Transient‐Based Radio Frequency Fingerprinting Using Singular Superlet Transform and Convolutional Neural Network

ABSTRACT Radio frequency fingerprinting (RFF) is a technique for identifying wireless devices based on their intrinsic physical characteristics. In recent years, a variety of techniques have been proposed for this problem, including the use of convolutional neural networks (CNN) applied to the images created by applying Time‐Frequency (TF) transforms to the signals emitted by the wireless devices. The main contribution of this letter is the novel application of CNN in combination with the Singular Superlet Transform (SSLT), which has not yet been introduced in the RFF literature. The SSLT is a recently introduced TF superresolution representation that consists of increasingly bandwidth‐constrained wavelets and is particularly suitable for transient signals. In the proposed approach, the original RFF signal is converted into images that are input to a CNN with a multi‐head attention layer. The letter evaluates the SSLT‐based approach on two different public RFF datasets of wireless devices used in the Internet of Things (IoT) for different values of Signal‐to‐Noise Ratio (SNR). The results show that the proposed approach is able to outperform other combinations of CNN and TF transforms used in the literature for RFF, and it is particularly robust in the presence of noise.