Novel Radio-Frequency Fingerprint Recognition Scheme Using Multiwavelets-Based Cyclic-Spectrum Graph Analysis
Qian Wang, Pengwei Li, Xiao Ling Yan, Hsiao‐Chun Wu, Yiyan Wu · 2021
A novel radio-frequency (RF) fingerprint recognition scheme using finite-element-multiwavelets-based cyclic-spectrum graph analysis is introduced in this work. Recognizing or classifying RF signals emitted from different devices without any help of spread/embedded identification codes or sequences is intriguing but challenging nowadays. In our proposed new approach, an RF signal emitted by an electronic device is first decomposed into multiple subbands using finite-element multiwavelets with a certain appropriate multiplicity. Thus, the cyclic spectra of extracted subband signals are formed and then the corresponding graphs can be established thereby. Such graph features may represent the RF fingerprints and be utilized to distinguish (recognize) devices by use of the discrepancies among them. Our simulations are based on realworld RF signals and the results demonstrate that the proposed finite-element-multiwavelets-based scheme can lead to higher correct recognition rates but lower computational complexities than the prevalent GHM-multiwavelets-based scheme and the discrete-Fourier-transform (DFT) method.