Skip to main content
SpringerLink
Log in

Robust Multiuser Detection Method Based on Neural-net Preprocessing in Impulsive Noise Environment

  • Conference paper
Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

Included in the following conference series:

  • 2173 Accesses

Abstract

This paper models the ambient noise as α stable distribution and proposes a novel robust multiuser detection (MUD) method that involves an adaptive nonlinear preprocessor based on multilayer perceptron neural-network whose action is to suppress the negative effect of impulsive noises on the followed decorrelating decision-feedback (DDF) multiuser detector. Simulation results indicate the proposed new method is robust and offers performance enhancement over traditional technology in impulsive noises.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

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

    MATH  Google Scholar 

  2. Nikias, C.L., Shao, M.: Signal Processing with Alpha-stable Distribution and Application. John Wiley & Sons Inc., New York (1995)

    Google Scholar 

  3. Middleton, D.: Non-Gaussian Models in Signal Processing for Telecommunications: New Methods and Results for Class A and Class B Noise Models. IEEE Trans. on Information Theory 45(4), 1129–1149 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  4. Aazhang, B., Poor, H.: Performance of DS/SSMA Communications in Impulsive Channels-Part I: Linear Correlation Receivers. IEEE Trans. on Communications 35(11), 1179–1188 (1987)

    Article  Google Scholar 

  5. Chuah, T.C., Sharif, B.S., Hinton, O.R.: Robust CDMA Multiuser Detection Using a Neural-Network Approach. IEEE Trans. on Neural Networks 12(6), 1546–1558 (2001)

    Google Scholar 

  6. Chuah, T.C., Sharif, B.S., Hinton, O.R.: Robust Decorrelating Decision-Feed Back Multiuser in Non-Gaussian Channels. IEEE Trans. on Signal Processing 81(8), 1997–2004 (2001)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, Dalian, Liaoning, 116024, China

    Ying Guo & Tianshuang Qiu

Authors
  1. Ying Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tianshuang Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

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

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Guo, Y., Qiu, T. (2006). Robust Multiuser Detection Method Based on Neural-net Preprocessing in Impulsive Noise Environment. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_16

Download citation

  • DOI: https://doi.org/10.1007/11760191_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation