Radio Frequency Fingerprint Extraction Based on Multi-Dimension Approximate Entropy

With the advance in wireless network technique, its security becomes of paramount importance. Radio Frequency Fingerprint (RFF) is the underlying characteristic of hardware chains in transmitters, which can be used as a unique ID for specific emitter identification (SEI) and a non-cryptographic access authentication technology in physical layer to enhance wireless network security. To date, few studies have extracted the inevitable non-linearity in the transmitter as RFF features. Hence, this letter provides a novel nonlinear dynamics approach based on Multi-dimension Approximate Entropy (MApEn) for SEI. Specifically, this method utilizes the steady-state portion of the preamble structure of Standard IEEE802.11b/g to obtain the nonlinear properties of wireless network cards. The experimental results demonstrate that the proposed identification algorithm outperforms the existing steady-state methods in terms of the identification accuracy.

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