基于变量节点LLR消息加权的改进最小和算法

摘要

In order to improve the bit-error-rate performance of the single-minimum Min-Sum algorithm for decoding Lowdensity parity check (LDPC) codes,the IMS-WVN (Improved Min Sum algorithm based on weighted message LLR of variable nodes) was proposed in this paper,firstly,determined the estimation parameter of the sub-minimum value accorded to the number of decoding iterations,and added the minimum value to replace the sub-minimum so as to enhance the reliability of the check-node.Secondly,the currently message of variable-to-check node and the message of old variable-tocheck node were weighted to decrease the oscillation of the variable node and decrease the average number of decoding iteration.The simulation results show that the IMS-WVN algorithm had improved 0.53 dB than VWMS algorithm and the 3.2 dB order of error rate,when the error rate was 10-5,The average number of iterations of the IMS-WVN algorithm is 58％ less than that of the MS algorithm.%为了提高低密度奇偶校验(LDPC)码的单最小值最小和(single-minimum Min-Sum,smMS)算法的误码性能,提出了一种基于变量节点LLR(Log Likelihood Ratio)消息加权的改进最小和(Improved Min Sum algorithm based on weighted message LLR of variable nodes,IMS-WVN)算法.首先,将迭代次数所确定的次小值的估值参数与最小值相加后取代次小值,以增强smMS算法校验节点的可靠度.然后,将变量节点输出LLR消息与迭代前LLR消息进行加权处理,降低变量节点的振荡幅度,降低平均译码迭代次数.仿真结果表明,在信噪比为3.2 dB时,IMS-WVN算法的误码性能比VWMS算法提升0.53 dB,当误码率为10-5时,IMS-WVN算法平均译码迭代次数较MS算法减少58％.