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 …

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 …

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 work, we propose a neural adjusted min-sum (NAMS) decoder for low-density parity-
check (LDPC) codes. In particular, we improve the traditional normalized min-sum (NMS) …

The trade-off between decoding performance and hardware costs has been a long-standing
challenge in Low-Density Parity Check (LDPC) decoding. Based on model-driven …

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, a neural self-corrected min-sum (NSCMS) decoder is proposed for 5G new
radio (NR) low-density parity-check (LDPC) codes. Training in real-time on a rapidly …

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 …

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

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 …