Neural Layered Min-Sum Decoding for Protograph LDPC Codes
Dexin Zhang, Jincheng Dai, Kailin Tan, Kai Niu, Mingzhe Chen, H. Vincent Poor, Shuguang Cui
Abstract
In this paper, layered min-sum (MS) iterative decoding is formulated as a customized neural network following the sequential scheduling of check node (CN) updates. By virtue of the lifting structure of protograph low-density parity-check (LDPC) codes, identical network parameters are shared among all derived edges originating from the same edge in the protograph, which makes the number of learn- able parameters manageable. The proposed neural layered MS decoder can support arbitrary codelengths consequently. Moreover, an iteration-wise greedy training method is proposed to tune the parameters such that it avoids the vanishing gradient problem and accelerates the decoding convergence.
BibTeX
@inproceedings{icassp2021_neurallayeredmin,
title = {Neural Layered Min-Sum Decoding for Protograph LDPC Codes},
author = {Dexin Zhang and Jincheng Dai and Kailin Tan and Kai Niu and Mingzhe Chen and H. Vincent Poor and Shuguang Cui},
booktitle = {ICASSP 2021},
year = {2021}
}