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arXiv 2401.15166cs.ITeess.SPmath.IT
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多维空间耦合码的概率设计

Probabilistic Design of Multi-Dimensional Spatially-Coupled Codes

Middle East Technical University(中东技术大学)

Canberk İrimağzı, Ata Tanrıkulu, Ahmed Hareedy

AI总结:

本文提出基于梯度下降的概率框架设计多维空间耦合码,通过最小化短环期望数量并利用局部最优分布指导优化,显著降低短环数量并减少设计复杂度。

摘要:

由于具有优异的渐近性能和有限长度性能,空间耦合(SC)码是一类日益受到关注的低密度奇偶校验码。多维(MD)SC码通过重定位连接SC码的副本而构建,以减轻各种非均匀性来源并在许多数据存储和数据传输系统中提高性能。随着MD-SC码设计中自由度的增加,由于设计过程复杂性的增长,适当利用这些自由度变得更加困难。在本文中,我们提出了一种基于梯度下降(GD)算法的MD-SC码设计的概率框架,以设计更好的MD码并应对这一挑战。具体而言,我们将码的图表示中我们寻求最小化的短环期望数量,表示为表征MD-SC码设计的概率分布矩阵的条目函数。然后,我们找到一个局部最优的概率分布,该分布作为有限长度算法优化器的起点,该优化器生成最终的MD-SC码。我们提供了理论分析以及算法,并给出了实验结果表明,我们设计的MD码(方便地称为GD-MD码)与现有最先进方法相比,具有显著更低的短环数量。此外,我们的算法在少数迭代中收敛到解,这证实了通过局部最优GD-MD分布限制搜索空间所带来的复杂性降低。

英文摘要:

Because of their excellent asymptotic and finite-length performance, spatially-coupled (SC) codes are a class of low-density parity-check codes that is gaining increasing attention. Multi-dimensional (MD) SC codes are constructed by connecting copies of an SC code via relocations in order to mitigate various sources of non-uniformity and improve performance in many data storage and data transmission systems. As the number of degrees of freedom in the MD-SC code design increases, appropriately exploiting them becomes more difficult because of the complexity growth of the design process. In this paper, we propose a probabilistic framework for the MD-SC code design, which is based on the gradient-descent (GD) algorithm, to design better MD codes and address this challenge. In particular, we express the expected number of short cycles, which we seek to minimize, in the graph representation of the code in terms of entries of a probability-distribution matrix that characterizes the MD-SC code design. We then find a locally-optimal probability distribution, which serves as the starting point of a finite-length algorithmic optimizer that produces the final MD-SC code. We offer the theoretical analysis as well as the algorithms, and we present experimental results demonstrating that our MD codes, conveniently called GD-MD codes, have notably lower short cycle numbers compared with the available state-of-the-art. Moreover, our algorithms converge on solutions in few iterations, which confirms the complexity reduction as a result of limiting the search space via the locally-optimal GD-MD distributions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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