  Radio frequency fingerprint identification (RFFI) is a promising device
authentication technique based on the transmitter hardware impairments. In this
paper, we propose a scalable and robust RFFI framework achieved by deep
learning powered radio frequency fingerprint (RFF) extractor. Specifically, we
leverage the deep metric learning to train an RFF extractor, which has
excellent generalization ability and can extract RFFs from previously unseen
devices. Any devices can be enrolled via the pre-trained RFF extractor and the
RFF database can be maintained efficiently for allowing devices to join and
leave. Wireless channel impacts the RFF extraction and is tackled by exploiting
channel independent feature and data augmentation. We carried out extensive
experimental evaluation involving 60 commercial off-the-shelf LoRa devices and
a USRP N210 software defined radio platform. The results have successfully
demonstrated that our framework can achieve excellent generalization abilities
for device classification and rogue device detection as well as effective
channel mitigation.
