Blind Multiuser Detection Based on Kernel Approximation

  • Tao Yang
  • Bo Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


A kernel based multiuser detection (MUD) scheme in code-division multiple-access (CDMA) system is proposed. In this scheme, the support vector (SV) under support vector (SVM) framework is obtained through a kernel sparsity approximation, which regulates the kernel width parameter via a heuristic approach to obtain an approximate equivalent SV. The corresponding SV coefficient is attained through evaluation of generalized eigenvalue problem, which avoids the conventional costly quadratic programming (QP) computation procedure in SVM. Simulation results show that the proposed scheme has almost the same BER as standard SVM and is better than minimum mean square error (MMSE) scheme when sample set is relatively large, meanwhile the proposed scheme have a low computation complexity.


Support Vector Minimum Mean Square Error Kernel Approximation Multiuser Detection Spreading Code 
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  1. 1.
    Rahman, S.A., Saito, M., Okada, M., et al.: An MC-CDMA Signal Equalization and Detection Scheme Based on Support Vector Machines. In: Proceedings of 1st International Symposium on Wireless Communication Systems, pp. 11–15 (2004)Google Scholar
  2. 2.
    Zhou, W., Zhang, L., Jiao, L.: Adaptive Support Vector Machine Multiuser Detection. Chinese Journal of Electronics 31(1), 92–97 (2003)Google Scholar
  3. 3.
    Chen, S., Samingan, A.K., Hanzo, L.: Support Vector Machine Multiuser Receiver for DS-CDMA Signals in Multipath Channels. IEEE Trans. on Neural Networks 12(3), 604–611 (2001)CrossRefGoogle Scholar
  4. 4.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)MATHGoogle Scholar
  5. 5.
    Scholkopf, B., Knirsch, P., Smola, A., et al.: Fast Approximation of Support Vector Kernel Expansions and An Interpretation of Clustering as Approximation in Feature Spaces. In: Proceedings of DAGM-Symposium, pp. 125–132 (1998)Google Scholar
  6. 6.
    David, M.J., Robert, T., Duin, P.W.: Data Domain Description Using Support Vectors. In: Proceedings of ESANN, Belgium, pp. 251–256 (1999)Google Scholar
  7. 7.
    Scholkopf, B., Mika, S., Burges, C.J.C.: Input Space versus Feature Space in Kernel-based Methods. IEEE Trans. on Neural Networks 10(5), 1000–1017 (1999)CrossRefGoogle Scholar
  8. 8.
    Martinez, D., Bray, A.: Nonlinear Blind Source Separation Using Kernel. IEEE Trans. on Neural Networks 14(1), 228–235 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tao Yang
    • 1
  • Bo Hu
    • 1
  1. 1.Department of Electronics EngineeringFudan UniversityShanghaiChina

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