Browsing by Author "Baykas, Tuncer"

Now showing 1 - 16 of 16

Citation Count: 1
A 130 nm CMOS Receiver for Visible Light Communication
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Baykaş, Tunçer; Yagan, Muhammed Yaser; Uysal, Murat; Pusane, Ali Emre; Baykas, Tuncer; Dundar, Gunhan; Yalcinkaya, Arda Deniz
Visible light communication (VLC) is an emerging technology that has been gaining attention over the last few years. Transmission of data at higher rates in a VLC system is mainly limited by the modulation bandwidth of the employed LED. To alleviate this limitation, equalization is frequently employed. This is usually achieved by either using discrete circuit elements or in digital form. In this paper, we present a power-efficient VLC receiver as a system-on-chip, implemented in 130 nm CMOS technology. The proposed receiver supports LEDs with different bandwidths thanks to the switchable equalizer. We tested the proposed receiver using phosphorescent white LEDs with different bandwidths on an experimental VLC link. For each tested LED, around 20 fold improvement in data rate was achieved compared to the original bandwidth of the LED. For the LED with a modulation bandwidth of 1.6 MHz, data rates of 32 Mbps and 50 Mbps at a BER of 10(-2) were obtained at a distance of 2 meters without and with a blue filter, respectively.
Citation Count: 0
Analysis and Optimization of the Network Throughput in IEEE 802.15.13 based Visible Light Communication Networks
(IEEE, 2021) Baykaş, Tunçer; Elamassie, Mohammed; Baykas, Tuncer; Uysal, Murat
In line with the growing interest on visible light communication (VLC), IEEE has initiated standardization efforts on this emerging technology. In this work, we consider IEEE 802.15.13 Optical Wireless Personal Area Networks (OWPAN) standard draft. The underlying MAC protocol uses contention free and contention access periods. For a standard-compliant VLC network, we analyze the network load and propose an algorithm to improve the network throughput by proper selection of period lengths. Our suggested algorithm improves the network performance by at least 5% in the case of variable network traffic up to 15 active users.
Citation Count: 1
Analysis of deep learning based path loss prediction from satellite images
(IEEE, 2021) Baykaş, Tunçer; Ates, Hasan F.; Baykas, Tuncer; Gunturk, Bahadir K.
Determining the channel model parameters of a wireless communication system, either by measurements or by running electromagnetic propagation simulations, is a time-consuming process. Any rapid deployment of network demands faster determination of at least major channel parameters. In this paper, we investigate the idea of using deep convolutional neural networks and satellite images for channel parameters (i.e., path loss exponent n and shadowing factor sigma) prediction in a cellular network with aerial base stations. Specifically, we investigate the performance dependency of the method on three different factors: height of the transmitter antenna, quantization levels of the channel parameters and architectural design of CNN. The results presented in this paper show a high prediction accuracy of the channel parameters in real-time.
Citation Count: 1
Busy Tone Based Power Control for Coordination of IFFY 802.11af and 802.22 System
(IEEE, 2017) Baykaş, Tunçer; Erküçük, Serhat; Karatalay, Onur; Baykas, Tuncer
In this paper, a new power control algorithm based on busy tone approach has been proposed for the coordination of IEEE 802.22 and IEEE 802.11af systems in TV white space. Different from the earlier studies, in addition to both 802.11af access point and clients listening to the busy tone, they also adjust their communication power according to the location information and use hopping for communication, if needed Acccordingly, interference caused to 802.22 systems has been reduced while the 802.11af systems are still able to communicate. This study quantifies the 802.11af and 802.22 system performances in terms of interfering packet rate and succesful packet transmission rate for different scenarios considering the communication parameters and channel models adapted for the standards.
Citation Count: 0
A comparative analysis of diversity combining techniques for repetitive transmissions in time spreading SCMA systems
(John Wiley & Sons Ltd, 2024) Şadi, Yalçın; Erküçük, Serhat; Baykaş, Tunçer; Erkucuk, Serhat; Anpalagan, Alagan; Baykas, Tuncer
Sparse Code Multiple Access (SCMA) is a recently introduced wireless communication network technology. There are various techniques in SCMA systems to increase the system's efficiency, and one of these techniques is time spreading. By adding repetitive transmission and time spreading into SCMA, it is shown in previous works that the Bit-Error-Rate (BER) results are improved convincingly. However, in the previous works, other diversity combining techniques have not been considered. This paper introduces a new approach to further improve the performance of repetitive transmission in SCMA systems with time spreading by adding imperialist competitive algorithm in diversity combining. Alongside, four different combining techniques; equal gain combining, maximal ratio combining, selection combining, and genetic algorithm are considered to bring comparative analysis to show the significance of the new technique. Results show that the proposed method offers up to 2.3 dB gain in terms of BER, under certain conditions.
