Deep Learning-Based Detection for Marker Codes over Insertion and Deletion Channels
This work addresses a practical limitation in coding schemes for storage systems like DNA storage and racetrack memory, offering incremental improvements by adapting existing detection algorithms to handle imperfect channel knowledge.
The paper tackles the problem of detecting insertion and deletion errors in marker codes without requiring perfect channel state information, proposing two deep learning-based algorithms that achieve significantly better error performance compared to existing methods, with simulation results showing substantial improvements under CSI uncertainty and unknown channel models.
Marker code is an effective coding scheme to protect data from insertions and deletions. It has potential applications in future storage systems, such as DNA storage and racetrack memory. When decoding marker codes, perfect channel state information (CSI), i.e., insertion and deletion probabilities, are required to detect insertion and deletion errors. Sometimes, the perfect CSI is not easy to obtain or the accurate channel model is unknown. Therefore, it is deserved to develop detecting algorithms for marker code without the knowledge of perfect CSI. In this paper, we propose two CSI-agnostic detecting algorithms for marker code based on deep learning. The first one is a model-driven deep learning method, which deep unfolds the original iterative detecting algorithm of marker code. In this method, CSI become weights in neural networks and these weights can be learned from training data. The second one is a data-driven method which is an end-to-end system based on the deep bidirectional gated recurrent unit network. Simulation results show that error performances of the proposed methods are significantly better than that of the original detection algorithm with CSI uncertainty. Furthermore, the proposed data-driven method exhibits better error performances than other methods for unknown channel models.