arXiv subjects

Osman Tokluoglu

Publications and source records attributed to Osman Tokluoglu.

10 recordsLinked to original sources

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

A Comparative Analysis of MLP and Kolmogorov-Arnold Networks (KAN) for Faster-than-Nyquist (FTN) Signaling Detection

Faster-than-Nyquist signaling improves spectral ef- ficiency by deliberately introducing inter-symbol interference. Classical sequence detectors such as BCJR can approach optimal performance, but their computational cost grows rapidly with channel memory. This paper investigates data-driven FTN BPSK detection under AWGN through a direct comparison between multilayer perceptrons and Kolmogorov Arnold Networks. A large-scale Monte Carlo dataset containing nearly four million labeled windows is generated for a time-packing factor of zero point eight and signal-to-noise ratio values from seven to ten decibels. The best MLP obtained from width sweeping uses hidden width thirty two, whereas the selected KAN uses hidden width four with spline grid size five. At ten decibels, the MLP produces a bit error rate of one point three times ten to the minus four, while the KAN reaches seven times ten to the minus six. This corresponds to an eighteen point six times lower bit error rate while using only one eighth of the MLP hidden width. The results show that KAN provides a more effective and more parameter-efficient neural decision model than the MLP baseline for FTN BPSK detection.

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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 $\tau=0.8$. 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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arXiv2026

Delta-Position Estimation-Based IMU Odometry: A Comparison of MLP and Kolmogorov-Arnold Networks

In this study, the learning-based inertial odometry problem is investigated using raw IMU measurements obtained from the EuRoC MAV benchmark dataset. Instead of absolute position regression-a formulation that may lead to large constant errors-the models are trained to estimate the incremental displacement ({\Delta}p) over a fixed 50 ms sliding window, and the full trajectory is reconstructed through numerical integration. A standard Multi-Layer Perceptron (MLP) is compared with a Kolmogorov-Arnold Network (KAN) equipped with learnable B-spline activations. Although KAN has 6.9 times fewer parameters than MLP (8,444 versus 57,859), it produces a 44% lower error in terms of final cumulative drift on the test trajectory (9.61 m versus 17.23 m). In addition, KAN exhibits more stable behavior in terms of long-term error accumulation, with lower P_50 and P_90 cumulative drift values. These findings indicate that learnable B-spline-based activations have the potential to reduce error accumulation in the inertial odometry problem.

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arXiv2026

Deep Learning-Based Automatic Modulation Classification Using GRU Networks

Automatic modulation classification (AMC) plays a critical role in modern wireless communication systems, particularly in non-cooperative scenarios where prior knowledge of the transmitted signal is unavailable. In this study, a gated recurrent unit (GRU)-based deep learning framework is investigated for the classification of digital modulation schemes by exploiting the temporal characteristics of received signals. The proposed approach operates directly on in-phase and quadrature (I/Q) signal representations and aims to learn discriminative features in a data-driven manner without relying on handcrafted feature extraction. The performance of the proposed model is evaluated for BPSK, QPSK, and 16PSK modulation schemes under additive white Gaussian noise (AWGN) channel conditions across a wide range of signal-to-noise ratio (SNR) levels. The obtained results demonstrate that the GRU-based model achieves reliable classification performance, with overall accuracy improving from 55.3% at -10 dB SNR to 98.5% at 15 dB SNR. In particular, the model exhibits strong performance at moderate and high SNR levels, while maintaining reasonable accuracy even under challenging low SNR conditions. These findings suggest that GRU-based architectures provide a promising and computationally efficient solution for modulation classification tasks. The presented results represent an initial step toward more comprehensive studies, including extensions to fading channel environments, additional modulation schemes, and real-time implementations using hardware platforms.

