基于变量节点LLR消息加权的改进最小和算法

发布时间：2019-05-05 14:31

【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,sm MS)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sm MS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 d B时,IMS-WVN算法的误码性能比VWMS算法提升0.53 d B,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 performance of the single minimum sum (single-minimum Min-Sum,sm MS) algorithm for low density parity check (LDPC) codes, An improved minimum sum (Improved Min Sum algorithm based on weighted message LLR of variable nodes,IMS-WVN) algorithm based on variable node LLR (Log Likelihood Ratio) message weighting is proposed. Firstly, the estimation parameters of the sub-minimum value determined by the number of iterations are added to the minimum value, and then the sub-minimum value is replaced to enhance the reliability of the sm MS algorithm to verify the node reliability. Then, the output LLR message of the variable node and the LLR message before the iteration are weighted to reduce the oscillation amplitude of the variable node and the average number of decoding iterations. The simulation results show that when the SNR is 3.2dB, the error performance of IMS-WVN algorithm is 0.53dB higher than that of VWMS algorithm. When the bit error rate is 10-5, the average decoding iteration times of IMS-WVN algorithm are reduced by 58% compared with MS algorithm.
【作者单位】：桂林电子科技大学信息与通信学院;
【基金】：广西自然基金项目(2013GXNSFFA019004,2014JJ70068) 广西教育厅重点项目(ZD2014052)
【分类号】：TN911.22

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