arXiv subjects

Nurettin Safak

Publications and source records attributed to Nurettin Safak.

5 recordsLinked to original sources

arXiv2026

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

Automatic modulation classification (AMC) models are frequently trained and validated on synthetic or channel-cabled data, leaving open the question of how they behave once path loss and antenna pointing error are introduced by a genuine free-space link. We report a curriculum fine-tuning study of a hybrid CNN-Transformer AMC model. The general-purpose, all-32-class dataset underlying the model was built entirely at 915 MHz, on a controlled, clock/PPS-synchronized MIMO-expansion-cable link (not spatial-multiplexing transmission); all subsequent free-space, real-hardware experimentation - the sequential fine-tuning curriculum, matched-distance evaluation, and every reported over-the-air accuracy figure - was carried out at 4 GHz, across five directional-antenna distances (25, 35, 50, 70, 75 cm) under fixed TX/RX gain. Three distances used near-ideal antenna alignment (~99%) and two used a deliberately introduced partial misalignment (~85%), fine-tuned last in the curriculum. We report matched-distance test accuracy (91.8-93.7% across all five 4 GHz conditions) and confusion-matrix analysis grounded in RF theory, and report honestly where our sequential fine-tuning order confounds cumulative link adaptation with antenna alignment, rather than overstating what the data can support.

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arXiv2026

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

Recurrent detectors such as bidirectional long short-term memory (Bi-LSTM) networks are low-complexity alternatives to the optimal Bahl-Cocke-Jelinek-Raviv (BCJR) detector for faster-than-Nyquist (FTN) signaling. Motivated by convolutional detectors that build the intersymbol interference (ISI) structure into their architecture, we ask whether processing nested ISI windows in separate recurrent branches improves the bit error rate (BER) of a Bi-LSTM. Across roughly 260 controlled trainings it does not: at a matched parameter budget and a matched readout, the multi-window architecture never significantly beats a plain Bi-LSTM. Nested windowing is an invertible rearrangement that adds no information, extra branches only add bottlenecks, and a distillation diagnostic shows the network is already near optimal for its window. The limitation is therefore the observation model, not the architecture. Keeping the architecture fixed, we pre-whiten the input, restoring the conditional independence that colored matched-filter noise violates, and distill the BCJR soft posterior into the network. With 3.4% more parameters this reaches 1.05 times the BCJR BER at a compression factor of 0.8 and 1.89 times at 0.7, improving to 1.47 times when the whitened window is widened. The 23.7% BER reduction at 0.8 requires an ill-conditioned ISI matrix but is not monotone in the conditioning, and it holds across five independent noise realizations and a symbol-level McNemar test.

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arXiv2026

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, which contains no gating mechanism, is a sufficient and parameter-efficient model. The proposed detector processes the received sequence in both forward and backward directions in a single pass, forming a learned counterpart of the optimal BCJR forward-backward recursion. Under a root-raised-cosine pulse over an additive white Gaussian noise channel, simulations for two compression factors show that the proposed detector, with only twenty-five to sixty-five trainable parameters, attains a bit error rate very close to that of the M-BCJR algorithm, while reducing the look-up-table hardware cost by thirty-eight to forty-six percent and using no explicit division or exponential operations. A more compact configuration offers up to a sixty-seven percent reduction at a small performance penalty. To the best of our knowledge, this is the first study to investigate the classical Elman recurrent neural network architecture for the faster-than-Nyquist detection problem.

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arXiv2026

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition

Automatic RF modulation recognition is of critical importance in spectrum monitoring, electronic warfare, and cognitive radio applications, where low signal-to-noise ratio (SNR) conditions and the growing diversity of modulation schemes limit the performance of existing methods. This paper proposes an uncertainty-driven hybrid deep learning architecture for recognizing RF signals over a broad modulation space. The proposed approach carries out a multi-stage classification process by combining spectral information obtained through low-cost FFT-based preprocessing with time-frequency features extracted from short-time Fourier transform (STFT) spectrograms. The architecture comprises a 2D convolutional neural network (2D CNN)-based path for fast, low-latency primary classification, MC Dropout-supported Bayesian uncertainty estimation for assessing classification reliability, and a BiLSTM-based secondary decision mechanism activated under high-uncertainty conditions. The proposed system is evaluated in a controlled simulation environment spanning different SNR levels and modulation classes. Experimental results show that the primary 2D CNN path achieves $83.3\pm0.7\%$ accuracy with an inference time of only 0.138 ms per sample, providing superior performance compared with traditional rule-based and classical machine-learning approaches. Furthermore, the obtained findings reveal the limitations of compact spectral feature representations and classifiers lacking temporal modeling, particularly in disambiguating FSK-based modulations. The uncertainty estimation module offers promising results for detecting low-confidence decisions, and the proposed approach demonstrates the potential of a low-latency and scalable solution for real-time RF modulation recognition.

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arXiv2026

Self-Attention Transformer-Based Detector for Faster-than-Nyquist Signaling

In this study, a novel encoder-only Transformer-based receiver architecture is presented for BPSK signals transmitted over Faster-than-Nyquist (FTN) signaling channels that introduce intentional inter-symbol interference (ISI) with a compression factor of $τ=0.8$. A complete end-to-end communication chain encompassing BPSK modulation, RRC pulse shaping, and the ISI coefficients arising from matched filtering was constructed and evaluated. The proposed Transformer receiver was benchmarked against the optimal BCJR detector over an $E_b/N_0$ range of 0-8 dB. To systematically close the BER gap to the BCJR, a two-stage training strategy combining multi-SNR pretraining and per-SNR curriculum fine-tuning was developed. The computational complexity and inference latency of the Transformer receiver were analyzed in comparison with a GRU based receiver. Attention map visualizations revealed that the Transformer autonomously identifies the FTN-induced ISI memory structure without requiring any prior channel knowledge; as the SNR increases, the attention weights become significantly concentrated around the center token and its nearest neighbors.

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