Skip to main content
SpringerLink
Log in

A Neural Network-Based Blind Multiuser Receiver for DS-CDMA Communication Systems

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The Universal Mobile Telecommunications System (UMTS) which is based on Wideband-Code Division Multiple Access (W-CDMA) techniques is one of the most important broadband wireless communication systems. Adaptive Blind Multiuser Detection was widely considered for mobile receivers. The main drawback of this approach is that it achieves the optimum solution after a certain number of bit times. This paper deals with a new neural network approach in order to reduce the convergence time in different application environments. In particular, a modified Kennedy-Chua neural network, based on the Hopfield model is proposed. The neural network stability was investigated by means of a suitable analytical approach, while the performance of the proposed receiver scheme was derived by means of computer simulations. The numerical results shown in this paper highlight a fast convergence behavior of the proposed scheme, in particular under multipath-fading conditions.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. T. Ojanperä and R. Prasad, “An Overview of Air Interface Multiple Access for IMT-2000/UMTS”, IEEE Communications Magazine, Vol. 36, No. 9, pp. 82–95, 1998.

    Article  Google Scholar 

  2. E. Dahlman, B. Gudmundson, M. Nilsson and J. Sköld, “UMTS/IMT-2000 Based on Wideband CDMA”, IEEE Communications Magazine, Vol. 36, No. 9, pp. 70–80, 1998.

    Article  Google Scholar 

  3. M. Honig, U. Madhow and S. Verdù, “Blind Adaptive Multiuser Detection”, IEEE Transactions on Information Theory, Vol. 41, No. 4, pp. 944–960, 1995.

    Article  MATH  Google Scholar 

  4. U. Mitra and H.V. Poor, “Adaptive Receiver Algorithms for Near-Far Resistance CDMA”, IEEE Transactions on Communications, Vol. 43, No. 2 – part 3, pp. 1713–1724, 1995.

    Article  MATH  Google Scholar 

  5. G.I. Kechriotis and E.S. Manolakos, “Hopfield Neural Network Implementation of the Optimal CDMA Multiuser Detector”, IEEE Transactions on Neural Networks, Vol. 7, No. 1, pp. 131–141, 1996.

    Article  Google Scholar 

  6. K. Das and S.D. Morgera, “Adaptive Interference Cancellation for DS-CDMA Systems Using Neural Network Techniques”, IEEE Journal on Selected Areas in Communications, Vol. 16, No. 9, pp. 1774–1784, 1998.

    Article  Google Scholar 

  7. S.H. Yoon and S.S. Rao, “Annealed Neural Network Based Multiuser Detector in Code Division Multiple Access Communications”, IEE Proceedings – Communications, Vol. 147, No. 1, pp. 57–62, 2000.

    Article  Google Scholar 

  8. A. Hottinen, “Self-Organizing Multiuser Detection”, in Proc. of IEEE ISSTA ‘94, vol. 1, Oulu, Finland, July 1994, pp. 152–156.

    Google Scholar 

  9. J. Joutsensalo and P. Pajunen, “Blind Symbol Learning Algorithms for CDMA Systems”, in Proc. of the 1998 IEEE International Joint Conference on Neural Networks, Vol. 1, Anchorage, AK, U.S.A., May 1998, pp. 152–155.

    Google Scholar 

  10. N. Wang, W.-P. Zhu and B. Zheng, “Blind Multiuser Detection for DS-CDMA Systems: A Neural Network Approach”, in Proc. of ISCAS ‘99, Vol. 5, Orlando, FL, U.S.A., May 1999, pp. 603–606.

    MATH  Google Scholar 

  11. J.J. Hopfield, “Neurons with Graded Response Have Collective Computational Properties Like Those of Two-State Neurons”, Proceedings of the National Academy of Sciences, Vol. 81, pp. 3088–3092, 1984.

    Article  Google Scholar 

  12. G. Jeney, J. Levendovszky and L. Kovacs, “Blind Adaptive Stochastic Neural Network for Multiuser Detection”, in Proc. of VTC 2001 Spring, Vol. 2, Rhodes, Greece, May 2001, pp. 1868–1872.

