In the search for highly efficient decoders for short LDPC codes approaching maximum likelihood performance, a relayed decoding strategy, specifically activating the ordered statistics decoding process upon failure of a neural min-sum decoder, is enhanced by instilling three innovations. Firstly, soft information gathered at each step of the neural min-sum decoder is leveraged to forge a new reliability measure using a convolutional neural network. This measure aids in constructing the most reliable basis of ordered statistics decoding, bolstering the decoding process by excluding error-prone bits or concentrating them in a smaller area. Secondly, an adaptive ordered statistics decoding process is introduced, guided by a derived decoding path comprising prioritized blocks, each containing distinct test error patterns. The priority of these blocks is determined from the statistical data during the query phase. Furthermore, effective complexity management methods are devised by adjusting the decoding path's length or refining constraints on the involved blocks. Thirdly, a simple auxiliary criterion is introduced to reduce computational complexity by minimizing the number of candidate codewords before selecting the optimal estimate. Extensive experimental results and complexity analysis strongly support the proposed framework, demonstrating its advantages in terms of high throughput, low complexity, independence from noise variance, in addition to superior decoding performance.

翻译：在探索逼近最大似然性能的短LDPC码高效译码器过程中，本文通过引入三项创新改进了一种中继译码策略——即在神经最小和译码器失败时激活有序统计译码过程。首先，利用神经最小和译码器每步收集的软信息，通过卷积神经网络构建新的可靠性度量。该度量有助于构建有序统计译码的最可靠基，通过排除易错比特或将其集中到更小区域来增强译码过程。其次，引入自适应有序统计译码过程，该过程由包含优先级块的导出译码路径引导，每个块包含不同的测试错误模式。块的优先级根据查询阶段的统计数据确定，并通过调整译码路径长度或细化对相关块的约束来设计有效的复杂度管理方法。第三，引入简单辅助准则，通过在选择最优估计前最小化候选码字数量来降低计算复杂度。大量实验与复杂度分析有力支持了所提出的框架，证明了其在吞吐量高、复杂度低、不依赖噪声方差及优异译码性能方面的优势。