Globally Convergent Deflationary Instantaneous Blind Source Separation Algorithm for Digital Communication Signals

Recently an instantaneous blind source separation (BSS) approach that exploits the bounded magnitude structure of digital communications signals has been introduced. In this paper, we introduce a deflationary adaptive algorithm based on this criterion and provide its convergence analysis. We show that the resulting algorithm is convergent to one of the globally optimal points that correspond to perfect separation. The simulation examples related to the separation of digital communication signals are provided to illustrate the convergence and the performance of the algorithm

[1]  Dinh-Tuan Pham,et al.  Local minima of information-theoretic criteria in blind source separation , 2005, IEEE Signal Processing Letters.

[2]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[3]  Alper T. Erdogan,et al.  A simple geometric blind source separation method for bounded magnitude sources , 2006, IEEE Transactions on Signal Processing.

[4]  Dinh-Tuan Pham,et al.  Blind separation of instantaneous mixture of sources based on order statistics , 2000, IEEE Trans. Signal Process..

[5]  Michel Verleysen,et al.  Information theoretic versus cumulant-based contrasts for multimodal source separation , 2005, IEEE Signal Processing Letters.

[6]  Dinh-Tuan Pham,et al.  Mutual information approach to blind separation of stationary sources , 2002, IEEE Trans. Inf. Theory.

[7]  A. Benveniste,et al.  Robust identification of a nonminimum phase system: Blind adjustment of a linear equalizer in data communications , 1980 .

[8]  Alper T. Erdogan,et al.  A blind separation approach for magnitude bounded sources , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[9]  Michel Verleysen,et al.  On the entropy minimization of a linear mixture of variables for source separation , 2005, Signal Process..

[10]  Michel Verleysen,et al.  SWM : a class of convex contrasts for source separation , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[11]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[12]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[13]  Constantinos B. Papadias,et al.  Globally convergent blind source separation based on a multiuser kurtosis maximization criterion , 2000, IEEE Trans. Signal Process..

[14]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[15]  Nathalie Delfosse,et al.  Adaptive blind separation of independent sources: A deflation approach , 1995, Signal Process..

[16]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[17]  D. Donoho ON MINIMUM ENTROPY DECONVOLUTION , 1981 .

[18]  D. Godard,et al.  Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication Systems , 1980, IEEE Trans. Commun..

[19]  Philippe Garat,et al.  Blind separation of mixture of independent sources through a quasi-maximum likelihood approach , 1997, IEEE Trans. Signal Process..

[20]  P. Loubaton,et al.  Blind deconvolution of multivariate signals: A deflation approach , 1993, Proceedings of ICC '93 - IEEE International Conference on Communications.

[21]  D. Bertsekas,et al.  Convergen e Rate of In remental Subgradient Algorithms , 2000 .

[22]  Alper T. Erdogan,et al.  Fast and low complexity blind equalization via subgradient projections , 2005, IEEE Transactions on Signal Processing.

[23]  Jean-Louis Goffin,et al.  Convergence of a simple subgradient level method , 1999, Math. Program..