Radiometric identification using variational mode decomposition
| Autor: | Franc Dimc, Gianmarco Baldini, Gary Steri, Raimondo Giuliani |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Line-of-sight
General Computer Science business.industry Computer science 020206 networking & telecommunications 02 engineering and technology Dedicated short-range communications Hilbert–Huang transform Identification (information) symbols.namesake Additive white Gaussian noise Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering symbols Electronic engineering Wireless 020201 artificial intelligence & image processing Fading Radio frequency Electrical and Electronic Engineering business |
| Zdroj: | Computers & Electrical Engineering. 76:364-378 |
| ISSN: | 0045-7906 |
| DOI: | 10.1016/j.compeleceng.2019.04.014 |
| Popis: | Radiometric Identification (RAI) is the identification of wireless devices through their Radio Frequency (RF) emissions. In recent years, the research community has investigated it applying different methods and sets of statistical features extracted from the digitized RF emissions. In this paper, the authors investigate the application of Variational Mode Decomposition (VMD), recently introduced as an improvement to Empirical Mode Decomposition (EMD). VMD is applied to two sets of RF emissions from: wireless devices supporting Dedicated Short Range Communications (DSRC) at 5.9 GHz and Internet of Things wireless devices transmitting in the Industrial, Scientific and Medical (ISM) band at 2.4 GHz. Various machine learning algorithms have been used for classification and results are compared. Performances of VMD are evaluated against other approaches used in literature in Line of Sight (LOS) conditions, with Additive White Gaussian Noise (AWGN) and fading effects. Results show that VMD significantly outperforms other approaches. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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