Neural Network Decoders for Permutation Codes Correcting Different Errors

TL;DR

This paper introduces neural network decoders for permutation codes that can correct various error types in power line communication and flash memory, using a novel one-shot decoding approach as classification tasks.

Contribution

It presents the first general neural network decoders capable of correcting multiple error types in permutation codes for two key applications.

Findings

Decoders perform well across different error models.

Neural network approach enables one-shot decoding.

First general decoder for these error types in permutation codes.

Abstract

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.