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.

翻译：为了纠正电线通信应用和闪存级调制的不同类型的错误,对变异代码进行了广泛研究,以纠正电线通信应用和闪存级调制的不同类型错误。在本文件中,我们引入了神经网络变异代码解码器,用一发解码来纠正这些错误,其中将非二进制符号的解码任务视为一个长度代号($$)的非二进制符号的分类任务。这些实际上是为处理这两个应用程序的任何错误类型而引入的首个通用解码器。解码器的性能通过使用不同的错误模型进行模拟来评估。