Representation-Oblivious Error Correction by Natural Redundancy
Representation-Oblivious Error Correction by Natural Redundancy
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DOI：
10.1109/icc.2019.8762073
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Pulakesh Upadhyaya;Anxiao Jiang
中科院分区：
文献类型：
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作者：
Pulakesh Upadhyaya;Anxiao Jiang
存储系统强烈需要大幅提高其纠错能力，特别是对于长期存储，累积的错误可能会超过纠错码（ECC）的解码阈值。在这项工作中，提出了一种新方案，使用深度学习根据噪声文件的自然冗余对其进行软解码。然后将软解码结果与 ECC 相结合，以获得更好的纠错性能。该方案是忽略表示的：它不需要先验知识如何在不同类型的文件中表示数据（例如，从符号映射到位、压缩以及与元数据组合），这使得该解决方案更方便用于存储系统。实验结果证实，即使文件的误码率明显超过ECC的解码阈值，该方案也能显着提高不同类型文件的数据恢复能力。本作品的代码已公开发布。
Storage systems have a strong need for substantially improving their error correction capabilities, especially for long-term storage where the accumulating errors can exceed the decoding threshold of error-correcting codes (ECCs). In this work, a new scheme is presented that uses deep learning to perform soft decoding for noisy files based on their natural redundancy. The soft decoding result is then combined with ECCs for substantially better error correction performance. The scheme is representation-oblivious: it requires no prior knowledge on how data are represented (e.g., mapped from symbols to bits, compressed, and combined with meta data) in different types of files, which makes the solution more convenient to use for storage systems. Experimental results confirm that the scheme can substantially improve the ability to recover data for different types of files even when the bit error rates in the files have significantly exceeded the decoding threshold of the ECC. The code of this work has been publicly released.