Abstract
The complex-valued signal model is useful for several practical applications, yet few algorithms for separating complex linear mixtures exist. This paper develops two algorithms for separating mixtures of independent complex-valued signals in which statistical independence of the real and imaginary components is assumed. The procedures extract sources assuming that the kurtoses of either the real or imaginary components are non-zero. Simulations indicate the efficacy of the methods in performing source separation for wireless communications models.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Cardoso, J.-F., Soloumiac, A.: Blind beamforming for non-Gaussian signals. IEE Proc. F. 140, 362–370 (1993)
Bingham, E., Hyvarinen, A.: A fast fixed-point algorithm for independent component analysis of complex-valued signals. Int. J. Neural Syst. 10(1), 1–8 (2000)
Amari, S., Douglas, S.C., Cichocki, A., Yang, H.H.: Multichannel blind deconvolution using the natural gradient. In: Proc. 1st IEEE Workshop Signal Proc. Adv. Wireless Commun., Paris, France, April 1997, pp. 101–104 (1997)
Calhoun, V.D., Adali, T., Pearlson, G.D., van Zijl, P.C., Pekar, J.J.: Independent component analysis of fMRI data in the complex domain. Magn. Reson. Med. 48, 180–192 (2002)
Eriksson, J., Seppola, A.-M., Koivunen, V.: Complex ICA for circular and noncircular sources. In: Proc. EUSIPCO 2005, Antalya, Turkey (September 2005)
Eriksson, J., Koivunen, V.: Complex-valued ICA using second order statistics. In: Proc. IEEE Workshop Machine Learning Signal Processing, Sao Luis, Brazil, October 2004, pp. 183–191 (2004)
Eriksson, J., Koivunen, V.: Complex random vectors and ICA models: Identifiability, uniqueness, and separability. IEEE Trans. Inform. Theory 52(3) (March 2006) (in press)
Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, New York (2001)
Douglas, S.C.: Fixed-point FastICA algorithms for the blind separation of complexvalued signal mixtures. In: Proc. 39th Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA (October 2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Douglas, S.C., Eriksson, J., Koivunen, V. (2006). Fixed-Point Complex ICA Algorithms for the Blind Separation of Sources Using Their Real or Imaginary Components. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_43
Download citation
DOI: https://doi.org/10.1007/11679363_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
Online ISBN: 978-3-540-32631-1
eBook Packages: Computer ScienceComputer Science (R0)