A Radio Frequency Fingerprinting Scheme Using Learnable Signal Representation

Radio frequency (RF) fingerprinting is a promising technique for device authentication due to its advantages of difficult-to-tamper and inimitableness. Rather than employing hand-crafted features, deep learning-based methods that learn low-level signal representation from raw signals have been explored recently. This letter proposes a novel RF fingerprinting scheme using learnable short-time Fourier transform (STFT) and convolutional neural network (CNN). Instead of representing radio signals by spectrograms from a fixed STFT and processing signal representation and classification separately, this approach integrates a parameterized STFT-based signal representation module and a CNN classifier into a single framework. The signal representation module is jointly trained with the CNN classifier using a single identification loss, transforming the input radio signals into spectrograms desired by the CNN classifier. Experimental results show that the RF fingerprint identification accuracy of the proposed scheme with learnable signal representation is significantly improved compared to baseline schemes with traditional hand-crafted signal representations.