Decoder diversity is a powerful error correction framework in which a collection of decoders
collaboratively correct a set of error patterns otherwise uncorrectable by any individual …

In this paper, we introduce a method of using quantized neural networks (QNN) to design
finite alphabet message passing decoders (FAID) for Low-Density Parity Check (LDPC) …

Low-density parity-check (LDPC) codes have been successfully commercialized in
communication systems due to their strong error correction capabilities and simple decoding …

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) …

Recently, we introduced a new class of finite alphabet iterative decoders (FAIDs) for low-
density parity-check (LDPC) codes. These decoders are capable of surpassing belief …

This article deals with the decoding of short block length Low Density Parity Check (LDPC)
codes. It has already been demonstrated that Belief Propagation (BP) can be adjusted to the …

This paper investigates decoder diversity architectures for short low-density parity-check
(LDPC) codes, based on recurrent neural network (RNN) models of the belief-propagation …

A deep learning algorithm for improving the performance of the Sum-Product Algorithm
(SPA) based decoders is investigated. The proposed Neural Network Decoders (NND)[22] …

In this paper, we discuss the perspectives of utilizing deep neural networks (DNN) to decode
Low-Density Parity Check (LDPC) codes. The main idea is to build a neural network to learn …

In this letter we propose a novel framework for designing decoders, for Low-Density Parity
Check (LDPC) codes, that surpasses the frame error rate performance of Belief-Propagation …