论文标题
置换代码纠正不同错误的神经网络解码器
Neural Network Decoders for Permutation Codes Correcting Different Errors
论文作者
论文摘要
对置换代码进行了广泛的研究，以纠正电源线通信上的应用程序和闪存等级调制的不同类型的错误。在本文中，我们介绍了置换代码的神经网络解码器，以单发解码纠正这些错误，这些解码将解码视为$ n $分类任务的非二进制符号，用于长度$ n $的代码。这些实际上是引入的第一个通用解码器，用于处理这两个应用程序的任何错误类型。通过具有不同误差模型的模拟来评估解码器的性能。
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.
