Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Secrecy performance of full-duplex space-air integrated networks in the presence of active/passive eavesdropper, and friendly jammer(Wiley, 2024) Buyuksar, Ayse Betul; Erdoğan, Eylem; Altunbas, IbrahimIn this paper, a full-duplex (FD) space-air ground integrated network (SAGIN) system with passive and active eavesdroppers (PE/AE) and a friendly jammer (FJ) is investigated. The shadowing side information (SSI)-based unmanned aerial vehicle relay node (URN) selection strategy is considered to improve signal-to-interference plus noise power ratio (SINR) at the ground destination unit. To quantify the secrecy performance of the considered scenario, outage probability (OP), interception probability (IP), and transmission secrecy outage probability (TSOP) are investigated in the presence of FJ and PE/AE. The results have shown that aerial AE is an important threat since it can severely degrade the OP of the main transmission link. Furthermore, the FJ can decrease the IP of the eavesdropper by causing interference with the cost of power consumption of URNs. Simulations are performed to verify the theoretical findings.Article Citation - WoS: 3Citation - Scopus: 4Liquid Metal-Controlled Dual-Band Doppler Radar for Enhanced Velocity Measurement(IEEE, 2024) Karatay, Anıl; Yaman, FatihDoppler radars, which are critical instruments for velocity measurement, may need to be reconfigured to adapt to different environmental conditions or for ease of use. However, conventional electrical, optical, and physical reconfiguration methods often come with several disadvantages such as deteriorated radiation pattern, reduced radiation efficiency, and high cost. Therefore, the aim of this article is to integrate microwave components that can be controlled using liquid metal (LM) displacement into a Doppler radar to adjust its main lobe direction and operating frequency to the desired values and enhance the measurement capacity of the respective radar. Through this study, multiple parameters of an operational Doppler radar have been simultaneously adjusted using LM displacement exploitation for the first time, thus avoiding the shortcomings associated with conventional reconfiguration methods. To achieve this objective, initially, a back-to-back Vivaldi antenna operating at 2.45 GHz is designed, and beam switching ability is imparted to the structure using the LM displacement method. Subsequently, various techniques are used to convert the structure into a dual-band antenna capable of simultaneous operation at 2.45 and 5.8 GHz, ensuring the desired beam switching feature at both the frequencies. In addition, a power divider capable of switching between the two operating frequencies through LM assistance is proposed, and its integration into the radar system enables the control of both main lobe direction and frequency using the proposed method.Article Citation - WoS: 27Citation - Scopus: 34A New Method for Gan-Based Data Augmentation for Classes With Distinct Clusters(Pergamon-Elsevier Science Ltd, 2024) Kuntalp, Mehmet; Düzyel, OkanData augmentation is a commonly used approach for addressing the issue of limited data availability in machine learning. There are various methods available, including classical and modern techniques. However, when applying modern data augmentation methods, such as Generative Adversarial Neural Networks (GANs), to a class specific data, the resulting data can exhibit structural discrepancies. This study explores a different use of GANs as a data augmentation method that solves this problem using the electrocardiogram (ECG) signals in the MITBIH arrhythmia dataset as the example. We begin by examining the cluster structure of a specific class using tDistributed Stochastic Neighbor (t-SNE) method. Based on this cluster structure, we propose a new method for applying GANs to augment data for that class. We assess the effect of our method in a classification task using 1-D Convolutional Neural Network (CNN), Support Vector Machine (SVM), One vs one classifier (Ovo), K-Nearest Neighbors (KNN), and Random Forest as the classifiers. The results demonstrate that our proposed method could lead to better classification performance if a specific class has distinct clusters when compared to normal use of GANs.Correction Corrections To “massive Mimo-Noma Based Mec in Task Offloading for Delay Minimization”(IEEE, 2023) Yılmaz, Saadet Simay; Özbek, Berna[No abstract available]Article Citation - WoS: 2Citation - Scopus: 2A Non-Resonant Approach for Dielectric Constant Reconstructions Via Newton Iterations(Elsevier, 2023) Özkal, Ceren; Yaman, FatihIn this study, a Newton–Raphson-based iterative method has been proposed to obtain dielectric constants accurately from measurements. The originalities of the approach lie in its applicability at non-resonant frequencies, which brings a significant experimental simplicity by avoiding critical coupling, expansion of available frequencies in different bands with the same cost-efficient low-Q (?60) cavity. The direct problem involves either measuring power values inside a cavity (14.6 × 5 × 20.6) cm via a spectrum analyzer or simulating the complete setup via CST-MWS software at one of the non-resonant modes, 1.5 GHz. The solution to the inverse problem provides fastly converging results with an error rate of 1% for the unknown permittivities. The experiments were carried out using five different liquid samples even though the proposed technique does not have a limitation on solid materials. Applicability and the effectiveness of the introduced method is illustrated in detail and comparisons with the perturbation method is provided. © 2023 Elsevier GmbHArticle Citation - WoS: 6Citation - Scopus: 7Time-Resolved Eeg Signal Analysis for Motor Imagery Activity Recognition(Elsevier, 2023) Olcay, Bilal Orkan; Karaçalı, BilgeAccurately characterizing brain activity requires detailed feature analysis in the temporal, spatial, and spectral domains. While previous research has proposed various spatial and spectral feature extraction methods to distinguish between different cognitive tasks, temporal feature analysis for each separate brain region and frequency band has been largely overlooked. This study introduces two novel approaches for recognizing cognitive activity: temporal entropic profiling and time-aligned common spatio-spectral patterns analysis. These approaches capture and use discriminative short-lived signal segments for motor imagery activity recognition. In Approach-1, we evaluated nine different measures to determine