Open Access

Performance Analysis of Blind Subspace-Based Signature Estimation Algorithms for DS-CDMA Systems with Unknown Correlated Noise

EURASIP Journal on Advances in Signal Processing20062007:083863

Received: 3 October 2005

Accepted: 1 April 2006

Published: 18 September 2006


We analyze the performance of two popular blind subspace-based signature waveform estimation techniques proposed by Wang and Poor and Buzzi and Poor for direct-sequence code division multiple-access (DS-CDMA) systems with unknown correlated noise. Using the first-order perturbation theory, analytical expressions for the mean-square error (MSE) of these algorithms are derived. We also obtain simple high SNR approximations of the MSE expressions which explicitly clarify how the performance of these techniques depends on the environmental parameters and how it is related to that of the conventional techniques that are based on the standard white noise assumption. Numerical examples further verify the consistency of the obtained analytical results with simulation results.


White NoiseEstimation AlgorithmQuantum InformationEstimation TechniqueConventional Technique


Authors’ Affiliations

Department of Communication Systems, Darmstadt University of Technology, Darmstadt, Germany


  1. Verdú S: Multiuser Detection. Cambridge University Press, Cambridge, UK; 1998.MATHGoogle Scholar
  2. Honig M, Madhow U, Verdu S: Blind adaptive multiuser detection. IEEE Transactions on Information Theory 1995,41(4):944-960. 10.1109/18.391241View ArticleMATHGoogle Scholar
  3. Wang X, Poor HV: Blind multiuser detection: a subspace approach. IEEE Transactions on Information Theory 1998,44(2):677-690. 10.1109/18.661512MathSciNetView ArticleMATHGoogle Scholar
  4. Xu Z, Liu P, Wang X: Blind multiuser detection: from MOE to subspace methods. IEEE Transactions on Signal Processing 2004,52(2):510-524. 10.1109/TSP.2003.821111MathSciNetView ArticleGoogle Scholar
  5. Zarifi K, Shahbazpanahi S, Gershman AB, Luo Z-Q: Robust blind multiuser detection based on the worst-case performance optimization of the MMSE receiver. IEEE Transactions on Signal Processing 2005,53(1):295-305.MathSciNetView ArticleGoogle Scholar
  6. Madhow U, Honig ML: MMSE interference suppression for direct-sequence spread-spectrum CDMA. IEEE Transactions on Communications 1994,42(12):3178-3188. 10.1109/26.339839View ArticleGoogle Scholar
  7. Mitra U, Poor HV: Adaptive receiver algorithms for near-far resistant CDMA. IEEE Transactions on Communications 1995,43(2–4, part 3):1713-1724.View ArticleMATHGoogle Scholar
  8. Bensley SE, Aazhang B: Subspace-based channel estimation for code division multiple access communication systems. IEEE Transactions on Communications 1996,44(8):1009-1020. 10.1109/26.535441View ArticleMATHGoogle Scholar
  9. Liu H, Xu G: Subspace method for signature waveform estimation in synchronous CDMA systems. IEEE Transactions on Communications 1996,44(10):1346-1354. 10.1109/26.539776View ArticleGoogle Scholar
  10. Torlak M, Xu G: Blind multiuser channel estimation in asynchronous CDMA systems. IEEE Transactions on Signal Processing 1997,45(1):137-147. 10.1109/78.552212View ArticleGoogle Scholar
  11. Wang X, Poor HV: Blind equalization and multiuser detection in dispersive CDMA channels. IEEE Transactions on Communications 1998,46(1):91-103. 10.1109/26.655407View ArticleGoogle Scholar
  12. Xu Z, Tsatsanis MK: Blind adaptive algorithms for minimum variance CDMA receivers. IEEE Transactions on Communications 2001,49(1):180-194. 10.1109/26.898261View ArticleMATHGoogle Scholar
  13. Li Q, Georghiades CN, Wang X: Blind multiuser detection in uplink CDMA with multipath fading: a sequential EM approach. IEEE Transactions on Communications 2004,52(1):71-81. 10.1109/TCOMM.2003.822172View ArticleGoogle Scholar
  14. Buzzi S, Lops M, Poor HV: Code-aided interference suppression for DS/CDMA overlay systems. Proceedings of the IEEE 2002,90(3):394-435. 10.1109/5.993406View ArticleGoogle Scholar
  15. Wang X, Poor HV: Blind joint equalization and multiuser detection for DS-CDMA in unknown correlated noise. IEEE Transactions on Circuits and Systems for Video Technology II 1999,46(7):886-895.Google Scholar
  16. Buzzi S, Poor HV: A single-antenna blind receiver for multiuser detection in unknown correlated noise. IEEE Transactions on Vehicular Technology 2002,51(1):209-215. 10.1109/25.992081View ArticleGoogle Scholar
  17. Yuen N, Friedlander B: Asymptotic performance analysis for signature waveform estimation in synchronous CDMA systems. IEEE Transactions on Signal Processing 1998,46(6):1753-1757. 10.1109/78.678517View ArticleGoogle Scholar
  18. Xu Z: On the second-order statistics of the weighted sample covariance matrix. IEEE Transactions on Signal Processing 2003,51(2):527-534. 10.1109/TSP.2002.807004MathSciNetView ArticleGoogle Scholar
  19. Xu Z: Effects of imperfect blind channel estimation on performance of linear CDMA receivers. IEEE Transactions on Signal Processing 2004,52(10, part 1):2873-2884. 10.1109/TSP.2004.834408View ArticleGoogle Scholar
  20. Zarifi K, Gershman AB: Performance analysis of subspace-based signature waveform estimation algorithms for DS-CDMA systems with unknown correlated noise. Proceedings of 6th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC '05), June 2005, New York, NY, USA 600-604.Google Scholar
  21. Høst-Madsen A, Wang X, Bahng S: Asymptotic analysis of blind multiuser detection with blind channel estimation. IEEE Transactions on Signal Processing 2004,52(6):1722-1738. 10.1109/TSP.2004.827158View ArticleGoogle Scholar
  22. Honig M, Tsatsanis MK: Adaptive techniques for multiuser CDMA receivers. IEEE Signal Processing Magazine 2000,17(3):49-61. 10.1109/79.841725View ArticleGoogle Scholar
  23. Parkvall S: Variability of user performance in cellular DS-CDMA-long versus short spreading sequences. IEEE Transactions on Communications 2000,48(7):1178-1187. 10.1109/26.855525View ArticleGoogle Scholar
  24. Coope ID, Renaud PF: Trace inequalities with applications to orthogonal regression and matrix nearness problems. In Report UCDMS2000/17. Department of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand; November 2000. Scholar
  25. Horn RA, Johnson CR: Matrix Analysis. Cambridge University Press, Cambridge, UK; 1999.Google Scholar
  26. Li F, Liu H, Vaccaro RJ: Performance analysis for DOA estimation algorithms: unification, simplification, and observations. IEEE Transactions on Aerospace and Electronic Systems 1993,29(4):1170-1184. 10.1109/7.259520View ArticleGoogle Scholar
  27. Xu Z: Perturbation analysis for subspace decomposition with applications in subspace-based algorithms. IEEE Transactions on Signal Processing 2002,50(11):2820-2830. 10.1109/TSP.2002.804084View ArticleGoogle Scholar
  28. Brillinger DR: Time Series: Data Analysis and Theory, Classics in Applied Mathematics. Volume 36. SIAM, Philadelphia, Pa, USA; 2001.View ArticleGoogle Scholar


© K. Zarifi and A. B. Gershman 2007

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.