Iterative Error Decimation for Syndrome-Based Neural Network Decoders
Information Theory
2021-08-31 v4 math.IT
Abstract
In this letter, we introduce a new syndrome-based decoder where a deep neural network (DNN) estimates the error pattern from the reliability and syndrome of the received vector. The proposed algorithm works by iteratively selecting the most confident positions to be the error bits of the error pattern, updating the vector received when a new position of the error pattern is selected. Simulation results for the (63,45) and (63,36) BCH codes show that the proposed approach outperforms existing neural network decoders. In addition, the new decoder is flexible in that it can be applied on top of any existing syndrome-based DNN decoder without retraining.
Cite
@article{arxiv.2012.00089,
title = {Iterative Error Decimation for Syndrome-Based Neural Network Decoders},
author = {Jorge Kysnney Santos Kamassury and Danilo Silva},
journal= {arXiv preprint arXiv:2012.00089},
year = {2021}
}
Comments
To appear in Journal of Communication and Information Systems