  Radio frequency fingerprint identification (RFFI) can uniquely classify
wireless devices by analyzing the received signal distortions caused by the
intrinsic hardware impairments. The state-of-the-art deep learning techniques
such as convolutional neural network (CNN) have been adopted to classify IoT
devices with high accuracy. However, deep learning-based RFFI requires input
data of a fixed size. In addition, many IoT devices work in low signal-to-noise
ratio (SNR) scenarios but the low SNR RFFI is rarely investigated. In this
paper, the state-of-the-art transformer model is used as the classifier, which
can process signals of variable length. Data augmentation is adopted to improve
low SNR RFFI performance. A multi-packet inference approach is further proposed
to improve the classification accuracy in low SNR scenarios. We take LoRa as a
case study and evaluate the system by classifying 10 commercial-off-the-shelf
LoRa devices in various SNR conditions. The online augmentation can boost the
low SNR RFFI performance by up to 50% and multi-packet inference can further
increase it by over 20%.
