发表机构
Harvard University（哈佛大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对信道切换与漂移场景下软GRAND算法的解码错误问题，通过界定错误边界、采用状态路径混合与导频刷新方法，经广义高斯BPSK实验验证可提升解码性能。
AI 中文摘要
在信道切换或漂移场景下，用于排序软GRAND查询的后验概率可能与匹配的校正后验概率存在差异，这会提升查询排名，进而增加有限预算下的解码错误。我们将对数查询排名界定为匹配后验自信息与正对数后验不匹配项之和；精确的随机子集碰撞概率可得出GRANDAB的错误边界。对于在无记忆信道间切换且采用容量可达的均匀输入的情况，当对数路径类大小为亚线性时，状态路径混合方法可在低于最小组成容量的容许路径上均匀地使错误消失。对于漂移场景，导频刷新可限制不匹配项，并得到跟踪上界的连续极小化器。我们通过广义高斯BPSK实验对上述内容进行了验证。
英文摘要
Under channel switching or drift, the posterior used to order Guessing Random Additive Noise Decoding (GRAND) queries can differ from the matched correction posterior, increasing query rank and finite-budget error. We bound log query rank by matched posterior self-information plus positive log-posterior mismatch; exact random-subset collision probabilities bound GRAND with abandonment (GRANDAB) error. Under switching, a state-path mixture achieves vanishing error uniformly over admissible paths below the minimum constituent capacity when log path-class size is sublinear and uniform input is capacity achieving. Under drift, pilot refresh bounds mismatch and yields a continuous refresh-period minimizer. Generalized-Gaussian BPSK simulations evaluate both regimes.
