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Adaptive Decision Feedback Multiuser Detectors with Recurrent Neural Networks for DS-CDMA in Fading Channels

  • Rodrigo C. de Lamare
  • Raimundo Sampaio-Neto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3124)

Abstract

In this work we propose adaptive decision feedback (DF) multiuser detectors (MUDs) for DS-CDMA systems using recurrent neural networks (RNN). A DF CDMA receiver structure is presented with dynamically driven RNNs in the feedforward section and finite impulse response (FIR) linear filters in the feedback section for performing interference cancellation. A stochastic gradient (SG) algorithm is developed for estimation of the parameters of the proposed receiver structure. A comparative analysis of adaptive minimum mean squared error (MMSE) receivers operating with SG algorithms is carried out for linear and DF receivers with FIR filters and neural receiver structures with and without DF. Simulation experiments including fading channels show that the DF neural MUD outperforms DF MUDs with linear FIR filters, linear receivers and the neural receiver without interference cancellation.

Keywords

Additive White Gaussian Noise Minimum Mean Square Error Recurrent Neural Network Rayleigh Fading Channel Multiuser Detector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Rodrigo C. de Lamare
    • 1
  • Raimundo Sampaio-Neto
    • 1
  1. 1.CETUC/PUC-RIORio de JaneiroBrazil

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