A Lightweight Radio Frequency Fingerprint Recognition Method Based on Spatial Synergy Enhancing Attention

Radio frequency (RF) fingerprint identification can recognize specific devices by analyzing the distinct features of the radiation sources in wireless signals. To address the issue of accuracy and computational cost in RF fingerprint identification, a lightweight method using spatial synergy enhancing attention (SSEA) is proposed. Firstly, the wireless signal is analyzed by short-time Fourier transform (STFT) to obtain the feature representation in the time-frequency domain. Then, the timefrequency spectrogram is input to the designed MobileNetV3SSEA neural network to extract features and identify the RF fingerprint. Experiments based on the public Bluetooth device data set show that the proposed method is effective in improving the accuracy compared with the traditional network identification method, and achieves a fast, efficient, and low computational cost RF fingerprint identification.