Neural Network Decoders for Permutation Codes Correcting Different Errors
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 40 references that are in the map. First posted on arXiv 7 Jun 2022.
Builds on
11 of 40 references.
References · 11
- 2017Deep Learning Methods for Improved Decoding of Linear Codescited by 66
- 2016Learning to Decode Linear Codes Using Deep Learningcited by 60
- 2018Deep Learning for Decoding of Linear Codes - A Syndrome-Based Approachcited by 34
- 2017Neural Offset Min-Sum Decodingcited by 24
- 2020Pruning Neural Belief Propagation Decoderscited by 10
- 2019Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptationcited by 9
- 2018Deep Learning Based Sphere Decodingcited by 8
- 2019Active Deep Decoding of Linear Codescited by 8
- 2017Performance Evaluation of Channel Decoding With Deep Neural Networkscited by 7
- 2019What Can Machine Learning Teach Us about Communications?cited by 4
- 2020Neural network approaches to point lattice decodingcited by 3
Built on by
Not cited yet.