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Support Vector Machine Multiuser Detector for TD-SCDMA Communication System in Multipath Channels

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Advances in Neural Networks – ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3173))

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Abstract

TD-SCDMA, a part of the third generation mobile communication standards IMT2000 and UMTS is described. A nonlinear multiuser detector based on Support Vector Machine (SVM) is presented and the performance is compared with traditional linear detector. The presented detector can approach optimum multiuser detector than the other linear detectors by using the Midamble of TD-SCDMA. The results of simulation show the feasibility and validity of this detector in multipath channel.

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References

  1. Verdu, S.: Multiuser Detection. Cambridge Univ. Press, Cambridge (1998)

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  2. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)

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  3. Chen, S., Samingan, A.K., Hanzo, L.: Support vector machine multiuser receiver for DSCDMA signals in multipath channels. IEEE Trans. on Neural Networks 12, 604–611 (1999)

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Author information

Authors and Affiliations

  1. Lab. for Intelligent Information Processing, Xidian University, 710071, Xi’an, China

    Yonggang Wang & Licheng Jiao

  2. Air Force Engineering University, 710068, Xi’an, China

    Yonggang Wang & Dongfang Zhao

Authors
  1. Yonggang Wang
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  2. Licheng Jiao
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  3. Dongfang Zhao
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Wang, Y., Jiao, L., Zhao, D. (2004). Support Vector Machine Multiuser Detector for TD-SCDMA Communication System in Multipath Channels. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_99

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  • DOI: https://doi.org/10.1007/978-3-540-28647-9_99

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22841-7

  • Online ISBN: 978-3-540-28647-9

  • eBook Packages: Springer Book Archive

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