Adaptive Reweighted Freezing Belief Propagation Decoding for Polar Codes

GUO Xiaojin; ZHANG Jingtao

doi:10.11959/j.issn.1000-0801.DXKX260028

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Adaptive Reweighted Freezing Belief Propagation Decoding for Polar Codes

更新时间：2026-06-23

- Adaptive Reweighted Freezing Belief Propagation Decoding for Polar Codes
- Telecommunications Science (2026)
- 作者机构：
  
  重庆邮电大学通信与信息工程学院，重庆 400065
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX260028
  CLC： TN929.5
- Received：09 January 2026，
  
  Revised：2026-03-23，
  
  Accepted：23 June 2026，
- 稿件说明：
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GUO Xiaojin, ZHANG Jingtao. Adaptive Reweighted Freezing Belief Propagation Decoding for Polar Codes[J/OL]. Telecommunications Science, 2026.
DOI：

GUO Xiaojin, ZHANG Jingtao. Adaptive Reweighted Freezing Belief Propagation Decoding for Polar Codes[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260028.

摘要

针对极化码BP译码冗余计算量大、收敛速度慢及译码性能受限的问题，提出一种自适应重加权冻结（ARF）BP译码算法。该算法通过自适应重加权机制调节消息的更新权重，加速译码的收敛速度，并对因子图中已收敛的节点进行冻结判定以削减冗余计算。仿真结果表明，在低信噪比区域ARF-BP算法相比传统BP译码算法可获得约1.1dB的性能增益，相较于G矩阵早停准则的BP译码能降低约64%的平均迭代次数以及可冻结约20%的因子图节点。该算法具有低时延与低功耗优势，适用于5G大规模机器类型通信及超可靠低延迟通信等关键场景。

Abstract

To address the challenges of high computational redundancy

slow convergence speed

and limited decoding performance in Belief Propagation (BP) decoding of polar codes

this paper proposes an Adaptive Reweighted Freezing (ARF) BP decoding algorithm. This algorithm introduces an adaptive reweighting mechanism to regulate the weights of message updates

thereby accelerating the convergence of the decoding process. Furthermore

it implements a freezing judgment strategy for converged nodes within the factor graph to eliminate redundant calculations. Simulation results demonstrate that

in the low Signal-to-Noise Ratio (SNR) region

the ARF-BP algorithm achieves a performance gain of approximately 1.1 dB compared to the traditional BP decoding algorithm. Moreover

compared to BP decoding with the G-matrix early stopping criterion

it reduces the average number of iterations by about 64% and effectively freezes approximately 20% of the factor graph nodes. Featuring low latency and low power consumption

this algorithm is well-suited for critical scenarios such as 5G massive Machine Type Communications and Ultra-Reliable Low-Latency Communications.

关键词

Keywords

references

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