AI 中文总结
该研究提出软集成合并阶段（SECS），结合有序统计解码（OSD），在短码解码中大幅减少置信传播（BP）迭代次数，同时接近最大似然（ML）解码性能，实现低延迟可靠通信。
AI 中文摘要
集成解码是超可靠低延迟通信领域极具前景的技术，它通过并行运行M个不同的置信传播（BP）解码器，以硬件并行性换取解码延迟。传统集成解码从候选结果中选择输出，因此当没有成员解码器找到正确码字时，解码就会失败。本研究表明，浅层、多样化的BP解码器成员纠错能力较差，但排序能力出色。基于该观察，我们提出了软集成合并阶段（SECS），该阶段仅在几次迭代后就合并外部消息，生成的可靠性排序中最可靠的位置几乎无错误。后续的重编码阶段（如有序统计解码（OSD））可将此排序转换为近最大似然（ML）候选码字，其延迟仅为完全收敛集成解码的BP延迟的一小部分。我们在三种短码上验证了该方案：(63,30)博斯-乔赫里-霍昆格姆（Bose-Ray-Chaudhuri-Hocquenghem）码、超完全PG(2,8)码，以及搜索设计的(105,53)循环码。在所有案例中，带OSD后处理的SECS都缩小了与ML解码的大部分差距，同时显著减少了所需的BP迭代次数。
英文摘要
Ensemble decoding is a promising technique for ultra-reliable low-latency communication, as it trades hardware parallelism for decoding latency by running M diverse belief propagation (BP) decoders in parallel. Conventional ensembles select their output from the candidates. Hence, decoding fails whenever no member finds the correct codeword. In this work, we show that shallow, diverse BP members are poor correctors, but excellent sorters. Based on this observation, we propose a soft ensemble-combining stage (SECS) that combines extrinsic messages after only a few iterations, yielding a reliability ordering whose most reliable positions are nearly error-free. A subsequent re-encoding stage (like ordered-statistics decoding (OSD)) converts this ordering into a near-maximum likelihood (ML) candidate codeword, at a fraction of the BP latency of a fully converged ensemble. We demonstrate the proposed scheme on three short codes: a (63,30) Bose-Ray-Chaudhuri-Hocquenghem code, an overcomplete PG(2,8) code, and a search-designed (105,53) cyclic code. In all cases, the SECS with OSD post-processing closes most of the gap to ML decoding, while significantly reducing the number of required BP iterations.
Comments6 pages, 8 figures, submitted to an IEEE conference for possible publication