Citation Count: 0
Comparative Performance Evaluation of VLC, LTE and WLAN Technologies in Indoor Environments
(IEEE, 2021) Baykaş, Tunçer; Karbalayghareh, Mehdi; Miramirkhani, Farshad; Uysal, Murat; Baykas, Tuncer
Recent years have seen an exponential rise in the demand for indoor wireless connections that have driven future generation networks to aim for higher data rates with extended coverage and affordable rates. The two most prominent technologies for providing indoor wireless connections, WLAN and LTE, have their limitations and they can not coexist in a single band to form heterogeneous networks (HetNets). Visible light communication (VLC) has seen rapid growth in recent years as it has the capability to seamlessly merge with the existing technologies and provide wireless connections with high data rates. VLC based hybrid indoor network effectively combines the preferences of an end-user with the practicality of implementation. In this work, we investigate specific VLC/WLAN and VLC/LTE hybrid scenarios to perform a detailed analysis on the effect of user mobility on the performance of the system and how the performance of the network (in terms of throughput) can be maximized The study aims to show how different technologies complement each other in the best and even the worst-case scenarios.
Citation Count: 0
Decoding compositional complexity: Identifying composers using a model fusion-based approach with nonlinear signal processing and chaotic dynamics
(Pergamon-elsevier Science Ltd, 2024) Baykaş, Tunçer; Hekimoğlu, Mustafa; Hekimoglu, Mustafa; Pekcan, Onder; Tuncay, Gonul Pacaci
Music, a universal medium that effortlessly transcends the confines of language and culture, serves as a vessel for the distinctive expression of a composer's ingenuity, particularly palpable through the elaborate symphony of melodies, harmonies, and rhythms. This phenomenon is acutely observable in the realm of Turkish Classical Music, where the identification of individual composers poses a formidable challenge due to a confluence of diverse stylistic expressions and sophisticated techniques. Shaped by centuries of cultural interchanges, this genre is celebrated for its convoluted rhythmic frameworks and deep melodic modes, often exhibiting fractal characteristics that compound the complexity of composer classification based on mere audio signals. In response to these complexities, this study introduces an advanced analytical paradigm that amalgamates Multi-resolution analysis, spectral entropy assessments, and a spectrum of multidimensional chaotic and statistical descriptors. By invoking chaos theory, the research delineates distinct patterns and features inherent to musical compositions, subsequently deploying these discoveries for composer categorization. Employing a model fusion-based strategy, the approach utilizes esteemed base estimators for section-level probabilistic determinations, subsequently amalgamated at the song level through a Long Short-Term Memory (LSTM) neural network model to classify a corpus of 380 compositions from 15 distinct composers. The results of this study not only highlight the efficacy of chaos-based approaches in Musical Information Retrieval but also provide a nuanced understanding of the unique characteristics of Turkish Classical Music, thus advancing the boundaries of how musicological data is scrutinized and conceptualized within scholarly discourse.
Citation Count: 112
IEEE 802.15.7r1 Reference Channel Models for Visible Light Communications
(IEEE-Inst Electrical Electronics Engineers Inc, 2017) Baykaş, Tunçer; Panayırcı, Erdal; Baykas, Tuncer; Uysal, Murat; Panayırcı, Erdal
The IEEE has established the standardization group 802.15.7r1 "Short Range Optical Wireless Communications", which is currently in the process of developing a standard for visible light communication (VLC). As with any other communication system, realistic channel models are of critical importance for VLC system design, performance evaluation, and testing. This article presents the reference channel models that were endorsed by the IEEE 802.15.7r1 Task Group for evaluation of VLC system proposals. These were developed for typical indoor environments, including home, office, and manufacturing cells. While highlighting the channel models, we further discuss physical layer techniques potentially considered for IEEE 802.15.7r1.
