发表机构
Center for Connected Autonomy and AI, Florida Atlantic University; Air Force Research Laboratory（佛罗里达大西洋大学互联自主与人工智能中心; 空军研究实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种信道感知神经网络，从IQ测量中提取二进制密钥特征，利用多任务学习与射线追踪增强，在POWDER测试台上验证了低比特不一致性和高密钥多样性，实现去中心化无线密钥生成。
AI 中文摘要
物理层密钥生成（PKG）使无线设备能够从互易的信道观测中建立共享密钥，而无需直接交换密钥。这一能力对边缘网络具有吸引力，因为在边缘网络中，分布式且资源受限的设备可能需要在有限访问集中式基础设施的情况下，实现轻量级的密钥建立。我们提出了一种用于PKG的信道感知神经网络，该网络直接从接收到的IQ测量中提取二进制密钥特征，同时将学习到的表示明确地基于底层多径信道。所提出的多任务循环神经网络联合学习保持互易性的二进制特征和辅助信道估计，其训练目标结合了深度度量学习与信道感知监督。结构化信道探测能够从空中测量中进行信道估计，而Sionna-RT射线追踪用于增加额外传播条件下的训练数据。我们使用在POWDER无线电测试平台上收集的室内和室外软件定义无线电测量来评估该框架。在所有评估场景中，所提出的模型对于互易的Alice-Bob观测产生的比特不一致性低于与Eve相关的观测。射线追踪数据增强显著提高了密钥多样性，在室内和两个室外场景中，唯一密钥率分别提高到0.94、0.99和0.99。成功协调的信道感知密钥在SHA-3隐私放大之前通过了选定的NIST随机性测试。结果证明了信道感知表示学习在去中心化无线密钥建立中的潜力，同时突出了密钥多样性与协调可靠性之间的重要权衡。
英文摘要
Physical-layer key generation (PKG) enables wireless devices to establish shared keys from reciprocal channel observations without directly exchanging the key. This capability is attractive for edge networks, where distributed and resource-constrained devices may require lightweight key establishment with limited access to centralized infrastructure. We introduce a channel-informed neural network for PKG that derives binary key features directly from received IQ measurements while explicitly grounding the learned representation in the underlying multipath channel. The proposed multi-task recurrent neural network jointly learns reciprocity-preserving binary features and an auxiliary channel estimate using a training objective that combines deep metric learning with channel-informed supervision. Structured channel sounding enables channel estimation from over-the-air measurements, while Sionna-RT ray tracing is used to augment training with additional propagation conditions. We evaluate the framework using indoor and outdoor software-defined-radio measurements collected on the POWDER radio testbed. Across all evaluated scenarios, the proposed model produces lower bit disagreement for reciprocal Alice-Bob observations than for Eve-related observations. Ray-traced data augmentation substantially improves key diversity, increasing the unique-key rate to 0.94, 0.99, and 0.99 across the indoor and two outdoor scenarios, respectively. Successfully reconciled channel-informed keys pass the selected NIST randomness tests prior to SHA-3 privacy amplification. The results demonstrate the potential of channel-informed representation learning for decentralized wireless key establishment while highlighting an important tradeoff between key diversity and reconciliation reliability.
