Adaptive Space-Time Decision Feedback Neural Detectors with Data Selection for High-Data Rate Users in DS-CDMA Systems
Rodrigo C. de Lamare, Raimundo Sampaio-Neto

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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Adaptive Filtering Techniques · Wireless Communication Networks Research · Blind Source Separation Techniques