Citation Count: 0
An Integrated Molecular Communication System Based on Acoustic Tweezers
(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Baykaş, Tunçer; Pusane, Ali Emre; Baykas, Tuncer
In this work, a molecular communication link integrated with a micro-electro mechanical system (MEMS) based environment has been designed and simulated. The motivation behind this approach is to explore the possibility of merging acoustic tweezing technique with a molecular communication system to increase the accuracy and reliability of the overall communication link. The proposed design is simulated using finite element methods that mimic the actual environment for an accurate solution. We derive symbol error rate as a performance metric and further show that the proposed system outperforms the diffusion-based modulation techniques and facilitates a reliable communication in the presence of fluid flow and while being insusceptible to external factors.
Citation Count: 8
Location Aware Vertical Handover in a VLC/WLAN Hybrid Network
(IEEE-Inst Electrical Electronics Engineers Inc, 2021) Baykaş, Tunçer; Baykas, Tuncer
Visible light communication (VLC) has emerged as a promising technology for wireless communication as it offers higher data rates and secure data transmission along with providing indoor illumination. However, VLC is restricted by the line of sight (LoS) nature of the optical channel that consequently results in light path blockages. Therefore, an effective solution would be to combine VLC with a radio frequency (RF) system to form a hybrid VLC/RF network that would take into account the preferences of an end-user with the practicality of implementation. In such networks, an efficient vertical handover (VHO) technique is the most critical element as it ensures a seamless transition between the two networks. In this work, we propose a vertical handover technique that utilizes the user's location information to make a handover decision. We found that the frequency of light path blockages increases with the increasing number of users in a confined space, resulting in significant performance deterioration. This additional information is then utilized so that the VHO algorithm effectively selects the most feasible network. The proposed algorithm has been tested against the immediate vertical handover algorithm (I-VHO) and the dwell vertical handover algorithm (D-VHO) with two different dwell times. The average number of handovers, quality of experience (QoE), and packet loss have been set as performance metrics. We show from several simulation scenarios that the proposed method results in a fewer number of handovers while maintaining higher QoE and lower packet loss.
Citation Count: 6
Measurement-Based Large Scale Statistical Modeling of Air-to-Air Wireless UAV Channels via Novel Time-Frequency Analysis
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Baykaş, Tunçer; Kaplan, Batuhan; Kahraman, Ibrahim; Kesir, Samed; Yarkan, Serhan; Ekti, Ali Riza; Baykas, Tuncer
Any operation scenario for unmanned aerial vehicles also known as drones in real world requires resilient wireless link to guarantee capacity and performance for users, which can only be achieved by obtaining detailed knowledge about the propagation channel. Thus, this study investigates the large-scale channel propagation statistics for the line of sight air-to-air (A2A) drone communications to estimate the path loss exponent (PLE). We conducted a measurement campaign at 5.8 GHz, using low cost and light weight software defined radio based channel sounder which is developed in this study and then further integrated on commercially available drones. To determine the PLE, frequency-based, time-based and time-frequency based methods are utilized. Accuracy of the proposed method is verified under ideal conditions in a well-isolated anechoic chamber before the actual measurement campaign to verify the performance in a free space path loss environment. The path loss exponent for A2A wireless drone channel is estimated with these verified methods.
Citation Count: 0
Performance Evaluation of DTN Routing Protocols for Drone Swarms Using a Web-Based Simulator
(IEEE, 2023) Baykaş, Tunçer; Kizilirmak, Refik Caglar; Ukaegbu, Ikechi; Baykas, Tuncer
The growing interest in drone swarms and their applications using wireless communication has resulted in the necessity for innovative network architectures. Drone swarms are subject to highly dynamic network topology, and the low drone density may constrain connectivity and overall network performance. The Delay Tolerant Network (DTN) architecture, which is based on dynamic discovery of neighbouring nodes and relay of data via store-and-forward mechanism, overcomes these challenges. In this study, we develop a DTN simulator for drone swarms using web technologies that operate within the web browser. We employ two commonly used DTN routing algorithms, namely epidemic routing and spray-and-wait routing, for varying numbers of nodes, simulation durations, and maximum allowed copies in the network. We compare these protocols by evaluating performance metrics such as delivery probability, number of hops/copies, and delivery time, through a case study. Additionally, we investigate the influence of the outage probability of the air-to-air channel between nodes.
