AI 中文总结
研究CV-QKD中高速LDPC解码的硬件加速问题，在统一低信噪比定点框架下比较SPA、MSA和NMS，通过FER和平均迭代次数评估多种格式，得出SPA总体性能最佳，Q8.4的SPA在大规模实现中平衡可靠性与硬件效率最佳。
AI 中文摘要
高速低密度奇偶校验（LDPC）解码是连续变量量子密钥分发（CV-QKD）中的一个主要瓶颈，这推动了使用定点算法进行硬件加速。这项工作在统一的低信噪比定点框架下，使用通用的图、矩阵和量化设置，比较了和积算法（SPA）、最小和算法（MSA）和归一化最小和算法（NMS）。通过误帧率（FER）和平均迭代次数评估了多种格式。结果表明，性能在很大程度上取决于解码器规则和数值精度之间的相互作用。SPA总体性能最佳。对于低复杂度解码器，Q16.8是最低的一致精度，NMS优于MSA。实际上，对于大规模实现，具有Q8.4的SPA在可靠性和硬件效率之间提供了最佳平衡。
英文摘要
High-speed LDPC decoding is a major bottleneck in CV-QKD and motivates hardware acceleration with fixed-point arithmetic. This work compares SPA, MSA, and NMS under a unified low-SNR fixed-point framework using common graph, matrix, and quantization settings. Multiple formats are evaluated through FER, and average iterations. The results show that performance depends strongly on the interaction between decoder rule and numerical precision. SPA achieved the best overall performance. For reduced-complexity decoders, Q16.8 was the lowest consistent precision, with NMS outperforming MSA. Practically, SPA with Q8.4 offered the best balance between reliability and hardware efficiency for large-scale implementations.
CommentsAccepted for publication in the XLIV Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT 2026)
