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为了减小低密度奇偶校验(low-density parity-check,LDPC)码的译码算法复杂度,提高译码性能,该文针对致信传播(belief propagation,BP)译码算法及其简化算法的分析,提出了一种基于校验节点度的分类修正最小和译码算法。该算法将最小和译码算法中校验节点输入外信息绝对值的最小值和次小值分类,并根据该节点的度计算与BP算法的偏移量,分别选择不同的阈值和修正因子对外信息进行补偿。仿真结果表明,该算法在高信噪比区域的译码性能高于BP算法,并且计算复杂度大大低于BP算法,是一种适用于各种校验节点度分布,而且是能较好兼顾性能与实现复杂度的译码算法。
In order to reduce the complexity of decoding algorithm of low-density parity-check (LDPC) code and improve the decoding performance, this paper proposes a belief propagation (BP) decoding algorithm and its simplified algorithm This paper proposes a new classification and decoding algorithm based on check node degree. The algorithm classifies the minimum and the minimum of the absolute value of the external information input by the check node in the minimum and decoding algorithms, and calculates the offset from the BP algorithm according to the degree of the node, and selects different thresholds and correction factors separately Information is compensated. The simulation results show that the proposed algorithm has higher decoding performance in high signal-to-noise ratio region than BP algorithm and its computational complexity is much lower than that of BP algorithm. This algorithm is suitable for all kinds of check node degree distributions, Performance and Implementation Complexity Decoding Algorithm.