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 …

With the applications of deep learning networks booming in physical layer communication,
deep-learning-based channel decoding methods have become a research hotspot …

Compared with traditional low-density parity-check (LDPC) decoding algorithms, the current
model-driven deep learning (DL)-based LDPC decoding algorithms face the disadvantage …

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 …

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 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 …

Inspired by the recent advances in deep learning (DL), this work presents a deep neural
network aided decoding algorithm for binary linear codes. Based on the concept of deep …

The min-sum (MS) algorithm can decode Low-density parity-check (LDPC) codes with low
computational complexity at the cost of slight performance loss. It is an effective way to …

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 …