发表机构
Technical University of Munich（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对物理层密钥生成问题，提出变分概率量化（VPQ）方法，结合神经编码器与线性码偏移安全草图，在高斯衰落信道上实现更低的窃听者泄漏，验证了其性能优势。
AI 中文摘要
在存在窃听者Eve的情况下，从Alice和Bob的相关观测中生成密钥是物理层安全的基础。经典流程采用手动量化，之后进行隐私放大，且不优化与密钥速率相关的目标。我们提出变分概率量化（Variational Probabilistic Quantization, VPQ）：将相关源直接映射到离散密钥字母表的神经编码器，通过变分对抗目标进行训练，该目标的熵、失配和泄漏项与单向密钥速率的三个项匹配。线性码偏移安全草图随后将编码器输出协调为相同密钥，无需单独的隐私放大步骤。我们证明VPQ损失是诱导源的单向密钥容量的下界，并针对给定字母表大小和失配概率的源类，推导了最优最坏情况密钥速率的闭式表达式，该速率由有限域线性草图实现。在高斯衰落信道上，与1种经典基线和2种近期基于学习的基线相比，VPQ向相关窃听者的泄漏更少，且Reed–Solomon协调在预测的有限块长度间隙内运行。
英文摘要
Secret key generation from correlated observations at Alice and Bob, in the presence of an eavesdropper Eve, underpins physical-layer security. Classical pipelines quantize by hand, amplify privacy afterwards, and optimize no objective tied to a key rate. We propose Variational Probabilistic Quantization (VPQ): neural encoders that map the correlated sources directly into a discrete key alphabet, trained by a variational adversarial objective whose entropy, mismatch, and leakage terms match the three terms of the one-way secret key rate. A linear code-offset secure sketch then reconciles the encoder outputs into an identical key without a separate privacy amplification step. We prove that the VPQ losses lower-bound the one-way secret key capacity of the induced source, and derive in closed form the optimal worst-case key rate over the source class of a given alphabet size and mismatch probability, attained by finite-field linear sketches. On Gaussian fading channels, VPQ leaks less to a correlated eavesdropper than one classical and two recent learning-based baselines, and Reed--Solomon reconciliation operates within the predicted finite-blocklength gap.
Comments6 pages, 4 figures. Accepted at IEEE GLOBECOM 2026
