Deep Learning Based Symbol Detection With Natural Redundancy for Non-Uniform Memoryless Sources

Abstract

In this letter, we consider improving the performance of symbol detection by utilizing the natural redundancy (NR) that widely exists in the transmission sources. As an initial study, the NR considered in this letter is the redundancy inherent in the data caused by the non-uniform distribution of the memoryless transmission sources. We first theoretically analyze the utilization of NR to improve the performance of symbol detection from the perspective of information theory, and then propose a deep learning (DL) based iterative symbol detection algorithm with the source’s prior distribution unknown. Simulation results demonstrate that the performance of symbol detection can be improved by utilizing NR and our proposed DL-based iterative symbol detection algorithm can obtain the theoretically optimal performance even when the source distribution is blind to the detector.