Receiver-Agnostic Radio Frequency Fingerprinting via Domain-Invariant Feature Learning

Radio Frequency Fingerprinting (RFF) is an emerging physical-layer authentication technique that leverages intrinsic hardware imperfections introduced during the manufacturing of IoT devices. These device-specific features, inherently embedded in emitted RF signals, enable passive and non-intrusive device identification. While most existing RFF methods rely on data from a single receiver, real-world IoT deployments typically involve heterogeneous receivers, leading to cross-receiver domain shifts that severely degrade identification performance. However, research addressing this issue remains limited. To address this challenge, we propose a receiver-agnostic RFF framework that enhances generalization by learning domain-invariant features through a dual-perspective feature extraction strategy. Specifically, Fourier phase information is leveraged for its semantic stability and robustness to domain shifts. A knowledge distillation framework is introduced to extract intra-domain invariant features from the phase component, ensuring feature consistency within each receiver. In addition, a cross-domain correlation alignment mechanism is employed to capture shared representations across receivers, thereby improving robustness to distributional variations. Extensive experiments demonstrate that the proposed method consistently outperforms state-of-the-art domain generalization techniques. Moreover, results show that increasing the number of training receivers significantly improves identification accuracy in unseen receiver scenarios. The codes can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Edith-xx/Receiver-agnostic-RFFI-CL-</uri>