Informed Fixed Scheduling for Faster Convergence of Shuffled Belief-Propagation Decoding

Informed Fixed Scheduling for Faster Convergence of Shuffled Belief-Propagation Decoding
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用于更快收敛混洗置信传播解码的知情固定调度

DOI:
10.1109/lcomm.2016.2617309
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发表时间:
2017
期刊:
IEEE Communications Letters
影响因子:
--
通讯作者:
Han Guojun
Han Guojun
中科院分区:
其他
文献类型:
--
作者:
Aslam Chaudhry Adnan;Guan Yong Liang;Cai Kui;Han Guojun

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为了提高二进制低密度奇偶校验码(LDPC)的混洗置信传播(BP)译码的收敛性,提出了一种新的IFS译码方案。IFS根据代码图中更新的邻居的数量找到变量节点的适当顺序,确保在单次迭代中使用最大数量的最新消息更新。这允许及时地利用最可靠的消息更新,从而导致更快的错误率收敛。仿真结果表明,在二进制输入加性白色高斯噪声信道中,在不影响误码性能的情况下,IFS算法在规则LDPC码和非规则LDPC码下分别将BP译码器的收敛速度提高了20%和45%.
A novel informed fixed scheduling (IFS) scheme for shuffled belief-propagation (BP) decoding of binary low-density parity-check (LDPC) code is introduced to improve the BP decoding convergence. The IFS finds an appropriate order of variable nodes in accordance with the number of updated neighbors in the code graph, ensuring that the maximum number of latest message updates is utilized within a single iteration. This allows the utilization of most reliable message updates in a timely manner, leading to faster error-rate convergence. Simulation results show that the proposed IFS scheme improves the convergence speed of BP decoder by up to 20% for regular LDPC codes and 45% for irregular LDPC codes, without affecting the error-rate performance, at medium-to-high signal-to-noise ratio over binary-input additive white Gaussian noise channel.
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