  Radio frequency fingerprint identification (RFFI) can classify wireless
devices by analyzing the signal distortions caused by the intrinsic hardware
impairments. State-of-the-art neural networks have been adopted for RFFI.
However, many neural networks, e.g., multilayer perceptron (MLP) and
convolutional neural network (CNN), require fixed-size input data. In addition,
many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the RFFI
performance in such scenarios is rarely investigated. In this paper, we analyze
the reason why MLP- and CNN-based RFFI systems are constrained by the input
size. To overcome this, we propose four neural networks that can process
signals of variable lengths, namely flatten-free CNN, long short-term memory
(LSTM) network, gated recurrent unit (GRU) network and transformer. We adopt
data augmentation during training which can significantly improve the model's
robustness to noise. We compare two augmentation schemes, namely offline and
online augmentation. The results show the online one performs better. During
the inference, a multi-packet inference approach is further leveraged 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 classification accuracy by up to 50% and the multi-packet inference
approach can further increase the accuracy by over 20%.
