1 research outputs found
Adaptive Space-Time Decision Feedback Neural Detectors with Data Selection for High-Data Rate Users in DS-CDMA Systems
A space-time adaptive decision feedback (DF) receiver using recurrent neural
networks (RNN) is proposed for joint equalization and interference suppression
in direct-sequence code-division-multiple-access (DS-CDMA) systems equipped
with antenna arrays. The proposed receiver structure employs dynamically driven
RNNs in the feedforward section for equalization and multi-access interference
suppression and a finite impulse response (FIR) linear filter in the feedback
section for performing interference cancellation. A data selective gradient
algorithm, based upon the set-membership design framework, is proposed for the
estimation of the coefficients of RNN structures and is applied to the
estimation of the parameters of the proposed neural receiver structure.
Simulation results show that the proposed techniques achieve significant
performance gains over existing schemes.Comment: 6 figures; IEEE Transactions on Neural Networks, 200