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) …

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) …

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 …

This letter proposes a novel approach for designing non-surjective (NS) finite alphabet
iterative decoders (FAIDs) for quasi-cyclic low-density parity-check (LDPC) codes, especially …

We investigate the application of machine learning techniques (in particular, deep neural
networks, DNN) to improve the decoding algorithms of short quasi-cyclic low-density …

The success of deep learning has encouraged its applications in decoding error-correcting
codes, eg, LDPC decoding. In this paper, we propose a model-driven deep learning method …

Deep learning based decoding networks have shown significant improvement in decoding
low density parity check (LDPC) codes, but the neural decoders are limited by rate-matching …

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

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 …

Recently, neural networks have improved MinSum message-passing decoders for low-
density parity-check (LDPC) codes by multiplying or adding weights to the messages, where …