基于变量节点LLR消息加权的改进最小和算法

Improved Min Sum Algorithm Based on Weighted Message  LLR of Variable Nodes

  • 摘要: 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%。

     

    Abstract: In order to improve the bit-error-rate performance of the single-minimum Min-Sum algorithm for decoding Low-density 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-to-check 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.

     

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