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.· 1 citation
This work asks whether processing nested ISI windows in separate recurrent branches improves the bit error rate (BER) of a Bi-LSTM, and pre-whiten the input, restoring the conditional independence that colored matched-filter noise violates, and distill the BCJR soft posterior into the network.
Nurettin Şafak, Osman Tokluoğlu, E. Çavuş· 0 citations
This study proposes a low-complexity, bidirectional, single-pass Elman recurrent neural network detector for binary phase-shift keying signals transmitted with faster-than-Nyquist signaling. Since the faster-than-Nyquist intersymbol interference has a short, finite memory, the classical Elman recurrent neural network,...
Nurettin Safak, Osman Tokluoğlu, E. Çavuş· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.