    Google Scholar 

  13. M.P. Kennedy and L.O. Chua, “Neural Networks for Nonlinear Programming”, IEEE Transactions on Circuits and Systems, Vol. 35, No. 5, pp. 554–562, 1988.

    Article  MathSciNet  Google Scholar 

  14. R. Fantacci, F. Guidi, F. Rastelli, D. Tarchi and P. Tortoli, “Dsp Implementation of a Neural Network Based Blind Multiuser Receiver for DS-CDMA Communication Systems”, in Proc. of DSP2002, Vol. 2, Santorini, Greece, July 2002, pp. 823–826.

    Google Scholar 

  15. J.G. Proakis, Digital Communications, 3rd edn, McGraw Hill: Singapore, 1995.

    Google Scholar 

  16. S. Verdù, Multiuser Detection, Cambridge University Press: New York, U.S.A, 1998.

    Google Scholar 

  17. S. Verdù, “Minimum Probability of Error for Asynchronous Gaussian Multiple-Access Channels”, IEEE Transactions on Information Theory, Vol. 32, No. 1, pp. 85–96, 1986.

    Article  MATH  Google Scholar 

  18. U. Madhow and M.L. Honig, “MMSE Interference Suppression for Direct-Sequence Spread-Spectrum CDMA”, IEEE Transactions on Communications, Vol. 42, No. 12, pp. 3178–3188, 1994.

    Article  Google Scholar 

  19. L. Györfi, “Adaptive Linear Procedures under General Conditions”, IEEE Transactions on Information Theory, Vol. 30, No. 2, pp. 262–267, 1984.

    Article  Google Scholar 

  20. J.M. Zurada, Introduction to Artificial Neural Systems, PWS Publishing Company: Boston, U.S.A., 1995.

    Google Scholar 

  21. D.W. Tank and J.J. Hopfield, “Simple ‘Neural’ Optimization Networks: An A/D Converter, Signal Decision Circuit, and a Linear Programming Circuit”, IEEE Transactions on Circuits and Systems, Vol. 33, No. 7, pp. 533–541, 1986.

    Article  Google Scholar 

  22. L.O. Chua and G.-N. Lin, “Nonlinear Programming without Computation”, IEEE Transactions on Circuits and Systems, Vol. 31, No. 2, pp. 182–188, 1984.

    Article  MathSciNet  Google Scholar 

  23. M. Forti and A. Tesi, “New Conditions for Global Stability of Neural Networks with Application to Linear and Quadratic Programming Problems”, IEEE Transactions on Circuits and Systems – Part I: Fundamental Theory and Applications, Vol. 42, No. 7, pp. 354–366, 1995.

    Article  MATH  MathSciNet  Google Scholar 

  24. R. Fantacci, M. Forti, M. Marini, D. Tarchi and G. Vannuccini, “A Neural Network Based Blind Multiuser Receiver for DS-CDMA Communication Systems”, Dipartimento di Elettronica e Telecomunicazioni, Università di Firenze, Firenze, Italy, Internal Report DET-07-01, 2001.

  25. ”Radio Transmission and Reception”, GSM, 07 2002, 3GPP Technical Specification, ver. 8.13.0. [Online]. Available: http://www.3gpp.org

  26. M.W. Hirsch, “Convergent Activation Dynamics in Continuous Time Networks”, Neural Networks, Vol. 2, No. 5, pp. 331–349, 1989.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Elettronica e Telecomunicazioni, Università di Firenze, Via S. Marta, 3, 50139, Firenze, Italy

    Romano Fantacci, Leonardo Mancini, Mauro Marini & Daniele Tarchi

Authors
  1. Romano Fantacci
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leonardo Mancini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mauro Marini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniele Tarchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fantacci, R., Mancini, L., Marini, M. et al. A Neural Network-Based Blind Multiuser Receiver for DS-CDMA Communication Systems. Wireless Personal Communications 27, 195–213 (2003). https://doi.org/10.1023/B:WIRE.0000010149.52740.87

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/B:WIRE.0000010149.52740.87

Search

Navigation