  Radio Frequency Fingerprint Identification (RFFI), which exploits non-ideal
hardware-induced unique distortion resident in the transmit signals to identify
an emitter, is emerging as a means to enhance the security of communication
systems. Recently, machine learning has achieved great success in developing
state-of-the-art RFFI models. However, few works consider cross-receiver RFFI
problems, where the RFFI model is trained and deployed on different receivers.
Due to altered receiver characteristics, direct deployment of RFFI model on a
new receiver leads to significant performance degradation. To address this
issue, we formulate the cross-receiver RFFI as a model adaptation problem,
which adapts the trained model to unlabeled signals from a new receiver. We
first develop a theoretical generalization error bound for the adaptation
model. Motivated by the bound, we propose a novel method to solve the
cross-receiver RFFI problem, which includes domain alignment and adaptive
pseudo-labeling. The former aims at finding a feature space where both domains
exhibit similar distributions, effectively reducing the domain discrepancy.
Meanwhile, the latter employs a dynamic pseudo-labeling scheme to implicitly
transfer the label information from the labeled receiver to the new receiver.
Experimental results indicate that the proposed method can effectively mitigate
the receiver impact and improve the cross-receiver RFFI performance.
