  Radio-frequency fingerprints~(RFFs) are promising solutions for realizing
low-cost physical layer authentication. Machine learning-based methods have
been proposed for RFF extraction and discrimination. However, most existing
methods are designed for the closed-set scenario where the set of devices is
remains unchanged. These methods can not be generalized to the RFF
discrimination of unknown devices. To enable the discrimination of RFF from
both known and unknown devices, we propose a new end-to-end deep learning
framework for extracting RFFs from raw received signals. The proposed framework
comprises a novel preprocessing module, called neural synchronization~(NS),
which incorporates the data-driven learning with signal processing priors as an
inductive bias from communication-model based processing. Compared to
traditional carrier synchronization techniques, which are static, this module
estimates offsets by two learnable deep neural networks jointly trained by the
RFF extractor. Additionally, a hypersphere representation is proposed to
further improve the discrimination of RFF. Theoretical analysis shows that such
a data-and-model framework can better optimize the mutual information between
device identity and the RFF, which naturally leads to better performance.
Experimental results verify that the proposed RFF significantly outperforms
purely data-driven DNN-design and existing handcrafted RFF methods in terms of
both discrimination and network generalizability.
