Efficient Channel-Temporal Attention for Boosting RF Fingerprinting

In recent years, Deep Convolutional Neural Networks (DCNNs) have been widely used to solve Radio Frequency (RF) fingerprinting task. DCNNs are capable of learning the proper convolution kernels driven by data and directly extracting RF fingerprints from raw In-phase/Quadratur (IQ) data which are brought by variations or minor flaws in transmitters' circuits, enabling the identification of a specific transmitter. One of the main challenges in employing this sort of technology is how to optimize model design so that it can automatically learn discriminative RF fingerprints and show robustness to changes in environmental factors. To this end, this paper proposes ECTAttention, an Efficient Channel-Temporal Attention block that can be used to enhance the feature learning capability of DCNNs. ECTAttention has two parallel branches. On the one hand, it automatically mines the correlation between channels through channel attention to discover and enhance important convolution kernels. On the other hand, it can recalibrate the feature map through temporal attention. ECTAttention has good flexibility and high efficiency, and can be combined with existing DCNNs to effectively enhance their feature learning ability on the basis of increasing only a small amount of computational consumption, so as to achieve high precision of RF fingerprinting. Our experimental results show that ResNet enhanced by ECTAttention can identify 10 USRP X310 SDRs with an accuracy of 97.5%, and achieve a recognition accuracy of 91.9% for 56 actual ADS-B signal sources under unconstrained acquisition environment.