This paper introduces an innovative approach to the design of efficient decoders that meet the rigorous requirements of modern communication systems, particularly in terms of ultra-reliability and low-latency. We enhance an established hybrid decoding framework by proposing an ordered statistical decoding scheme augmented with a sliding window technique. This novel component replaces a key element of the current architecture, significantly reducing average complexity. A critical aspect of our scheme is the integration of a pre-trained neural network model that dynamically determines the progression or halt of the sliding window process. Furthermore, we present a user-defined soft margin mechanism that adeptly balances the trade-off between decoding accuracy and complexity. Empirical results, supported by a thorough complexity analysis, demonstrate that the proposed scheme holds a competitive advantage over existing state-of-the-art decoders, notably in addressing the decoding failures prevalent in neural min-sum decoders. Additionally, our research uncovers that short LDPC codes can deliver performance comparable to that of short classical linear codes within the critical waterfall region of the SNR, highlighting their potential for practical applications.

翻译：本文提出了一种创新方法，用于设计满足现代通信系统严苛要求（尤其是超可靠与低延迟需求）的高效译码器。我们通过提出一种融合滑动窗口技术的增强型有序统计译码方案，改进了现有的混合译码框架。该新模块取代了当前架构中的关键组件，显著降低了平均复杂度。本方案的核心在于集成预训练神经网络模型，该模型能动态决定滑动窗口进程的推进或终止。此外，我们引入一种用户自定义的软判决裕度机制，巧妙平衡译码精度与复杂度之间的权衡。基于详尽复杂度分析的实证结果表明，所提方案相较于现有最先进的译码器具有竞争优势，尤其在解决神经最小和译码器中常见的译码失败问题上表现突出。同时，本研究发现短LDPC码在信噪比的关键瀑布区间内可达到与短经典线性码相当的性能，凸显了其实际应用潜力。