为了进一步缩小最小和（min-sum,MS）算法和置信传播（belief propagation,BP）算法译码性能的差距，提高归一化最小和（normalized min-sum,NMS）算法的译码性能，提出了一种基于残差分层的改进归一化最小和低密度奇偶校验（low density parity check,LDPC）译码算法。首先定量分析MS算法存在的高估问题，利用BP算法和MS算法检验节点LLR消息的比值特性，求其归一化因子。为了降低译码的复杂度，根据最优归一化因子的变化特征，对其采用加权平均处理；为了降低平均迭代次数，加快译码的收敛速度，所提算法利用校验节点信息的残差特性进行分层处理，优先更新残差值较大的一层，在不同迭代之间动态地重新排列层。仿真结果表明，提出的RB_LINMS译码算法，在误比特率为10-5时，相比于传统的NMS算法，其译码性能可获得0.26 dB的增益，平均迭代次数最多能够降低33.20%。因此，在复杂度略有增加的情况下，能够实现更快的收敛速度和出色的译码性能。
In order to further narrow the gap between min-sum (MS) algorithm and belief propagation (BP) algorithm, and to improve the decoding performance of normalized minsum (NMS) algorithm, an improved normalized minimum sum LDPC decoding algorithm based on residual difference layer is proposed. Firstly, the overestimation problem of MS algorithm is quantitatively analyzed. BP algorithm and MS algorithm are used to test the ratio characteristics of node LLR messages, and the corresponding normalization factors are calculated. To reduce decoding complexity, a weighted average processing is adopted according to the variation in the optimal normalization factor. Additionally, to reduce the average number of iterations and accelerate decoding convergence, the proposed algorithm uses the residual characteristics of the check node information to prioritize updates in layers with larger residual values. The layers are dynamically rearranged between iterations. Simulation results show that the proposed RB_LINMS algorithm achieves a performance gain of approximately 0.26 dB in decoding, compared with the traditional NMS algorithm at the bit error rate 10-5, and reducer the average number of iterations by up to 33.20%. Therefore, with a slight increase in complexity, it offers faster convergence and improved decoding performance.
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