arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2206.03315cs.ITcs.LGmath.IT
收藏

纠正不同错误的置换码神经网络译码器

Neural Network Decoders for Permutation Codes Correcting Different Errors

National University of Singapore(新加坡国立大学)

Yeow Meng Chee, Hui Zhang

摘要:

为了纠正电力线通信和闪存的秩调制等应用中的不同类型的错误,对排列码进行了广泛的研究。在本文中,我们介绍了神经网络解码器的置换码,以纠正这些错误与一次性解码,处理的解码为$n$分类任务的非二进制符号的代码长度$n$。这些实际上是第一个通用解码器,用于处理这两个应用程序的任何错误类型。解码器的性能进行了评估与不同的错误模型的模拟。

英文摘要:

Permutation codes were extensively studied in order to correct different types of errors for the applications on power line communication and rank modulation for flash memory. In this paper, we introduce the neural network decoders for permutation codes to correct these errors with one-shot decoding, which treat the decoding as $n$ classification tasks for non-binary symbols for a code of length $n$. These are actually the first general decoders introduced to deal with any error type for these two applications. The performance of the decoders is evaluated by simulations with different error models.

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。

↑