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在极化码置信(BP)译码的因子图中,当承载确定信息的节点的对数似然信息计算错误时,可以被检测到。此时,对于因子图中参与该似然信息计算的节点,引入一个修正参数,以修正该节点承载的信息的对数似然信息。修正参数可以由密度进化的高斯近似算法得到。给出了置信译码原理及相应的改进算法,最后给出了复杂度分析和性能仿真。数据结果表明,在牺牲很小的复杂度的条件下,相比原算法,修正算法能够获得0.2 dB左右的比特信噪比增益。
In the factor map of Polarization Code Confidence (BP) decoding, the log-likelihood information of a node bearing deterministic information may be detected when it is calculated incorrectly. At this moment, for the nodes participating in the likelihood information calculation in the factor graph, a modification parameter is introduced to correct the log-likelihood information of the information carried by the node. The correction parameters can be derived from Gaussian approximation with density evolution. The principle of confidence decoding and the corresponding improved algorithm are given. Finally, the complexity analysis and performance simulation are given. The results show that the proposed algorithm achieves a bit SNR gain of about 0.2 dB compared to the original algorithm at the expense of small complexity.