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arXiv 2609.27519cs.ITmath.IT

块擦除信道与块z信道:有界译码器与有限块长

Block Erasure Channel and Block z-Channel with Bounded Decoders and Finite Blocklength

Bin Han, Yao Zhu, Rafael F. Schaefer, Wenwen Chen, Giuseppe Caire, Anke Schmeink, H. Vincent Poor, Hans D. Schotten

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中文总结 AI 辅助

针对有限块长下有界距离译码器,推导块混淆与擦除概率的严格界,并揭示块z信道抽象,为协议设计提供物理层支持。

中文摘要 AI 辅助

块错误率是有限块长(FBL)通信中的标准度量,然而它混淆了两种性质不同的失效模式:块混淆(译码器选择错误码字)和块擦除(译码器声明丢失)。高层协议将物理层失效视为擦除,但这种跨层假设缺乏FBL层面的论证。我们针对加性高斯白噪声(AWGN)信道上的有界距离译码器,在有限块长下推导了块混淆概率(BLCP)和块擦除概率(BLEP)的严格上界及配套的下侧估计,将编码问题重新表述为几何球填充问题。我们分析了这些界对块长和信噪比的敏感性,刻画了上界的包络,并推导了闭式Chernoff近似。将模型扩展到空闲传输块,我们给出了虚警概率(FAP)的界,并表明块z信道抽象可从有界译码几何中自然涌现。数值结果证实,混淆概率和虚警概率远低于错误率约束,为协议设计中假设的块擦除信道和块z信道抽象提供了定量的物理层支持。

英文摘要

Block error rate is a standard metric in finite blocklength (FBL) communication, yet it conflates two qualitatively different failure modes: block confusions, where the decoder selects a wrong codeword, and block erasures, where it declares a loss. Higher-layer protocols treat physical-layer failures as erasures, but this cross-layer assumption lacks FBL justification. We derive a rigorous upper bound and a companion lower-side estimate on the block confusion probability (BLCP) and block erasure probability (BLEP) for bounded-distance decoders over additive white Gaussian noise (AWGN) channels at finite blocklength, recasting the coding problem as a geometric sphere packing one. We analyze the sensitivity of these bounds to blocklength and signalto-noise ratio, characterize the envelope of the upper bound, and derive closed-form Chernoff approximations. Extending the model to idle transmission blocks, we bound the false alarm probability (FAP) and show that a block z-channel abstraction emerges from the bounded-decoding geometry. Numerical results confirm that confusion and false alarm probabilities lie far below the error rate constraint, providing quantitative physicallayer support for the block erasure channel and block z-channel abstractions assumed in protocol design.

发表机构

  • RPTU University Kaiserslautern-Landau(莱茵兰-普法塔尔凯泽斯劳滕-兰道应用技术大学)
  • Wuhan University(武汉大学)
  • RWTH Aachen University(亚琛工业大学)
  • TU Dresden(德累斯顿工业大学)
  • TU Berlin(柏林工业大学)
  • Princeton University(普林斯顿大学)
  • German Research Center for Artificial Intelligence (DFKI)(德国人工智能研究中心)

机构由 AI 辅助整理,请以论文原文为准。

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