Mitigating Receiver Impact on Radio Frequency Fingerprint Identification via Domain Adaptation

arXiv:2404.08566v1 Announce Type: cross
Abstract: 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 …

abstract art arxiv communication cs.lg domain domain adaptation eess.sp exploits hardware identification identify impact machine machine learning radio security state success systems type via