Preprint
Aug 2026
An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition
An uncertainty-driven hybrid deep learning architecture for recognizing RF signals over a broad modulation space by combining spectral information obtained through low-cost FFT-based preprocessing with time-frequency features extracted from short-time Fourier transform (STFT) spectrograms is proposed.
Nurettin Şafak, Durdu Can Yerdeyatar, Muhammet Sefa Demirel et al.
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