.png){kind=icon}
返回
Neural Min-Sum Decoding for Generalized LDPC Codes
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2022.3208834.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this letter, we investigate the min-sum (MS) and neural MS (NMS) decoding algorithms for generalized low-density parity-check (GLDPC) codes. Although the MS decoder is much simpler than the a posteriori probability (APP) decoder commonly used for GLDPC codes, the MS decoder has not been considered mainly due to its inferior decoding performance. However, we show that the performance can be improved by i) employing the NMS decoding algorithm and ii) optimizing the component parity check matrix (PCM). For the four representative short GLDPC codes in the literature, experimental results show that the NMS decoding performance with the optimized component PCM significantly outperforms the MS decoding performance and even outperforms the APP decoding performance for some cases.
Keyword:
Generalized low-density parity-check (GLDPC) code
min-sum (MS) decoding
neural min-sum (NMS) decoding
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
引用论文
分享收藏Generalized LDPC Codes for Ultra Reliable Low Latency Communication in 5G and Beyond.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
分享收藏
查看更多内容