Researcher:
Kar, Emrah

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Emrah

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Kar

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2021 - 20224

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    Measurement based non-line-of-sight vehicular visible light communication channel characterization
    (Institute of Electrical and Electronics Engineers (IEEE), 2022) Narmanlıoğlu, Ömer; Uysal, Murat; Department of Electrical and Electronics Engineering; N/A; N/A; Department of Electrical and Electronics Engineering; Turan, Buğra; Koç, Osman Nuri; Kar, Emrah; Ergen, Sinem Çöleri; PhD Student; Other; Other; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; N/A; N/; A; College of Engineering; N/A; N/A; N/A; 7211
    Vehicular visible light comnumication (V-VLC) aims to provide secure complementary vehicle-to-everything-communications (V2X) to increase road safety and traffic efficiency. V-VLC provides directional transmissions, mainly enabling line-of-sight (LoS) communications. How ever, reflections due to nearby objects enable non-line-of-sight (NLoS) transmissions, extending the usage scenarios beyond LoS. In this paper, we propose wide-band measurement based NLoS channel characterization, and evaluate the performance of direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) V-VIA: scheme for NLoS channel. We propose a distance based NLoS V-VLC channel path loss model considering reflection surface characteristics and NLoS V-VLC channel impulse response (CIR) incorporating the temporal broadening effect due to vehicle reflections through weighted double gamma function. The proposed path loss model yields higher accuracy up to 14 dB when c pared to single order reflection model whereas CIR model estimates the full width at half maximum up to 2 ns accuracy. We further demonstrate that the target bit-error-rate of 10(-3) can be achieved up to 7.86 in, 9.79 m, and 17.62 m distances for black, orange and white vehicle reflection induced measured NLoS V-VLC channels for DCO-OFDM transmissions.
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    On the reliability analysis of C-V2X Mode 4 for next generation connected vehicle applications
    (Institute of Electrical and Electronics Engineers (IEEE), 2022) Karaağaç, Sercan; N/A; N/A; N/A; N/A; N/A; Department of Electrical and Electronics Engineering; Reyhanoğlu, Aslıhan; Kar, Emrah; Kümeç, Feyzi Ege; Kara, Yahya Şükür Can; Turan, Buğra; Ergen, Sinem Çöleri; Researcher; Researcher; Researcher; Researcher; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; N/A; N/A; N/A; N/A; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/A; N/A; N/A; 7211
    Vehicle-to-Everything Communication (V2X) technologies are provisioned to play an important role in increasing road safety by enabling advanced connected vehicle applications such as cooperative perception, cooperative driving, and remote driving. However, the reliability of the technology is limited mainly due to wireless communication channel characteristics. Therefore, investigation of V2X reliability aspects is crucial to utilize the technology efficiently. In this paper, we provide simulation and measurement-based reliability analysis of Cellular Vehicle-to-Everything (C-V2X) Mode 4 technology for various message sizes and Modulation and Coding Schemes (MCS) selections. We demonstrate that the Packet Delivery Ratio (PDR), a key communication performance metric, heavily depends on message size and selected MCS.
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    Machine learning aided path loss estimator and jammer detector for heterogeneous vehicular networks
    (Ieee, 2021) N/A; N/A; N/A; N/A; N/A; Department of Electrical and Electronics Engineering; Turan, Buğra; Uyrus, Ali; Koç, Osman Nuri; Kar, Emrah; Ergen, Sinem Çöleri; PhD Student; PhD Student; Other; Researcher; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; N/A; N/A; College of Engineering; Koc University Ford Otosan Automotive Technologies Laboratory (KUFOTAL); N/A; N/A; N/A; N/A; 7211
    Heterogeneous vehicular communications aim to improve the reliability, security and delay performance of vehicle-to-vehicle (V2V) communications, by utilizing multiple communication technologies. Predicting the path loss through conventional fitting based models and radio frequency (RF) jamming detection through rule based models of different communication schemes fail to address comprehensive mobility and jamming scenarios. In this paper, we propose a machine learning based adaptive link quality estimation and jamming detection scheme for the optimum selection and aggregation of IEEE 802.11p and Vehicular Visible Light Communications (V-VLC) technologies targeting reliable V2V communications. We propose to use Random Forest regression and classifier based algorithms, where multiple individual learners with diversity are trained by using measurement data and the final result is obtained by averaging outputs of all learners. We test our framework on real-world road measurement data, demonstrating up to 234 dB and 0.56 dB Mean Absolute Error (MAE) improvement for V-VLC and IEEE 802.11p path loss prediction compared to fitting based models, respectively. The proposed jamming presence detection scheme yields 88.3% accuracy to detect noise interference injection for IEEE 802.11p links, yielding 3% better prediction performance than previously proposed deep convolutional neural network (DCNN) based scheme.
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    Vehicular visible light communications noise analysis and autoencoder based denoising
    (Institute of Electrical and Electronics Engineers (IEEE), 2022) Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Turan, Buğra; Kar, Emrah; Faculty Member; Other; PhD Student; Department of Electrical and Electronics Engineering; College of Engineering; 7211; N/A; N/A
    Vehicular visible light communications (V-VLC) is a promising intelligent transportation systems (ITS) technology for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications with the utilization of light emitting diodes (LEDs). The main degrading factor for the performance of V-VLC systems is noise. Unlike traditional radio frequency (RF) based systems, V-VLC systems include many noise sources: solar radiation, background lighting from vehicle, street, parking garage and tunnel lights. Traditional V-VLC system noise modeling is based on the additive white Gaussian noise assumption in the form of shot and thermal noise. In this paper, to investigate both time correlated and white noise components of the V-VLC channel, we propose a noise analysis based on Allan variance (AVAR), which provides a time-series analysis method to identify noise from the data. We also propose a generalized Wiener process based V-VLC channel noise synthesis methodology to generate different noise components. We further propose convolutional autoencoder (CAE) based denoising scheme to reduce V-VLC signal noise, which achieves reconstruction root mean square error (RMSE) of 0.0442 and 0.0474 for indoor and outdoor channels, respectively.