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

Dávid Tisza (), András Oláh () and János Levendovszky ()
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Dávid Tisza: Pázmány Péter Catholic University
András Oláh: Pázmány Péter Catholic University
János Levendovszky: Pázmány Péter Catholic University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2016, vol. 63, issue 1, No 6, 65-75

Abstract: 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).

Keywords: Feed forward neural network; Multi-user detection; Non-parametric MAP decision; CDMA (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11235-015-9973-0

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