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Multi-user detection using non-parametric Bayesian estimation by feed forward neural networks

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Abstract

This paper is concerned with developing novel encoding techniques for implementing non-parametric neural based detectors for systems using Code Division Multiple Access. These new encoding methods on the one hand can increase the processing speed and reduce the complexity of the Feed Forward Neural Network based detector, on the other. Furthermore, we demonstrate that an asymptotically optimal detection performance can be achieved by the proposed algorithms. Due to the increased processing rate, the new scheme may further improve Spectral Efficiency. Extensive simulations and the corresponding numerical analysis demonstrate that the proposed algorithms yield near optimal performance on real channel models (COST-207).

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Acknowledgments

The support of the Grants TÁMOP-4.2.1.B-11/2/KMR-2011-0002 and TÁMOP-4.2.2./B-10/1-2010-0014 are gratefully acknowledged. One author (Andras Olah) would like to acknowledge the support of the Bolyai Janos Research Scholarship of the Hungarian Academy of Sciences. This work was also supported by the European Union and the European Social Fund through project FuturICT.hu (Grant no.: TAMOP-4.2.2.C-11/1/KONV-2012-0013).

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Authors and Affiliations

  1. Faculty of Information Technology, Pázmány Péter Catholic University, Práter str. 50/a, Budapest, Hungary

    Dávid Tisza, András Oláh & János Levendovszky

  2. Department of Telecommunications, Budapest University of Technology and Economics, Budapest, Hungary

    János Levendovszky

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  1. Dávid Tisza
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  2. András Oláh
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  3. János Levendovszky
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Correspondence to Dávid Tisza.

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Tisza, D., Oláh, A. & Levendovszky, J. Multi-user detection using non-parametric Bayesian estimation by feed forward neural networks. Telecommun Syst 63, 65–75 (2016). https://doi.org/10.1007/s11235-015-9973-0

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