Radio frequency fingerprint (RFF) identification technique was developed with the aim of identifying and classifying wireless devices by exploiting their unique radio frequency (RF) features. This paper proposes a wireless device RFF identification scheme, which relies on the non-linear memory effect resulting from the concatenation of two matched root-raised cosine (RRC) filters with a non-linear power amplifier (PA) inserted in-between. This unique feature can be extracted from the distorted constellation diagrams, which is processed into density trace figure. Classification algorithm is based on image recognition methods developed to exploit the non-linear memory effect from the density trace figure. Simulation setup is carefully designed, and the results validated the effectiveness of our proposed RFF feature classification approach. In the range of SNR 1 dB to 25 dB, the overall identification accuracy is as high as 99%.
|Publication status||Accepted/In press - 13 Apr 2021|
|Event||2021 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems - , United States|
Duration: 18 May 2021 → 20 May 2021
|Conference||2021 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems|
|Period||18/05/21 → 20/05/21|