何时停止？序列集成的动态提前终止
When to Stop? Dynamic Early Termination of Sequential Ensembles
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中文总结 AI 辅助
针对序列集成解码的高复杂度问题，提出动态提前终止（DET）方法，通过决策树估计残余风险并动态停止，在IEEE 802.11 LDPC码上大幅降低计算量，同时保持性能。
中文摘要 AI 辅助
有序统计解码（OSD）后处理显著提升了置信传播（BP）集成的性能，但同时也消除了其基于校验子的自然停止准则。通过将集成成员顺序激活并配合提前终止，可以降低集成解码和OSD的高计算复杂度。因此，我们不再采用基于校验子的终止方式，而是将动态集成终止（DET）应用于序列集成：成员被逐一评估，当估计的未被评估成员纠正当前决策的风险低于离线拟合的阈值时，解码停止。考虑到面向硬件的设计，我们以分层归一化最小和分量解码器、行增强集成、对偶码上的条件低阶OSD以及传统的低成本解码器门来实例化该框架。决策树根据当前候选列表的统计信息估计残余风险。然后，成员相关的阈值针对预设的帧错误率（FER）损失。我们在多个IEEE 802.11低密度奇偶校验（LDPC）码上验证了该框架，这些码的块长度为648、1296和1944，码率为1/2和5/6。在每个码的设计点，即完整的64成员列表达到FER为10^-3时，DET解码器平均处理1.04至1.15个成员。在(648,540)码上，与完整列表相比，它减少了39倍的BP工作量和18倍的OSD激活次数，同时保持了约0.4分贝的信噪比（SNR）增益。
英文摘要
Ordered-statistics decoding (OSD) post-processing substantially improves the performance of belief propagation (BP) ensembles, but it also removes their natural syndrome-based stopping criterion. The high computational complexity of ensemble decoding and OSD can be reduced by sequential activation of ensemble members paired with an early termination. Instead of a syndrome-based termination we therefore apply dynamic ensemble termination (DET) to sequential ensembles: members are evaluated one at a time and decoding stops when the estimated risk that an unseen member would correct the current decision falls below an offline-fitted threshold. With hardware-oriented design in mind, we instantiate the framework with layered normalized min-sum component decoders, a row-boosted ensemble, conditional low-order OSD on the dual code, and a conventional low-cost decoder gate. A decision tree estimates the residual risk from statistics of the current candidate list. Member-dependent thresholds then target a prescribed frame error rate (FER) loss. We validate this framework on mutliple IEEE 802.11 low-density parity-check (LDPC) codes with blocklengths \(648,1296\), and \(1944\) at rates \(1/2\) and \(5/6\). At each code's design point, where the full \(64\)-member list reaches an FER of \(10^{-3}\), the DET decoder processes \(1.04\) to \(1.15\) members on average. On the \((648,540)\) code, it reduces BP work by \(39\times\) and OSD activations by \(18\times\) against the full list while retaining an signal-to-noise ratio (SNR) gain of about \(0.4\,\mathrm{dB}\).