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arXiv2026

Comparative Performance Analysis of NIST PQC Standards: From STM32 Software Limitations to FPGA-SoC Acceleration

The rapid advancement of quantum computing poses a significant threat to classical public-key cryptographic systems, necessitating the transition to Post-Quantum Cryptography (PQC). This study investigates the implementation challenges of NISTstandardized signature schemes on resource-constrained embedded hardware. We present a comparative analysis of SPHINCS+ and CRYSTALS-Dilithium on an ARM Cortex-M4 (STM32F407G) microcontroller. Our findings reveal that SPHINCS+ is practically unusable in this software-only environment, with impractical execution times. Furthermore, the reference Dilithium implementation failed to execute entirely on the MCU due to severe RAM and timing constraints. To overcome these hardware limitations, we integrated a hardware-accelerated Dilithium core onto a Xilinx Zynq-7000 ZedBoard SoC. By implementing a specialized Number Theoretic Transform (NTT) accelerator in the FPGA fabric, we achieved successful execution with performance rates for key generation and signature generation at millisecond levels. These results demonstrate that while pure software PQC is non-viable for standard microcontrollers, a hardware-software codesign approach provides the necessary efficiency for quantumresistant embedded systems.

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arXiv2026

Compact Convolutional Segmentation for Visual Landmark Extraction in GNSS-Denied UAV Navigation

Reliable localization of unmanned aerial vehicles (UAVs) becomes challenging when Global Navigation Satellite System (GNSS) signals are degraded, blocked, or intentionally jammed. In such GNSS-denied conditions, visual information obtained from onboard cameras can provide complementary cues for navigation by identifying spatially stable and distinc tive landmarks. This study proposes a compact convolutional segmentation framework for extracting candidate visual land marks from aerial imagery. The proposed model combines fully convolutional processing with dilation-based spatial con text extraction and residual feature transfer. Since a dedicated UAV landmark dataset is not available in this study, an aerial building segmentation dataset is adapted as an initial evaluation environment. Experimental results indicate that the proposed architecture provides a feasible front-end for candidate landmark extraction, while further improvements are required through extended training, UAV-specific datasets, and integration with localization or matching algorithms.

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arXiv2025

A Novel CNN Based Standalone Detector for Faster-than-Nyquist Signaling

This paper presents a novel convolutional neural network (CNN)-based detector for faster-than-Nyquist (FTN) signaling, introducing structured fixed kernel layers with domain-informed masking to effectively mitigate intersymbol interference (ISI). Unlike standard CNN architectures that rely on moving kernels, the proposed approach employs fixed convolutional kernels at predefined positions to explicitly learn ISI patterns at varying distances from the central symbol. To enhance feature extraction, a hierarchical filter allocation strategy is employed, assigning more filters to earlier layers for stronger ISI components and fewer to later layers for weaker components. This structured design improves feature representation, eliminates redundant computations, and enhances detection accuracy while maintaining computational efficiency. Simulation results demonstrate that the proposed detector achieves near-optimal bit error rate (BER) performance, comparable to the BCJR algorithm for the compression factor $\tau \geq 0.7$, while offering up to $46\%$ and $84\%$ computational cost reduction over M-BCJR for BPSK and QPSK, respectively. Additional evaluations confirm the method's adaptability to high-order modulations (up to 64-QAM), resilience in quasi-static multipath Rayleigh fading channels, and effectiveness under LDPC-coded FTN transmission, highlighting its robustness and practicality.

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arXiv2025

A Novel Domain-Aware CNN Architecture for Faster-than-Nyquist Signaling Detection

This paper proposes a convolutional neural network (CNN)-based detector for faster-than-Nyquist (FTN) signaling that employs structured fixed kernel layers with domain-informed masking to mitigate intersymbol interference (ISI). Unlike standard CNNs with sliding kernels, the proposed method utilizes fixed-position kernels to directly capture ISI effects at varying distances from the central symbol. A hierarchical filter allocation strategy is also introduced, assigning more filters to earlier layers for strong ISI patterns and fewer to later layers for weaker ones. This design improves detection accuracy while reducing redundant operations. Simulation results show that the detector achieves near-optimal bit error rate (BER) performance for $\tau \geq 0.7$, closely matching the BCJR algorithm, and offers computational gains of up to $46\%$ and $84\%$ over M-BCJR for BPSK and QPSK, respectively. Comparative analysis with other methods further highlights the efficiency and effectiveness of the proposed approach. To the best of our knowledge, this is the first application of a fixed-kernel CNN architecture tailored for FTN detection in the literature.

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