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

ZHANG Xiaojun; SONG Xin; GAO Jian; MI Yonghao; NIU kai

doi:10.11999/JEIT250853

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ZHANG Xiaojun, SONG Xin, GAO Jian, MI Yonghao, NIU kai. A Clipped NMS List Decoding Algorithm of LDPC Codes for 5G URLLC[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250853

Citation:

ZHANG Xiaojun, SONG Xin, GAO Jian, MI Yonghao, NIU kai. A Clipped NMS List Decoding Algorithm of LDPC Codes for 5G URLLC[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250853

ZHANG Xiaojun, SONG Xin, GAO Jian, MI Yonghao, NIU kai. A Clipped NMS List Decoding Algorithm of LDPC Codes for 5G URLLC[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250853

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

doi: 10.11999/JEIT250853 cstr: 32379.14.JEIT250853

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

Accepted Date: 2026-02-24
Rev Recd Date: 2026-02-24

Available Online: 2026-03-06

Abstract

Abstract

Objective As one of the coding schemes in the 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 susceptible to burst interference in wireless channels. The NMS decoding algorithm is highly sensitive to the distribution characteristics of input log-likelihood ratios (LLRs). Burst interference will cause LLRs to deviate from the Gaussian distribution, resulting in degradation in decoding performance. Meanwhile, 5G LDPC decoders are often equipped with a fixed number of processing units (PEs) according to the maximum lifting size to cover the full code length range. In URLLC (Ultra-Reliable Low-Latency Communications) short code transmission scenarios, the lifting size is much smaller than the maximum lifting size, leading to long-term idleness of a large number of processing units and insufficient utilization of hardware resources. To address the above issues, this paper proposes a Clipped Normalized Min-Sum List (CNMSL) decoding algorithm. By co-designing burst interference smoothing and idle resource reuse, it improves hardware resource utilization while enhancing decoding performance. Methods The statistical characteristics of LLRs over AWGN and interference channels are first analyzed, and the negative impact of burst interference on decoding performance is qualitatively illustrated to stem from the increased proportion of saturated LLRs induced by such interference. Next, the correlation between the optimal clipping threshold and channel noise variance, burst interference variance as well as burst probability is verified, which converges to a finite interval, the optimal threshold interval, when channel parameters undergo limited variations. On this basis, the CNMSL decoding algorithm is proposed. This algorithm constructs a list decoding architecture by reusing idle processing units in 5G LDPC decoders, where each decoding path performs independent and synchronous decoding to generate candidate codewords, and the optimal decoding result is screened out via CRC check. Meanwhile, an independent clipper is configured for each path with parameters set according to the optimal threshold interval, thereby effectively suppressing and mitigating the adverse effects of burst interference. Results and Discussions Experimental results show that the layered NMS algorithm almost fails to decode over interference channels without clipping mechanism. With a single clipping threshold, the algorithm works normally, and its BLER exhibits a convex-down trend of first decreasing and then increasing as the clipping threshold reduces. 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 $ BLER={10}^{-2} $ compared with that of 10, and the CNMSL algorithm further yields an additional 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 significantly higher than that of the layered NMS algorithm, with more remarkable improvement as the lifting factor decreases, and the average PE utilization of the CNMSL algorithm is increased by 69% compared with the layered NMS algorithm. Conclusions The CNMSL decoding algorithm is proposed in this paper, aiming to improve the error correction performance of the traditional layered NMS decoding algorithm over interference channels. By reusing idle PEs for list decoding to generate multiple candidate paths, the algorithm incurs no additional hardware overhead. In addition, an optimal threshold interval is defined to configure the clipper for each decoding path, which 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, it increases the PE utilization by an average of 69%.
- 5G URLLC,
- Burst interference,
- Clipper,
- Normalized Min-Sum,
- List decoding.

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References(20)

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