van Loan CF: Matrix Computations. Johns Hopkins University Press, Baltimore, Md, USA; 1983.MATHGoogle Scholar
  20. Hoel PG, Port SC, Stone CJ: Introduction to Probability Theory. Houghton Mifflin, Boston, Mass, USA; 1971.MATHGoogle Scholar
  21. Baggeroer AB: Confidence intervals for regression (MEM) spectral estimates. IEEE Transactions on Information Theory 1976, 22(5):534–545. 10.1109/TIT.1976.1055612MathSciNetView ArticleGoogle Scholar
  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. 10.1109/78.806077View ArticleGoogle Scholar

Copyright

© Yu and Ueng 2006

Advertisement