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 …

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 …

This article is focused on the performance evaluation of linear codes under optimal
maximum-likelihood (ML) decoding. Though the ML decoding algorithm is prohibitively …

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 …

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 …

Quantum stabilizer codes constructed from sparse matrices have good performance and can
be efficiently decoded by belief propagation (BP). A conventional BP decoding algorithm …

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 …

In this paper, we introduce the syndrome loss, an alternative loss function for neural error-
correcting decoders based on a relaxation of the syndrome. The syndrome loss penalizes …

A quantum stabilizer code over corresponds to a classical additive code over that is self-
orthogonal with respect to a symplectic inner product. We study the decoding of quantum …

Reed-Muller (RM) codes exhibit good performance under maximum-likelihood (ML)
decoding due to their highly-symmetric structure. In this paper, we explore the question of …