Deep Learning-Based Detection for Marker Codes over Insertion and
  Deletion Channels
Guochen Ma Xiaopeng Jiao Jianjun Mu Hui Han Yaming Yang
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.