A Novel Approach to Radiometric Identification

Abstract

This paper demonstrates that highly accurate radiometric identification is possible using CAPoNeF feature engineering method. We tested basic ML classification algorithms on experimental data gathered by SDR. The statistical and correlational properties of suggested features were analyzed first with the help of Point Biserial and Pearson Correlation Coefficients and then using P-values. The most relevant features were highlighted. Random Forest provided 99% accuracy. We give LIME description of model behavior. It turns out that even if the dimension of the feature space is reduced to 1, it is still possible to classify devices with 99% accuracy.

Generalized Hurst Hypothesis: Description of Time-Series in Communication Systems

“…The proposed generalized Hurst law may be used for classification of transmitters, as was done by Nigmatullin et al [30]. The generalized Hurst law may yield more reliable results since it captures fractal properties of a TLS, which was difficult to do with the parameters proposed in [30]. The new set of parameters may also show good results in a task of weak signal detection.…”

“…Such an approach may be useful in the radiometric identification task as well, when the transmitter devices are to be classified based on the difference of the received and the reference signal. The proposed generalized Hurst law may be used for classification of transmitters, as was done by Nigmatullin et al [30]. The generalized Hurst law may yield more reliable results since it captures fractal properties of a TLS, which was difficult to do with the parameters proposed in [30].…”