We present a novel framework for applying deep neural networks (DNN) to soft decoding of
linear codes at arbitrary block lengths. Unlike other approaches, our framework allows …

Error correction code is a major part of the physical communication layer, ensuring the
reliable transfer of data over noisy channels. Recently, neural decoders were shown to …

Neural networks can efficiently encode the probability distribution of errors in an error
correcting code. Moreover, these distributions can be conditioned on the syndromes of the …

Coding theory is a central discipline underpinning wireline and wireless modems that are
the workhorses of the information age. Progress in coding theory is largely driven by …

There have been significant research activities in recent years to automate the design of
channel encoders and decoders via deep learning. Due the dimensionality challenge in …

Constrained sequence (CS) codes, including fixed-length CS codes and variable-length CS
codes, have been widely used in modern wireless communication and data storage …

In this paper, we propose a new approach to design finite alphabet iterative decoders
(FAIDs) for Low-Density Parity Check (LDPC) codes over binary symmetric channel (BSC) …

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 …

Designing codes that combat the noise in a communication medium has remained a
significant area of research in information theory as well as wireless communications …

We consider near maximum-likelihood (ML) decoding of short linear block codes based on
neural belief propagation (BP) decoding recently introduced by Nachmani et al.. While this …