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Robust Multiuser Detection Using Artificial Neural Networks

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Soft Computing in Communications

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 136))

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Abstract

Abstract This chapter surveys the applications of artificial neural networks (ANNs) for multiuser detection in direct-sequence code-division multiple-access systems. We aim to stimulate more research on neural network techniques for robust multiuser detection in non-Gaussian channels. Here, we present the M-estimation technique for robust decoffelating detection and provide a summary of existing algorithms. We reveal the powerful features of ANNs by focusing on a special recurrent neural network structure for implementing the robust decorrelating detector and highlight its computational saving. Extension of this technique to multicarrier CDMA systems with turbo decoding in Rayleigh fading, impulsive noise channels is then investigated.

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Author information

Author notes

  1. B. S. Sharif

    Present address: Etisalat College of Engineering, Emirates Telecommunications Corporation, Sharjah, UAE

  2. T. C. Chuah

    Present address: Faculty of Engineering, Multimedia University, Jalan Multimedia, 63100, Cyberjaya, Selangor, Malaysia

Authors and Affiliations

  1. Department of Electrical, Electronic and Computer Engineering, University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 7RU, UK

    B. S. Sharif, T. C. Chuah & O. R. Hinton

Authors
  1. B. S. Sharif
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  2. T. C. Chuah
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  3. O. R. Hinton
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© 2004 Springer-Verlag Berlin Heidelberg

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Sharif, B.S., Chuah, T.C., Hinton, O.R. (2004). Robust Multiuser Detection Using Artificial Neural Networks. In: Soft Computing in Communications. Studies in Fuzziness and Soft Computing, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45090-0_6

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  • DOI: https://doi.org/10.1007/978-3-540-45090-0_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53623-6

  • Online ISBN: 978-3-540-45090-0

  • eBook Packages: Springer Book Archive

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