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Neural Self-Corrected Min-Sum Decoder for NR LDPC Codes
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DOI：10.1109/LCOMM.2024.3404110.png)
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摘要
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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 changing channel requires low latency and minimal resource utilization. Unfortunately, typical neural decoders for LDPC codes use large and deep neural networks proportional to the code length, leading to latency and resource problems. In the network of NSCMS similar to network model pruning, certain nodes meeting the self correction condition are deleted and excluded from learning. This reduces the computational complexity of learning, compared to conventional networks. Thus, the NSCMS decoder has high practicality in real-time training using machine learning. Furthermore, self-correction allows for more reliable message-based learning and significantly improves performance. Simulation results demonstrate that NSCMS decoders exhibit lower error rates than previously proposed min-sum decoders.
Keyword:
LDPC codes
iterative decoding
min-sum decoding
self-correction
deep learning
LDPC codes
iterative decoding
min-sum decoding
self-correction
deep learning
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
引用论文
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Reduced complexity iterative decoding of low-density parity check codes based on belief propagation基于置信传播的低密度奇偶校验码迭代译码复杂度降低
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