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A Gradient-Based Optimum Block Adaptation ICA Technique for Interference Suppression in Highly Dynamic Communication Channels

  • Wasfy B. Mikhael
  • Tianyu Yang
Open Access
Research Article
Part of the following topical collections:
  1. Reliable Communications over Rapidly Time-Varying Channels

Abstract

The fast fixed-point independent component analysis (ICA) algorithm has been widely used in various applications because of its fast convergence and superior performance. However, in a highly dynamic environment, real-time adaptation is necessary to track the variations of the mixing matrix. In this scenario, the gradient-based online learning algorithm performs better, but its convergence is slow, and depends on a proper choice of convergence factor. This paper develops a gradient-based optimum block adaptive ICA algorithm (OBA/ICA) that combines the advantages of the two algorithms. Simulation results for telecommunication applications indicate that the resulting performance is superior under time-varying conditions, which is particularly useful in mobile communications.

Keywords

Independent Component Analysis Mobile Communication Fast Convergence Online Learning Dynamic Communication 

References

  1. 1.
    Lee T-W, Lewicki MS, Sejnowski TJ: ICA mixture models for unsupervised classification of non-Gaussian classes and automatic context switching in blind signal separation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000, 22(10):1078–1089. 10.1109/34.879789CrossRefGoogle Scholar
  2. 2.
    Ristaniemi T, Joutsensalo J: Advanced ICA-based receivers for block fading DS-CDMA channels. Signal Processing 2002, 82(3):417–431. 10.1016/S0165-1684(01)00194-3CrossRefGoogle Scholar
  3. 3.
    Castedo L, Escudero C, Dapena A: A blind signal separation method for multiuser communications. IEEE Transactions on Signal Processing 1997, 45(5):1343–1348. 10.1109/78.575706MathSciNetCrossRefGoogle Scholar
  4. 4.
    Malaroiu S, Kiviluoto K, Oja E: Time series prediction with independent component analysis. Proceedings of International Conference on Advanced Investment Technology, January 2000, Gold Coast, AustraliaGoogle Scholar
  5. 5.
    McKeown M, Makeig S, Brown S, et al.: Blind separation of functional magnetic resonance imaging (fMRI) data. Human Brain Mapping 1998, 6(5–6):368–372. 10.1002/(SICI)1097-0193(1998)6:5/6<368::AID-HBM7>3.0.CO;2-ECrossRefGoogle Scholar
  6. 6.
    Isbell CL, Viola P: Restructuring sparse high-dimensional data for effective retrieval. In Advances in Neural Information Processing Systems. Volume 11. MIT Press, Cambridge, Mass, USA; 1999.Google Scholar
  7. 7.
    Hyvärinen A, Oja E: A fast fixed-point algorithm for independent component analysis. Neural Computation 1997, 9(7):1483–1492. 10.1162/neco.1997.9.7.1483CrossRefGoogle Scholar
  8. 8.
    Hyvärinen A: Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 1999, 10(3):626–634. 10.1109/72.761722CrossRefGoogle Scholar
  9. 9.
    Malouche Z, Macchi O: Adaptive unsupervised extraction of one component of a linear mixture with a single neuron. IEEE Transactions on Neural Networks 1998, 9(1):123–138. 10.1109/72.655034CrossRefGoogle Scholar
  10. 10.
    Gaeta M, Lacoume J-L: Source separation without prior knowledge: the maximum likelihood solution. Proceedings of the European Signal Processing Conference (EUSIPCO '90), September 1990, Barcelona, Spain 621–624.Google Scholar
  11. 11.
    Pham D-T: Blind separation of instantaneous mixture of sources via an independent component analysis. IEEE Transactions on Signal Processing 1996, 44(11):2768–2779. 10.1109/78.542183CrossRefGoogle Scholar
  12. 12.
    Comon P: Independent component analysis, a new concept. Signal Processing 1994, 36(3):287–314. 10.1016/0165-1684(94)90029-9CrossRefGoogle Scholar
