Efficient Variant of Noncircular Complex FastICA Algorithm for the Blind Source Separation of Digital Communication Signals

In this paper, an improved version of the noncircular complex FastICA (nc-FastICA) algorithm is proposed for the separation of digital communication signals. Compared with the original nc-FastICA algorithm, the proposed algorithm is asymptotically efficient for digital communication signals, i.e., its estimation error can be made much smaller by adaptively choosing the approximate optimal nonlinear function. Thus, the proposed algorithm can have a significantly improved performance for the separation of digital communication signals. Simulations confirm the efficiency of the proposed algorithm.

[1]  Shengli Xie,et al.  Mixing Matrix Estimation From Sparse Mixtures With Unknown Number of Sources , 2011, IEEE Transactions on Neural Networks.

[2]  Shan Wang,et al.  Blind Identification of Underdetermined Mixtures with Complex Sources Using the Generalized Generating Function , 2014, Circuits, Systems, and Signal Processing.

[3]  Hualiang Li,et al.  Complex ICA Using Nonlinear Functions , 2008, IEEE Transactions on Signal Processing.

[4]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[5]  Erkki Oja,et al.  Efficient Variant of Algorithm FastICA for Independent Component Analysis Attaining the CramÉr-Rao Lower Bound , 2006, IEEE Transactions on Neural Networks.

[6]  T. Adali,et al.  Adaptable Nonlinearity for Complex Maximization of Nongaussianity and a Fixed-Point Algorithm , 2006, 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing.

[7]  Tianwen Wei,et al.  FastICA Algorithm: Five Criteria for the Optimal Choice of the Nonlinearity Function , 2013, IEEE Transactions on Signal Processing.

[8]  Pierre Comon,et al.  CONFAC Decomposition Approach to Blind Identification of Underdetermined Mixtures Based on Generating Function Derivatives , 2012, IEEE Transactions on Signal Processing.

[9]  Dinh-Tuan Pham,et al.  Fast algorithms for mutual information based independent component analysis , 2004, IEEE Transactions on Signal Processing.

[10]  Yong Xiang,et al.  Time-Frequency Approach to Underdetermined Blind Source Separation , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[11]  E. Oja,et al.  Independent Component Analysis , 2013 .

[12]  Aapo Hyvärinen,et al.  Testing the ICA mixing matrix based on inter-subject or inter-session consistency , 2011, NeuroImage.

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

[14]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.

[15]  Ganesh R. Naik,et al.  Dimensional reduction using Blind source separation for identifying sources , 2011 .

[16]  Pierre Comon,et al.  Blind Identification of Underdetermined Mixtures Based on the Characteristic Function: The Complex Case , 2011, IEEE Transactions on Signal Processing.

[17]  Liping Li,et al.  Independent Component Analysis Based on Fast Proximal Gradient , 2012, Circuits Syst. Signal Process..

[18]  Ying-Ke Lei,et al.  An algorithm for underdetermined mixing matrix estimation , 2013, Neurocomputing.

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

[20]  Klaus Nordhausen,et al.  Deflation-Based FastICA With Adaptive Choices of Nonlinearities , 2014, IEEE Transactions on Signal Processing.

[21]  Ganesh R. Naik,et al.  An Overview of Independent Component Analysis and Its Applications , 2011, Informatica.

[22]  A. Hyvarinen,et al.  One-unit contrast functions for independent component analysis: a statistical analysis , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[23]  Klaus Nordhausen,et al.  Fast equivariant JADE , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[24]  P. Tichavský,et al.  Efficient variant of algorithm fastica for independent component analysis attaining the cramer-RAO lower bound , 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.

[25]  Tülay Adali,et al.  On Extending the Complex FastICA Algorithm to Noncircular Sources , 2008, IEEE Transactions on Signal Processing.

[26]  Jean-Francois Cardoso,et al.  Source separation using higher order moments , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[27]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals , 2000, Int. J. Neural Syst..

[28]  Ganesh R. Naik,et al.  Determining Number of Independent Sources in Undercomplete Mixture , 2009, EURASIP J. Adv. Signal Process..

[29]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[30]  Hualiang Li,et al.  Algorithms for Complex ML ICA and Their Stability Analysis Using Wirtinger Calculus , 2010, IEEE Transactions on Signal Processing.