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

TL;DR

This paper introduces an adaptive space-time neural decision feedback receiver with data selection for DS-CDMA systems, improving interference suppression and equalization for high-data-rate users.

Contribution

It proposes a novel RNN-based decision feedback receiver with data-selective training, enhancing interference cancellation in DS-CDMA systems.

Findings

Significant performance gains over existing schemes.

Effective joint equalization and interference suppression.

Adaptive RNN structure with data selection improves robustness.

Abstract

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.