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.

翻译：标记码是一种有效的编码方案，用于保护数据免受插入和删除错误的影响，在未来存储系统（如DNA存储和赛道存储器）中具有潜在应用价值。在解码标记码时，需要完美的信道状态信息（CSI），即插入和删除概率，以检测插入和删除错误。然而，完美CSI有时难以获取，或准确的信道模型未知。因此，有必要在无需完美CSI的情况下开发标记码的检测算法。本文提出了两种基于深度学习的、无需CSI的标记码检测算法。第一种是基于模型驱动的深度学习方法，该方法对标记码原始的迭代检测算法进行深度展开。在这种方法中，CSI转化为神经网络中的权重，这些权重可从训练数据中学习得到。第二种是基于数据驱动的方法，这是一个基于深度双向门控循环单元网络的端到端系统。仿真结果表明，在CSI不确定的情况下，所提方法的误码性能显著优于原始检测算法。此外，对于未知信道模型，所提出的数据驱动方法展现出优于其他方法的误码性能。