timing parameters that showed altered behavior associated with maximal inter-activity differences, which we then used in a machine-learning framework. In Approach-2, we used the best-performing signal characteristic measures from Approach-1 to determine the optimum latency of each channel at each frequency band for a CSP-based activity recognition strategy. We evaluated both approaches on two online available motor imagery EEG datasets and achieved average recognition accuracy levels of 86%. We compared our methods with four established BCI methods. The performance results show that our approaches exceeded the benchmark methods' performances, with notable improvements in the proposed time-aligned common spatio-spectral patterns approach. This study demonstrates that motor imagery recognition performance is improved when a temporal analysis is adopted alongside spatio-spectral neural feature analysis and that timing parameters associated with the maximal entropic difference of EEG segments to the cognitive tasks varied between different brain regions and subjects. © 2023 Elsevier LtdArticle Citation - WoS: 15Citation - Scopus: 17Massive Mimo-Noma Based Mec in Task Offloading for Delay Minimization(IEEE, 2023) Yilmaz, Saadet Simay; Özbek, BernaMobile edge computing (MEC) has been considered a promising technology to reduce task offloading and computing delay by enabling mobile devices to offload their computation-intensive tasks. Non-orthogonal multiple access (NOMA) is regarded as a promising method of increasing spectrum efficiency, while Massive multiple-input multiple-output (MIMO) can support a larger number of users for simultaneous offloading. These two technologies can effectively facilitate offloading and further improve the performance of MEC systems. In this work, we propose a NOMA and Massive MIMO assisted MEC system for delay-sensitive applications. Our objective is to minimize the overall computing and transmission delay under users' transmit power and MEC computing capability. Through the pairing scheme for Massive MIMO-NOMA, the users with the higher channel gain can offload all their data, while the users with the lower channel gain can offload a portion of their data to the MEC. Performance results are provided regarding to the sum data rate and overall system delay compared with the orthogonal multiple access (OMA)-MIMO based and Massive MIMO (M-MIMO) based MEC systems.Article Citation - WoS: 10Citation - Scopus: 13User Selection and Codebook Design for Noma-Based High Altitude Platform Station (haps) Communications(IEEE, 2022) Cumalı, İrem; Özbek, Berna; Karabulut Kurt, Güneş; Yanıkömeroğlu, HalimHigh altitude platform station (HAPS) communications have made a tremendous impact on recent research into sixth-generation (6G) and beyond wireless networks. The large coverage area and significant computational capability of HAPS systems enable many areas of utilization in 6G and beyond applications, including Internet of Things (IoT) services, augmented reality, and connected autonomous vehicles. In addition, non-orthogonal multiple access (NOMA) is a cutting-edge technology that can be utilized to enhance spectral efficiency in HAPS systems. In this paper, we exploit NOMA-based HAPS communications and multiple antennas to meet the connectivity, reliability, and high-data-rate requirements of 6G and beyond applications. We propose a user selection and correlation-based user pairing algorithm for a NOMA-based multi-user HAPS system. Moreover, we investigate the codebook design for HAPS communication and adapt the polar-cap codebook (PCC) to the HAPS channel which shows Rician fading propagation characteristics dominated by the line-of-sight (LOS) component. Performance evaluations show that the proposed user selection algorithm is perfectly suited to the HAPS channel and that the PCC provides a remarkable spectral efficiency.Article Citation - WoS: 8Citation - Scopus: 11Delay Minimization for Massive Mimo Based Cooperative Mobile Edge Computing System With Secure Offloading(IEEE, 2022) Mümtaz, Rao; Yılmaz, Simay; Özbek, BernaMobile edge computing (MEC) has been envisioned as a promising technology for enhancing the computational capacities of mobile devices by enabling task offloading. In this paper, we present a novel framework for a cooperative MEC system by employing Massive Multiple-Input Multiple-Output (MIMO) and non-orthogonal multiple access (NOMA) technologies, including security aspects. Specifically, in the proposed cooperative MEC system, there is no strong direct transmission link between the cell-edge user and the MEC server; consequently, the user sends their tasks to the MEC server through the helpers at the cell-centers. In the proposed framework, we minimize the overall delay, including secure offloading under the constraints of computing capability and transmit power. The proposed algorithm minimizes the overall delay in downlink and uplink transmission while satisfying security constraints to solve the formulated problem. The simulation results show that Massive MIMO based NOMA improves the performance of the secure MEC system by employing more than one helper.Article Citation - WoS: 22Citation - Scopus: 26Adaptive Sign Algorithm for Graph Signal Processing(Elsevier, 2022) Yan, Yi; Kuruoğlu, Ercan Engin; Altınkaya, Mustafa AzizEfficient and robust online processing techniques for irregularly structured data are crucial in the current era of data abundance. In this paper, we propose a graph/network version of the classical adaptive Sign algorithm for online graph signal estimation under impulsive noise. The recently introduced graph adaptive least mean squares algorithm is unstable under non-Gaussian impulsive noise and has high computational complexity. The Graph-Sign algorithm proposed in this work is based on the minimum dispersion criterion and therefore impulsive noise does not hinder its estimation quality. Unlike the recently proposed graph adaptive least mean pth power algorithm, our Graph-Sign algorithm can operate without prior knowledge of the noise distribution. The proposed Graph-Sign algorithm has a faster run time because of its low computational complexity compared to the existing adaptive graph signal processing algorithms. Experimenting on steady-state and time-varying graph signals estimation utilizing spectral properties of bandlimitedness and sampling, the Graph-Sign algorithm demonstrates fast, stable, and robust graph signal estimation performance under impulsive noise modeled by alpha stable, Cauchy, Student's t, or Laplace distributions.