@misc{Gasparyan_N._Y._Hybrid,
 author={Gasparyan, N. Y. and Hakhumian, A. A. and Sivolenko, E. R.},
 howpublished={online},
 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.},
 type={Electronic journal},
 title={Hybrid Bispectrum–Waterfall Feature Extraction with CS-DSB for RF Receiver},
 keywords={Physics},
}