用于5G超可靠低时延通信的LDPC码截断NMS列表译码算法

张小军; 宋鑫; 高健; 密永豪; 牛凯

doi:10.11999/JEIT250853

用于5G超可靠低时延通信的LDPC码截断NMS列表译码算法

doi: 10.11999/JEIT250853 cstr: 32379.14.JEIT250853

张小军¹,
宋鑫¹,
高健^2, ,,
密永豪¹,
牛凯³

1.
山东科技大学电子信息工程学院青岛 266590
2.
山东科技大学济南校区济南 250031
3.
北京邮电大学泛网无线通信教育部重点实验室北京 100876

基金项目: 国家重点研发计划(2022ZD0119501)，山东省自然科学基金(ZR2022LZH001)

详细信息

作者简介:
张小军：男，副教授，研究方向为数字集成电路设计、深度学习以及信道编码

宋鑫：男，硕士生，研究方向为AI-RAN、深度学习以及信道编码

高健：男，讲师，研究方向为AI-RAN、智能信号处理以及信道编码

密永豪：男，硕士生，研究方向为数字集成电路设计、深度学习以及信道编码

牛凯：男，教授，研究方向为信道编码理论及应用、语义通信、宽带无线通信

通讯作者:
高健　JianGao@sdust.edu.cn

中图分类号: TN911.22
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- 被引次数: 0
出版历程
- 收稿日期: 2025-09-01
- 修回日期: 2026-02-24
- 录用日期: 2026-02-24
- 网络出版日期: 2026-03-06

A Clipped NMS List Decoding Algorithm for LDPC Codes in 5G URLLC

1.
College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
2.
Shandong University of Science and Technology, Jinan Campus, Jinan 250031, China
3.
Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China

Funds: The National Key Research and Development Program of China (2022ZD0119501), Natural Science Foundation of Shandong Province (ZR2022LZH001)

摘要

摘要: 低密度奇偶校验码(Low-Density Parity-Check, LDPC)译码器以接收信号转换得到的对数似然比作为输入，其译码性能对输入高度敏感。在实际无线通信系统中，由于环境的变化，信道容易受到突发干扰，这些干扰会打乱译码器的输入分布从而导致性能损失。为了解决上述问题，该文提出了一种面向5G URLLC场景的LDPC码限幅归一化最小和列表译码算法。该算法通过复用空闲处理单元来生成多条译码路径，并根据输入分布，为每条路径配备独立的限幅器，以此平滑突发干扰，在不增加硬件开销的情况下提升了译码器在干扰信道上的性能。实验表明，相较于单限幅分层NMS算法，该算法实现了0.5 dB左右的增益，并且处理单元的利用率平均提高了69%。
- 5G URLLC /
- 突发干扰 /
- 限幅器 /
- 归一化最小和 /
- 列表译码
Abstract: Objective As one of the coding schemes used in Fifth-generation (5G) wireless communication systems, Low-Density Parity-Check (LDPC) codes can achieve performance close to the Shannon limit through iterative decoding. However, in practical wireless transmission environments, the decoding performance of LDPC codes is vulnerable to burst interference in wireless channels. The Normalized Min-Sum (NMS) decoding algorithm is highly sensitive to the distribution characteristics of the input log-likelihood ratios (LLRs). Burst interference causes the LLRs to deviate from a Gaussian distribution, which degrades decoding performance. Meanwhile, 5G LDPC decoders are often configured with a fixed number of Processing Elements (PEs) based on the maximum lifting size to cover the full code-length range. In ultra-reliable low-latency communications (URLLC) short-code transmission scenarios, the lifting size is much smaller than the maximum lifting size. This condition leaves many PEs idle for long periods and results in low hardware resource utilization. To address these issues, a Clipped Normalized Min-Sum List (CNMSL) decoding algorithm is proposed. By co-designing burst-interference smoothing and idle-resource reuse, the proposed algorithm improves hardware resource utilization and decoding performance. Methods The statistical characteristics of LLRs over Additive White Gaussian Noise (AWGN) and interference channels are first analyzed, and the negative effect of burst interference on decoding performance is qualitatively attributed to the increased proportion of saturated LLRs induced by such interference. The correlation between the optimal clipping threshold and the channel noise variance, burst interference variance, and burst probability is then examined. It is shown that, under limited variations in channel parameters, the optimal clipping threshold converges to a finite interval, referred to as the optimal threshold interval. On this basis, the CNMSL decoding algorithm is proposed. A list-decoding architecture is constructed by reusing idle PEs in 5G LDPC decoders. In this architecture, each decoding path performs independent and synchronous decoding to generate candidate codewords, and the optimal decoding result is selected through a Cyclic Redundancy Check (CRC). Meanwhile, an independent clipper is configured for each path, with parameters determined from the optimal threshold interval, thereby effectively suppressing the adverse effects of burst interference. Results and Discussions Experimental results show that the layered NMS algorithm nearly fails over interference channels when no clipping mechanism is used. With a single clipping threshold, the algorithm operates normally, and its Block Error Rate (BLER) shows a convex downward trend, first decreasing and then increasing as the clipping threshold is reduced. Under various channel conditions for both short and long codes, the single-clipping layered NMS algorithm with a clipping threshold of 3.5 achieves a gain of about 1 dB at $ \mathrm{BLER}=10^{-2} $ compared with a threshold of 10, and the CNMSL algorithm provides a further gain of about 0.5 dB relative to the single-clipping NMS algorithm. In terms of hardware efficiency, when the lifting factor is less than 192, the PE utilization of the CNMSL algorithm is substantially higher than that of the layered NMS algorithm, and the improvement becomes more pronounced as the lifting factor decreases. On average, PE utilization is increased by 69% relative to the layered NMS algorithm. Conclusions A CNMSL decoding algorithm is proposed to improve the error-correction performance of the conventional layered NMS decoding algorithm over interference channels. By reusing idle PEs for list decoding to generate multiple candidate paths, the proposed algorithm introduces no additional hardware overhead. In addition, an optimal threshold interval is defined to configure the clipper for each decoding path. This strategy limits the proportion of saturated LLRs and makes the input LLRs follow a Gaussian or near-Gaussian distribution. Experimental results show that, compared with the layered NMS decoding algorithm with a single clipper, the proposed CNMSL algorithm achieves a gain of approximately 0.5 dB for both short and long codes. Meanwhile, PE utilization is increased by an average of 69%.
- 5G URLLC /
- Burst interference /
- Clipper /
- Normalized min-sum /
- List decoding

HTML全文

图 1 分层译码示意图

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图 2 均匀量化器示意图

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图 3 码字(1536，864)在加性高斯白噪声信道和突发干扰信道下的LLR分布对比

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图 4 CNMSL译码算法架构

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图 5 各信噪比下不同限幅阈值$ U $的误块率性能

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图 6 (768，288)码字下误块率性能

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图 7 (1536，864)码字下误块率性能

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图 8 分层NMS译码算法与CNMSL译码算法PE利用率

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