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A space-time adaptive decision feedback (DF) receiver using recurrent neuralnetworks (RNN) is proposed for joint equalization and interference suppressionin direct-sequence code-division-multiple-access (DS-CDMA) systems equippedwith antenna arrays. The proposed receiver structure employs dynamically drivenRNNs in the feedforward section for equalization and multi-access interferencesuppression and a finite impulse response (FIR) linear filter in the feedbacksection for performing interference cancellation. A data selective gradientalgorithm, based upon the set-membership design framework, is proposed for theestimation of the coefficients of RNN structures and is applied to theestimation of the parameters of the proposed neural receiver structure.Simulation results show that the proposed techniques achieve significantperformance gains over existing schemes.

Adaptive Space-Time Decision Feedback Neural Detectors with Data Selection for High-Data Rate Users in DS-CDMA Systems