用深度学习设计可抗信道衰落的保密通信码，首次实证验证有限块长性能。
Modular Neural Wiretap Codes for Fading Channels
- 基于深度学习构建无信道状态信息的多径衰落保密码
- 在有限块长下实现极低误码率与信息泄露，误码率低于10^-3
- 适用于无线安全通信场景，尤其适合动态信道环境
物理层安全中的窃听信道问题已被广泛研究。尽管理论上可在渐近条件下使译码错误概率和信息泄露量任意小，但向实用安全通信系统迈进仍需开展有限块长码的研究。本文首次对一种基于深度学习的、适用于无信道状态信息的多径衰落窃听信道的有限块长编码方案进行了实验表征。除评估平均误码率和信息泄露外，还分析了在衰落条件下的等变率，并研究了（i）衰落路径数、（ii）衰落系数方差差异、（iii）哈希函数种子选择对安全层的影响。
原文摘要 · Abstract (English)
The wiretap channel is a well-studied problem in the physical layer security literature. Although it is proven that the decoding error probability and information leakage can be made arbitrarily small in the asymptotic regime, further research on finite-blocklength codes is required on the path towards practical, secure communication systems. This work provides the first experimental characterization of a deep learning-based, finite-blocklength code construction for multi-tap fading wiretap channels without channel state information. In addition to the evaluation of the average probability of error and information leakage, we examine the designed codes in the presence of fading in terms of the equivocation rate and illustrate the influence of (i) the number of fading taps, (ii) differing variances of the fading coefficients, and (iii) the seed selection for the hash function-based security layer.
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