陶志勇, 李艳. 基于变量节点更新改进的自修正最小和算法[J]. 云南大学学报(自然科学版), 2020, 42(2): 252-258. doi: 10.7540/j.ynu.20190521
引用本文: 陶志勇, 李艳. 基于变量节点更新改进的自修正最小和算法[J]. 云南大学学报(自然科学版), 2020, 42(2): 252-258. doi: 10.7540/j.ynu.20190521
TAO Zhi-yong, LI Yan. Modified self-corrected min-sum algorithm based on variable node update[J]. Journal of Yunnan University: Natural Sciences Edition, 2020, 42(2): 252-258. DOI: 10.7540/j.ynu.20190521
Citation: TAO Zhi-yong, LI Yan. Modified self-corrected min-sum algorithm based on variable node update[J]. Journal of Yunnan University: Natural Sciences Edition, 2020, 42(2): 252-258. DOI: 10.7540/j.ynu.20190521

基于变量节点更新改进的自修正最小和算法

Modified self-corrected min-sum algorithm based on variable node update

  • 摘要: 针对最小和(Min-Sum,MS)算法在奇偶校验码上的译码性能较差的问题,提出了一种改进的MS算法. 如果新变量节点消息和先前变量节点消息的符号不同,通过对新变量节点消息和先前变量节点消息动态加权处理修改迭代过程中的变量节点消息,以降低MS过高估计的不利影响. 利用深度学习方法实现的译码器不仅能够抑制MS近似的影响,同时能够抑制码结构中循环的不利影响. 仿真结果表明,与MS算法相比,改进的算法在几乎不增加复杂度的条件下获得了译码性能的显著提高,并且在中短码上的译码性能优于经典的置信度传播(Belief Propagation,BP)算法.

     

    Abstract: To solve the problem of poor performance of min-sum (MS) decoding algorithm on parity check codes, an improved MS algorithm is proposed. If the symbol of the new variable node message is different from the previous variable node message, the new variable node message and the previous variable node message are dynamically weighted to modify the variable node message in the iterative process, so as to reduce the adverse effect of MS overestimation. The decoder implemented by the deep learning method can not only suppress the influence of MS approximation, but also suppress the adverse influence of the loop in the code structure. Simulation results show that, compared with MS algorithm, the improved algorithm achieves significant improvement in decoding performance with almost no increase in complexity, and the decoding performance in short and medium codes is better than the classical belief propagation (BP) algorithm.

     

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