Neural Network Decoders for Permutation Codes Correcting Different Errors
This provides a general decoding solution for permutation codes in specific communication and memory applications, though it appears incremental as it applies neural networks to an existing coding problem.
The authors tackled the problem of decoding permutation codes for power line communication and flash memory applications by introducing the first general neural network decoders capable of handling any error type with one-shot decoding, achieving performance evaluated through simulations across different error models.
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.