Transient‐based radio frequency fingerprinting with adaptive ensemble of transforms and convolutional neural network

Abstract

Abstract Radio Frequency Fingerprinting (RFF) has been recently investigated by the research community to enhance wireless security. This letter proposes an RFF approach based only on the complex ramp‐up transient portion of the transmitted frame. The well known issues in literature with the use of the transients for RFF is that they have very short duration, they are strongly non‐stationary and their analysis is sensitive to noise. Then, this letter proposes the application of an ensemble of transforms to the original complex time domain representation in combination with Convolution Neural Network (CNN) to address these issues. To avoid the computing demanding calculation of all the considered transforms, a pre‐processing step based on feature extraction and filter feature selection is implemented to select the most discriminating transforms to reduce the input data to the CNN. The approach is applied to two public RFF data sets, where the complex transient ramp‐up have been extracted. The result shows that the proposed approach is able to obtain a classification accuracy higher than the baseline based on the original signal or selected transforms across various values of Signal to Noise Ratio (SNR).

Keywords

• artificial intelligence
• discrete Fourier transforms
• discrete wavelet transforms
• feature selection
• radio transmitters
• signal processing