Author

Nurettin Safak

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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. · 1 citation

#machine learning Preprint Sep 2026

Pre-Whitening and BCJR Posterior Distillation for Bi-LSTM Detection in Faster-than-Nyquist Signaling

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

#machine learning Preprint Sep 2026

Sub-6 GHz Over-the-Air AMC via Curriculum Fine-Tuned CNN-Transformers

A curriculum fine-tuning study of a hybrid CNN-Transformer AMC model, with matched-distance test accuracy and confusion-matrix analysis grounded in RF theory, and reports honestly where the sequential fine-tuning order confounds cumulative link adaptation with antenna alignment, rather than overstating what the data ca...

Nurettin Şafak, Muhammet Sefa Demirel, A. Marasli et al. · 0 citations

Preprint Aug 2026

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling

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