Globally convergent deflationary instantaneous blind source separation algorithm for digital communication signals

IEEE Transactions on Signal Processing

Volumn 55, Issue 5 II, 2007, Pages 2182-2192

  Erdogan, Alper Tunga ^a  

^a KOÇ UNIVERSITY   (Turkey)

Author keywords

Adaptive filtering; Blind source separation (BSS); Independent component analysis; Multiple in multiple out (MIMO) blind equalization; Subgradient

Indexed keywords

ADAPTIVE ALGORITHMS; ADAPTIVE FILTERING; BLIND EQUALIZATION; BLIND SOURCE SEPARATION; COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; INDEPENDENT COMPONENT ANALYSIS;

DEFLATIONARY ADAPTIVE ALGORITHM; DIGITAL COMMUNICATION SIGNALS; MULTIPLE-IN MULTIPLE-OUT (MIMO) BLIND EQUALIZATION;

DIGITAL COMMUNICATION SYSTEMS;

EID: 34247884300     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2007.893214     Document Type: Article

References (25)

1. A. T. Erdogan, "A simple geometric blind source separation method for bounded magnitude sources," IEEE Trans. Signal Processing, vol. 54, pp. 438-449, Feb. 2006.
2. C. Papadias, "Globally convergent blind source separation based on a multiuser kurtosis maximization criterion," IEEE Trans. Signal Process., vol. 48, pp. 3508-3519, Dec. 2000.
3. A. Hyvärinen, "Fast and robust fixed-point algorithms for independent component analysis," IEEE Trans. Neural Netw., vol. 10, no. 3, pp. 626-634, 1999.
4. N. Delfosse and P. Loubaton, "Adaptive blind separation of independent sources: A deflation approach," Signal Process., vol. 45, pp. 59-83, Jul. 1995.
5. P. Common, "Independent component analysis - A new concept?," Signal Process., vol. 36, pp. 287-314, Apr. 1994.
6. A. Hyvärinen and E. Oja, "A fast fixed-point algorithm for independent component analysis," Neural Comput., vol. 9, pp. 1483-1492, Oct. 1997.
7. D.-T. Pham, "Mutual information approach to blind separation of stationary sources," IEEE Trans. Signal Process., vol. 48, pp. 1935-1946, Jul. 2002.
8. A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis. New York: Wiley, 2001.
9. D.-T. Pham and P. Garat, "Blind separation of mixture of independent sources through a quasi-maximum likelihood approach," IEEE Trans. Signal Process., vol. 45, pp. 1712-1725, Jul. 1997.
10. A. Benveniste, M. Goursat, and G. Ruget, "Robust identification of a nonminimum phase system: Blind adjustment of a linear equalizer in data communications," IEEE Trans. Autom. Control, vol. 25, pp. 385-399, Jun. 1980.
11. D. Donoho, "On minimum entropy deconvolution," in Applied Time Series Analysis II, D. Findley, Ed., 1981, pp. 565-608.
12. D. N. Godard, "Self-recovering equalization and carrier tracking in two-dimensional data communication systems," IEEE Trans. Commun., vol. 28, pp. 1867-1875, Nov. 1980.
13. D.-T. Pham, "Blind separation of instantaneous mixtures of sources based on order statistics," IEEE Trans. Signal Process., vol. 48, pp. 363-375, Feb. 2000.
14. F. Vrins, M. Verleysen, and C. Jutten, "SWM: A class of convex contrasts for source separation," in Proc. IEEE ICASSP, Philadelphia, PA, Mar. 2005, vol. V, pp. 161-164.
15. A. T. Erdogan, "A blind separation approach for magnitude bounded sources," in Proc. IEEE ICASSP, Philadelphia, PA, Mar. 2005, vol. V, pp. 177-180.
16. D.-T. Pham and F. Vrins, "Local minima of information-theoretic criteria in blind source separation," IEEE Signal Process. Lett., vol. 12, pp. 788-791, Nov. 2005.
17. F. Vrins and M. Verleysen, "On the entropy minimization of a linear mixture of variables for source separation," Signal Process., vol. 85, pp. 1029-1044, May 2005.
18. F. Vrins and M. Verleysen, "Information theoretic versus cumulant-based contrasts for multi-modal source separation," IEEE Signal Process. Lett., vol. 12, pp. 190-193, Mar. 2005.
19. P. Loubaton and P. Regalia, "Blind deconvolution of multivariate signals: A deflation approach," in IEEE Int. Conf. Communications May 1993, vol. 2, pp. 1160-1164.
20. D.P. Bertsekas, Nonlinear Programming. Nashua, NH: Athena Scientific, 1999.
21. U. Brannlund, "On relaxation methods for nonsmooth convex optimization," Ph.D. dissertation, Dept. Mathematics, Royal Institute of Technology, Stockholm, 1993.
22. A. Nedic and D. Bertsekas, "Convergence rate of incremental subgradient algorithms," in Stochastic Optimization: Algorithms and Applications S. Ursayev and P. M. Pardalos, Eds. The Netherlands: Kluwer Academic, 2000.
23. A. T. Erdogan and C. Kizilkale, "Fast and low complexity blind equalization via subgradient projections," IEEE Trans. Signal Process., vol. 53, pp. 2513-2524, Jul. 2005.
24. J. Goffin and K. Kiwiel, "Convergence of a simple subgradient level method," Math. Programming, vol. 85, no. 1, pp. 207-211, 1999.
25. A. T. Erdogan, Performance of bounded magnitude blind source separation algorithm in noise (under preparation).