GRADE-AO: Towards Near-Optimal Spatially-Coupled Codes With High Memories
GRADE-AO: Towards Near-Optimal Spatially-Coupled Codes With High Memories
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GRADE-AO：迈向具有高内存的近乎最优空间耦合代码
DOI：
10.1109/isit45174.2021.9517931
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Dolecek, Lara
中科院分区：
文献类型：
--
作者：
Yang, Siyi;Hareedy, Ahmed;Venkatasubramanian, Shyam;Calderbank, Robert;Dolecek, Lara
空间耦合(SC)码以其阈值饱和现象和低延迟窗口译码算法而闻名，是流媒体应用和数据存储系统的理想选择。SC码是通过对基本块码进行分割，然后以卷积方式重新排列和级联分割的分量来构造的。分割分量的数量决定了SC码的“记忆性”。虽然采用更高的存储空间会导致改进的SC码性能，但众所周知，获得具有更高存储空间的最佳SC码是困难的。本文研究了SC码的性能与分块矩阵密度分布的关系。我们提出了一个通过梯度下降获得(局部)最优密度分布的概率框架。我们从服从所得分布的随机分块矩阵出发，对循环特性进行低复杂度的优化算法，以构造高存储容量、高性能的准循环SC码。仿真结果表明，通过该方法得到的码的性能明显优于相同约束长度的SC码和均匀分割的码。
Spatially-coupled (SC) codes, known for their threshold saturation phenomenon and low-latency windowed decoding algorithms, are ideal for streaming applications and data storage systems. SC codes are constructed by partitioning an underlying block code, followed by rearranging and concatenating the partitioned components in a “convolutional” manner. The number of partitioned components determines the “memory” of SC codes. While adopting higher memories results in improved SC code performance, obtaining optimal SC codes with high memory is known to be hard. In this paper, we investigate the relation between the performance of SC codes and the density distribution of partitioning matrices. We propose a probabilistic framework that obtains (locally) optimal density distributions via gradient descent. Starting from random partitioning matrices abiding by the obtained distribution, we perform low complexity optimization algorithms over the cycle properties to construct high memory, high performance quasi-cyclic SC codes. Simulation results show that codes obtained through our proposed method notably outperform state-of-the-art SC codes with the same constraint length and codes with uniform partitioning.
影响因子：
2.5
作者：
Ahmed Hareedy;R. Wu;L. Dolecek
通讯作者：
Ahmed Hareedy;R. Wu;L. Dolecek