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Blind Detection of Orthogonal Space-Time Block Coding Based on ICA Schemes

  • Ju Liu
  • Bo Gu
  • Hongji Xu
  • Jianping Qiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)

Abstract

Space-time block coding (STBC) can achieve transmit diversity gain and obtain higher coding gain without sacrifice of bandwidth. But the decoding requires accurate channel state information (CSI), which strongly determines the system performance. Independent component analysis (ICA) technique can be used to detect the transmitted signals without channel estimation. In this paper, we study two schemes based on ICA blind detection by exploiting the orthogonality of orthogonal space-time block coding (OSTBC). What is more, some blind algorithms based on channel estimation are used for performance evaluation. Simulation results for Rayleigh fading channels demonstrate that the two proposed schemes achieve significant bit error rate (BER) performance and indicate the optimal separation algorithms suitable for OSTBC system.

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References

  1. 1.
    Alamouti, S.M.: A Simple Transmit Diversity Technique for Wireless Communications. IEEE Journal on Selec. Areas in Comm. 16, 1451–1458 (1998)CrossRefGoogle Scholar
  2. 2.
    Tarokh, V.: Space-Time Block Codes from Orthogonal Designs. IEEE Trans. Inform. Theory 45, 1456–1467 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Stoica, P., Ganesan, G.: Space-time Block Codes: Trained, Blind and Semi-blind Detection. In: Proc. of IEEE ICASSP, pp. 1609–1612 (2002)Google Scholar
  4. 4.
    Xu, H., Liu, J., Hu, H.: Blind Detection Based Space-time Block Coding with Antenna Subset Selection. In: Proc. the Seventh International Conference on Signal Processing (ICSP 2004), Beijing, China, vol. 2, pp. 1731–1734 (2004)Google Scholar
  5. 5.
    Liu, J., Iserte, A.P., Lagunas, M.A.: Blind Separation of OSTBC Signals Using ICA Neural Networks. In: IEEE International Symposium on Signal Processing and Information Technology, Darmstadt, Germany (2003)Google Scholar
  6. 6.
    Cardoso, J.F., Laheld, B.H.: Equivariant Adaptive Source Separation. IEEE Trans. Signal Proc. 44, 3017–3030 (1996)CrossRefGoogle Scholar
  7. 7.
    Rinas, J., Kammeyer, K.D.: Comparison of Blind Source Separation Methods Based on Iterative Algorithms. In: 5th International ITG Conference on Source and Channel Coding (SCC 2004), Erlangen, Germany (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ju Liu
    • 1
    • 2
  • Bo Gu
    • 1
  • Hongji Xu
    • 1
  • Jianping Qiao
    • 1
  1. 1.School of Information Science and EngineeringShandong UniversityJinanChina
  2. 2.State Key Lab. of Integrated Services NetworksXidian UniversityXi’anChina

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