Abstract
With the rapid advancement of deep learning techniques, numerous neural networks have been successfully developed for radio frequency (RF) fingerprinting identification. In this work, we propose a lightweight yet reliable neural network framework featuring a 9-layer architecture based on the long short-term memory (LSTM) strategy, designed for efficient open-set fingerprinting identification. The simulated beacon frames model real-world propagation effects by incorporating random modulation, power amplifier nonlinearity, multi-path fading, inherent radio noise, and additive channel noise. We extensively evaluate the identification accuracy and efficiency of our LSTM network identification against well-known deep learning models such as ResNet (144 layers) and GoogleNet (177 layers). The evaluation covers a wide range of parameters, including transmitter variability (s), number of transmitters (N), frames per transmitter ([Formula: see text]) and signal-to-noise ratio ([Formula: see text]). Our results show that the LSTM network maintains an accuracy of more than [Formula: see text] in [Formula: see text] with [Formula: see text], even with up to [Formula: see text] transmitters. At lower values ([Formula: see text], [Formula: see text] dB), our LSTM network outperforms GoogleNet and matches ResNet in accuracy. Furthermore, it achieves a training acceleration of up to [Formula: see text] for [Formula: see text] and [Formula: see text], with inference times under 2 seconds. Meanwhile here, the usage of VRAM is reduced by up to [Formula: see text], and the model disk size is under 1 MB. Experiments on devices, including a high performance computing (HPC) node, a personal computer (PC), and three smartphones, demonstrate that the optimal strategy depends on the scale of the problem: local processing for [Formula: see text], remote training with local inference for [Formula: see text], and full remote processing for [Formula: see text].