Publication Details:

Established in 2008

Journal or Publication Title:

Armenian Journal of Physics=Ֆիզիկայի հայկական հանդես

Date of publication:

2025

Volume:

18

Number:

3

ISSN:

1829-1171

Official URL:

Coverage:

43-47

Abstract:

RF receiver identification requires robust feature extraction to distinguish subtle hardware-induced characteristics. Conventional methods based on higher-order spectra or time–frequency features often degrade under low SNR and multipath conditions. This paper proposes a hybrid bispectrum–waterfall feature extraction framework with CS-DSB (Carrier-Suppress Double Sideband) for RF receivers. The bispectrum highlights nonlinear phase coupling unique to receiver hardware, while waterfall features capture spectral and temporal variations. To improve efficiency, CS-DSB reduces data dimensionality while preserving discriminative information. A fusion scheme integrates both feature domains, followed by classification using a supervised learning model. Experimental results demonstrate that the proposed method significantly outperforms bispectrum-only, waterfall-only, and conventional approaches, achieving higher identification accuracy under noisy and bandwidth-limited scenarios. The findings show that combining bispectral, waterfall, and CS-DSB processing enhances robustness and enables efficient RF receiver fingerprinting.

Format:

pdf

Identifier:

oai:arar.sci.am:415749

Location of original object:

ՀՀ ԳԱԱ Հիմնարար գիտական գրադարան