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具有低密度奇偶校验预编码的加权BATS码

Weighted BATS Codes with LDPC Precoding.

作者信息

Zhang Wenyue, Zhu Min

机构信息

The State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China.

Science and Technology on Communication Networks Laboratory, Shijiazhuang 050081, China.

出版信息

Entropy (Basel). 2023 Apr 19;25(4):686. doi: 10.3390/e25040686.

Abstract

Batched Sparse (BATS) codes are a type of network coding scheme that use a combination of random linear network coding (RLNC) and fountain coding to enhance the reliability and efficiency of data transmission. In order to achieve unequal error protection for different data, researchers have proposed unequal error protection BATS (UEP-BATS) codes. However, current UEP-BATS codes suffer from high error floors in their decoding performance, which restricts their practical applications. To address this issue, we propose a novel UEP-BATS code scheme that employs a precoding stage prior to the weighted BATS code. The proposed precoding stage utilizes a partially regular low-density parity-check (PR-LDPC) code, which helps to mitigate the high error floors in the weighted BATS code We derive the asymptotic performance of the proposed scheme based on density evolution (DE). Additionally, we propose a searching algorithm to optimize precoding degree distribution within the complexity range of the precoding stage. Simulation results show that compared to the conventional weighted BATS codes, our proposed scheme offers superior UEP performance and lower error floor, which verifies the effectiveness of our scheme.

摘要

批处理稀疏(BATS)码是一种网络编码方案,它结合了随机线性网络编码(RLNC)和喷泉编码来提高数据传输的可靠性和效率。为了实现对不同数据的不等差错保护,研究人员提出了不等差错保护BATS(UEP-BATS)码。然而,当前的UEP-BATS码在解码性能方面存在较高的误码平底,这限制了它们的实际应用。为了解决这个问题,我们提出了一种新颖的UEP-BATS码方案,该方案在加权BATS码之前采用了一个预编码阶段。所提出的预编码阶段利用了部分正则低密度奇偶校验(PR-LDPC)码,这有助于减轻加权BATS码中的高误码平底。我们基于密度进化(DE)推导了所提方案的渐近性能。此外,我们提出了一种搜索算法,以在预编码阶段的复杂度范围内优化预编码度分布。仿真结果表明,与传统的加权BATS码相比,我们提出的方案具有优越的不等差错保护性能和更低的误码平底,这验证了我们方案的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f600/10137935/0d35e3bb61a2/entropy-25-00686-g001.jpg

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