An Artificial Radio Frequency Fingerprint Embedding Scheme for Device Identification
Zhen Zhang, Aiqun Hu, Wei Xu, Jiabao Yu, Yang Yang
Abstract
Radio frequency fingerprint (RFF) is an intrinsic physical characteristic of the device caused by hardware imperfection. It can be extracted from wireless signals and has been widely applied for device identification. However, the improving precision of electronic components reduces the distinguishability of RFF, which makes it difficult to identify large amount of devices that are of the same model, especially in a low signal-to-noise ratio (SNR) scenario. In this letter, we propose an artificial radio frequency fingerprint (ARFF) embedding scheme for device identification, which greatly increases the distinguishability of fingerprints and minimally affects the performance of communication. The scheme includes an adaptive filter based RFF extraction method, a principal component analysis (PCA) based ARFF generation algorithm and a proposed ARFF embedding algorithm that eliminates the influence of the device’s original RFF to form a preset ARFF pattern. Experimental results on ZigBee devices demonstrate that our ARFF based scheme achieves 99.91% identification accuracy and simultaneously has little impact on bit error rate.