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 …

In this letter, a model-driven deep learning (DL) decoder for irregular binary low-density
parity-check (LDPC) codes is proposed via the alternating direction method of multipliers …

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 …

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 …

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

For recent years, deep learning has been studied and utilized among decoders in wireless
and optical communication. For low density parity check (LDPC) codes, most works use …

The recent development of deep learning methods demonstrates a new insight to optimize
the decoding of linear codes. In this paper, we survey the typical neural network decoding …

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