Browsing by Author "Durak, Mehmet Hakan"

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Deep Learning Based Automatic Modulation Recognition Using GELU Activation Function
(IEEE, 2024) Altuntas, Bilge Erdem; Aksu, Omer; Celik, Melih Yigitcan; Durak, Mehmet Hakan
One promising technique for detecting signal modulation schemes in cognitive radio networks is automatic modulation recognition (AMR). AMR based on highperformance deep learning (DL) techniques have been made easier recently by the growing research on DL. But, as DL is evolving daily, AMR approaches must perform better, and new approaches must be developed. This research presents a new DL based technique for AMR used in modern communication systems' cognitive radio networks. To simultaneously learn the spatio-temporal signal correlations with Gaussian Error Linear Unit (GELU) activation function, the network architecture is built with multiple distinct convolutional blocks. The suggested technique achieves an overall 6-modulation classification rate of 80% at 20 dB SNR in the simulations performed with the generated dataset.
Delta Optimization for Event-Based Sampling in Goal-Oriented Communication Systems
(IEEE, 2025) Durak, Mehmet Hakan; Alves, Hirley
Goal-oriented communication (GOC) has emerged as a promising paradigm in modern wireless systems, emphasizing transmitting information relevant to the goal rather than the raw data itself. This study investigates event-based sampling methods, specifically the Send-on-Delta (SOD) and Send-on-Delta with Linear Prediction (SODwLP) algorithms, to explore the trade-off between normalized mean square error (NMSE) and the number of samples (NoS) in resourceconstrained scenarios. While SOD achieves fewer transmitted samples compared to SODwLP, it exhibits higher NMSE, making it less favorable in terms of signal reconstruction accuracy. Through a grid search optimization strategy, we demonstrated that SODwLP achieves a lower overall cost at equivalent delta values and consistently selects larger optimal delta thresholds across various weight configurations, balancing communication efficiency and accuracy more effectively. These results highlight the adaptability of SODwLP for GOC systems, where specific priorities such as accuracy or energy efficiency can significantly influence the choice of sampling parameters. Future research could extend these findings by employing advanced optimization techniques and evaluating real-world applications in IoT and 6G networks to enhance the practical relevance of these algorithms.
Dynamic and Sparsity Adaptive Compressed Sensing Based Active User Detection and Channel Estimation of Uplink Grant-Free Scma
(Spolecnost Pro Radioelektronicke Inzenyrstvi, 2021) Durak, Mehmet Hakan; Ertug, Ozgur
In uplink (UL) grant-free sparse code multiple access (SCMA) systems, unlike the conventional contention-based transmission, users' activities should be known be-fore data decoding due to sporadic transmission in massive machine-type communication (mMTC). Since compressed sensing (CS) is the theory of sparse signal reconstruction with fewer samples, this theory is a good solution to de-tect active users. In this paper, we propose the dynamic and sparsity adaptive compressed sensing (DSACS) based active user detection (AUD) and channel estimation (CE) of UL grant-free SCMA. Unlike most of the CS-based methods, sparsity knowledge or potential active user list is not needed in the proposed algorithm, which is already not known in the practical systems. The proposed algorithm adopts a stage-wise approach to expand the set of accurate active users for adaptively achieve the sparsity level. It uses the temporal correlation of users' activity to improve performance and re-duce complexity. Then, false detected users are eliminated with joint message passing algorithm (JMPA), and channel gains of the accurate active users are estimated again in CE with feedback. The simulation results show that the proposed method without sparsity knowledge is capable of achieving detection in various scenarios in case of sporadic transmis-sion in mMTC.
A Survey on Optimization Methods Used in Power Electronics
(Springer, 2025) Duman, Turgay; Durak, Mehmet Hakan; Tutam, Mahmut
Power electronics is integral to nearly every aspect of modern life, with applications spanning from small-scale devices like electric toothbrushes, wireless earbuds, and cell phone chargers to large-scale systems such as electric grids, renewable energy systems, and industrial automation. Alongside the unique challenges posed by these applications, advancements in key areas of power electronics have brought about additional complexities. As a result, optimization has become an essential tool in addressing these complexities for improving the performance, efficiency, and reliability of power systems. This article provides a comprehensive overview of both conventional and artificial intelligence based optimization methods, applied to key areas of power electronics, such as power factor correction, harmonic elimination, maximum power point tracking, switching methods, and design methodologies, highlighting their role in achieving diverse objectives. This analysis is critical for uncovering limitations in previous research and highlighting areas where the current literature is lacking. This paper conducts a systematic and in-depth examination of objectives, topologies, and optimization techniques, offering valuable insights for future research in the field. Our findings reveal that some factors have been thoroughly investigated using diverse optimization methods, whereas others, despite their frequent use in practice, have received limited attention in academic studies.