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：本文介绍了一种创新方法，用于设计满足现代通信系统严苛要求的高效译码器，特别是在超高可靠性和低延迟方面。我们通过提出一种结合滑动窗口技术的有序统计译码方案，增强了一种成熟的混合译码框架。该新型组件替代了现有架构中的关键元素，显著降低了平均复杂度。该方案的一个关键方面是集成了一个预训练的神经网络模型，能够动态决定滑动窗口过程的推进或停止。此外，我们提出了一种用户可定义的软边距机制，巧妙地平衡了译码准确性与复杂度之间的权衡。在深入复杂度分析支持下的实证结果表明，所提方案相比现有最先进译码器具有竞争优势，尤其在处理神经最小和译码器中普遍存在的译码失败问题方面。此外，我们的研究发现短LDPC码在信噪比的关键瀑布区域能够展现出与短经典线性码相媲美的性能，凸显了其在实际应用中的潜力。