The problem of low complexity, close to optimal, channel decoding of linear codes with short
to moderate block length is considered. It is shown that deep learning methods can be used …

We consider near maximum-likelihood (ML) decoding of short linear block codes. In
particular, we propose a novel decoding approach based on neural belief propagation …

Designing a practical, low complexity, close to optimal, channel decoder for powerful
algebraic codes with short to moderate block length is an open research problem. Recently …

Neural decoders were shown to outperform classical message passing techniques for short
BCH codes. In this work, we extend these results to much larger families of algebraic block …

SUMMARY A soft–in/soft–out algorithm which estimates the a posteriori probabilities (APP)
for each transmitted bit is investigated. The soft outputs can be used at the next decoding …

The recent development of deep learning methods provides a new approach to optimize the
belief propagation (BP) decoding of linear codes. However, the limitation of existing works is …

Recently, it was shown that if multiplicative weights are assigned to the edges of a Tanner
graph used in belief propagation decoding, it is possible to use deep learning techniques to …

An intuitive shortcut to understanding the maximum a posteriori (MAP) decoder is presented
based on an approximation. This is shown to correspond to a dual-maxima computation …

A novel deep learning method for improving the belief propagation algorithm is proposed.
The method generalizes the standard belief propagation algorithm by assigning weights to …

Iterative decoding of two-dimensional systematic convolutional codes has been termed"
turbo"(de) coding. Using log-likelihood algebra, we show that any decoder can be used …