Enhancement of Transient-Based Radio Frequency Fingerprinting with Smoothing and Gradient Functions

Radio frequency (RF) fingerprinting identification (RFFI) is a promising physical layer classification and authentication technique based on the intrinsic hardware defects of electronic systems in general and wireless communication emitters in particular as investigated in this paper. The hardware differences in the electronic components of the radio frequency front end of the wireless emitters propagate in the structure and shape (the fingerprints) of the signal transmitted in the space. After signal digitization, the analysis of the signal can be used to distinguish the source emitters. The presence of noise or signal attenuation may obfuscate the RF fingerprints (RFF) and significant research efforts in literature focused on removing the disturbances from the signal or enhancing the fingerprints. One potential issue is that de-noising techniques may remove the same fingerprints needed for the emitter identification. This paper proposes a combination of denoising and enhancing functions, whose contribution to the signal analysis are weighted using a feature selection algorithm applied to the spectral representation of the signal. A machine learning algorithm is used to implement the RFFI. The approach is applied on a recently published data set with 10 ZigBee devices where only the transient portion of the signal is used to implement the RFFI. The results show that the proposed approach outperforms the direct application of the machine learning algorithm on the spectral representation of the signal for different values of the Signal to Noise Ratio (SNR) in dB.