Citation Count: 0
PREDICTING PATH LOSS DISTRIBUTIONS OF A WIRELESS COMMUNICATION SYSTEM FOR MULTIPLE BASE STATION ALTITUDES FROM SATELLITE IMAGES
(Ieee, 2022) Baykaş, Tunçer; Gunturk, Bahadir K.; Ates, Hasan F.; Baykas, Tuncer
It is expected that unmanned aerial vehicles (UAVs) will play a vital role in future communication systems. Optimum positioning of UAVs, serving as base stations, can be done through extensive field measurements or ray tracing simulations when the 3D model of the region of interest is available. In this paper, we present an alternative approach to optimize UAV base station altitude for a region. The approach is based on deep learning; specifically, a 2D satellite image of the target region is input to a deep neural network to predict path loss distributions for different UAV altitudes. The neural network is designed and trained to produce multiple path loss distributions in a single inference; thus, it is not necessary to train a separate network for each altitude.
Citation Count: 7
Regression of Large-Scale Path Loss Parameters Using Deep Neural Networks
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Baykaş, Tunçer; Marey, Ahmed; Ates, Hasan F.; Baykas, Tuncer; Gunturk, Bahadir K.
Path loss exponent and shadowing factor are among important wireless channel parameters. These parameters can be estimated using field measurements or ray-tracing simulations, which are costly and time-consuming. In this letter, we take a deep neural network-based approach, which takes either satellite image or height map of a target region as input, and estimates the desired channel parameters. We use the well-known VGG-16 architecture, pretrained on the ImageNet dataset, as the backbone to extract image features, modify it as a regression network to produce channel parameters, and retrain it on our dataset, which consists of satellite image or height map as input and channel parameters as target values. We demonstrate that deep networks can be successfully utilized in estimating path loss exponent and shadowing factor of a region, simply from the region's satellite image or height map. The trained models and test codes are publicly available on a Github page.
Citation Count: 0
Residual LSTM neural network for time dependent consecutive pitch string recognition from spectrograms: a study on Turkish classical music makams
(Springer, 2023) Baykaş, Tunçer; Hekimoğlu, Mustafa; Baykas, Tuncer; Hekimoglu, Mustafa; Pekcan, Onder
Turkish classical music, characterized by 'makam', specific melodic configurations delineated by sequential pitches and intervals, is rich in cultural significance and poses a considerable challenge in identifying a musical piece's particular makam. This identification complexity remains an issue even for experienced musical experts, emphasizing the need for automated and accurate classification techniques. In response, we introduce a residual LSTM neural network model that classifies makams by leveraging the distinct sequential pitch patterns discerned within various audio segments over spectrogram-based inputs. This model's design uniquely merges the spatial capabilities of two-dimensional convolutional layers with the temporal understanding of one-dimensional convolutional and LSTM mechanisms embedded within a residual framework. Such an integrated approach allows for detailed temporal analysis of shifting frequencies, as revealed in logarithmically scaled spectrograms, and is adept at recognizing consecutive pitch patterns within segments. Employing stratified cross-validation on a comprehensive dataset encompassing 1154 pieces spanning 15 unique makams, we found that our model demonstrated an accuracy of 95.60% for a subset of 9 makams and 89.09% for all 15 makams. Our approach demonstrated consistent precision even when distinguishing makam pairs known for their closely related pitch sequences. To further validate our model's prowess, we conducted benchmark tests against established methodologies found in current literature, providing a comparative assessment of our proposed workflow's abilities.
Citation Count: 0
The use of statistical features for low-rate denial-of-service attack detection
(Springer int Publ Ag, 2024) Baykaş, Tunçer; Baykas, Tuncer; Anarim, Emin
Low-rate denial-of-service (LDoS) attacks can significantly reduce network performance. These attacks involve sending periodic high-intensity pulse data flows, sharing similar harmful effects with traditional DoS attacks. However, LDoS attacks have different attack modes, making detection particularly challenging. The high level of concealment associated with LDoS attacks makes them extremely difficult to identify using traditional DoS detection methods. In this paper, we explore the potential of using statistical features for LDoS attack detection. Our results demonstrate the promising performance of statistical features in detecting these attacks. Furthermore, through ANOVA, mutual information, RFE, and SHAP analysis, we find that entropy and L-moment-based features play a crucial role in LDoS attack detection. These findings provide valuable insights into utilizing statistical features enhancing network security, thereby improving the overall resilience and stability of networks against various types of attacks.