  Radio frequency fingerprint identification (RFFI) is an emerging device
authentication technique that relies on intrinsic hardware characteristics of
wireless devices. We designed an RFFI scheme for Long Range (LoRa) systems
based on spectrogram and convolutional neural network (CNN). Specifically, we
used spectrogram to represent the fine-grained time-frequency characteristics
of LoRa signals. In addition, we revealed that the instantaneous carrier
frequency offset (CFO) is drifting, which will result in misclassification and
significantly compromise the system stability; we demonstrated CFO compensation
is an effective mitigation. Finally, we designed a hybrid classifier that can
adjust CNN outputs with the estimated CFO. The mean value of CFO remains
relatively stable, hence it can be used to rule out CNN predictions whose
estimated CFO falls out of the range. We performed experiments in real wireless
environments using 20 LoRa devices under test (DUTs) and a Universal Software
Radio Peripheral (USRP) N210 receiver. By comparing with the IQ-based and
FFT-based RFFI schemes, our spectrogram-based scheme can reach the best
classification accuracy, i.e., 97.61% for 20 LoRa DUTs.