  13. 13.
    Principe J, Xu D, Fisher JW III: Information-theoretic learning. In Unsupervised Adaptive Filtering. Volume I. Edited by: Haykin S. John Wiley & Sons, New York, NY, USA; 2000:265–319.Google Scholar
  14. 14.
    Comon P, Mourrain B: Decomposition of quantics in sums of powers of linear forms. Signal Processing 1996, 53(2–3):93–107. 10.1016/0165-1684(96)00079-5CrossRefGoogle Scholar
  15. 15.
    Cardoso J-F: High-order contrasts for independent component analysis. Neural Computation 1999, 11(1):157–192. 10.1162/089976699300016863MathSciNetCrossRefGoogle Scholar
  16. 16.
    Yeredor A: Blind source separation via the second characteristic function. Signal Processing 2000, 80(5):897–902. 10.1016/S0165-1684(00)00062-1CrossRefGoogle Scholar
  17. 17.
    Hyvarienen A, Karhunen J, Oja E: Independent Component Analysis. John Wiley & Sons, New York, NY, USA; 2001.CrossRefGoogle Scholar
  18. 18.
    Sutton RS: Adapting bias by gradient descent: an incremental version of delta-bar-delta. Proceedings of the 10th National Conference on Artificial Intelligence, July 1992, San Jose, Calif, USA 171–176.Google Scholar
  19. 19.
    Murata N, Müller K-R, Ziehe A, Amari S: Adaptive on-line learning in changing environments. Advances in Neural Information Processing Systems (NIPS '96), December 1996, Denver, Colo, USA 9: 599–605.Google Scholar
  20. 20.
    Murata N, Kawanabe M, Ziehe A, Müller K, Amari S: On-line learning in changing environments with applications in supervised and unsupervised learning. Neural Networks 2002, 15(4–6):743–760.CrossRefGoogle Scholar
  21. 21.
    Orr GB: Dynamics and algorithms for stochastic search, M.S. thesis. Department of Computer Science and Engineering, Oregon Graduate Institute, Beaverton, Ore, USA; 1995.Google Scholar
  22. 22.
    Bottou L: Online algorithms and stochastic approximations. In Online Learning in Neural Networks. Edited by: Saad D. Cambridge University Press, Cambridge, UK; 1998:9–42.zbMATHGoogle Scholar
  23. 23.
    Orr GB, Leen TK: Using curvature information for fast stochastic search. In Advances in Neural Information Processing Systems. Volume 9. Edited by: Mozer M, Jordan M, Petsche T. MIT Press, Cambridge, Mass, USA; 1997.Google Scholar
  24. 24.
    Ristaniemi T, Joutsensalo J: Advanced ICA-based receivers for DS-CDMA systems. Proceedings of IEEE International Conference on Personal, Indoor, and Mobile Radio, Communications, September 2000, London, UKGoogle Scholar
  25. 25.
    Yang T, Mikhael WB: A general approach for image and co-channel interference suppression in diversity wireless receivers employing ICA. Journal of Circuits, Systems, and Signal Processing 2004, 23(4):317–327.zbMATHGoogle Scholar
  26. 26.
    Kostanic I, Mikhael WB: Blind source separation technique for reduction of co-channel interference. Electronics Letters 2002, 38(20):1210–1211. 10.1049/el:20020817CrossRefGoogle Scholar
  27. 27.
    Leong WY, Holmer J: Implementing ICA in blind multiuser detection. IEEE International Symposium on Communications and Information Technologies, October 2004, Sapporo, Japan 2: 947–952.CrossRefGoogle Scholar
  28. 28.
    Mikhael WB, Wu F: A fast block FIR adaptive digital filtering algorithm with individual adaptation of parameters. IEEE Transactions on Circuits and Systems 1989, 36(1):1–10. 10.1109/31.16558MathSciNetCrossRefGoogle Scholar

Copyright information

© Mikhael and Yang 2006

Authors and Affiliations

  • Wasfy B. Mikhael
    • 1
  • Tianyu Yang
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of Central FloridaOrlandoUSA
  2. 2.Department of Engineering SciencesEmbry-Riddle Aeronautical UniversityDaytona BeachUSA

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