Related papers: A Novel Approach to Radiometric Identification

A Novel Approach to Radiometric Identification

URL: http://arxiv.org/abs/2012.02256v1
Date: Wed, 2 Dec 2020 10:54:44 GMT
Title: A Novel Approach to Radiometric Identification
Authors: Raoul Nigmatullin, Semyon Dorokhin, Alexander Ivchenko
Abstract summary: 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.
Score: 68.8204255655161
License: http://creativecommons.org/licenses/by/4.0/
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 3, it is still possible to classify devices with 99% accuracy.

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