Deep Transfer Learning-based Detection for Flash Memory Channels
<|reference_start|>Deep Transfer Learning-based Detection for Flash Memory Channels: The NAND flash memory channel is corrupted by different types of noises, such as the data retention noise and the wear-out noise, which lead to unknown channel offset and make the flash memory channel non-stationary. In the literature, machine learning-based methods have been proposed for data detection for flash memory channels. However, these methods require a large number of training samples and labels to achieve a satisfactory performance, which is costly. Furthermore, with a large unknown channel offset, it may be impossible to obtain enough correct labels. In this paper, we reformulate the data detection for the flash memory channel as a transfer learning (TL) problem. We then propose a model-based deep TL (DTL) algorithm for flash memory channel detection. It can effectively reduce the training data size from $10^6$ samples to less than 104 samples. Moreover, we propose an unsupervised domain adaptation (UDA)-based DTL algorithm using moment alignment, which can detect data without any labels. Hence, it is suitable for scenarios where the decoding of error-correcting code fails and no labels can be obtained. Finally, a UDA-based threshold detector is proposed to eliminate the need for a neural network. Both the channel raw error rate analysis and simulation results demonstrate that the proposed DTL-based detection schemes can achieve near-optimal bit error rate (BER) performance with much less training data and/or without using any labels.<|reference_end|>
@article{mei2024deep,
    title={Deep Transfer Learning-based Detection for Flash Memory Channels},
    author={Zhen Mei, Kui Cai, Long Shi, Jun Li, Li Chen, and Kees A. Schouhamer
  Immink},
    journal={arXiv preprint arXiv:2410.05618},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05618},
    primaryClass={cs.IT eess.SP math.IT}
}