Kıvanç Türeli, Didem

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TÜRELI Didem Kıvanç
Didem Kivanc TURELi
Didem Kıvanç Türeli
Tureli, Kıvanç
Didem Kıvanç TÜRELI
D. K. Türeli
Kivanc D.
D. Kıvanç Türeli
Tureli, Didem
Tureli, K.
Türeli, Kıvanç
D. K. TURELi
D., Kıvanç Türeli
TURELi Didem Kivanc
Kıvanç Türeli, Didem
Türeli Didem Kıvanç
D. Kivanc Tureli
Tureli, Kivanc
Türeli, D.
D. K. Tureli
Tureli, D.
Didem K. Tureli
Türeli D.
D.,Kıvanç Türeli
Türeli, K.
Tureli Didem Kivanc
Didem, Kıvanç Türeli
Türeli, Didem
Didem K. Türeli
D. K. TÜRELI
Tureli D.
Didem Kivanc Tureli
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Dr.Öğr.Üyesi
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didem.kivanc@okan.edu.tr
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Scholarly Output

5

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2

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0

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2012 - 201932020 - 20232
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Now showing 1 - 5 of 5
  • Thumbnail Image
    Article
    Citation Count: 0
    Optimized Intelligent Design for Smart Systems Hybrid Beamforming and Power Adaptation Algorithms for Sensor Networks Decision-Making Approach
    (Springernature, 2019) Khiarullah, Ali Kamil; Tureli, Ufuk; Kivanc, Didem; Mekatronik / Mechatronics
    During last two decades, power adaptation and beamforming solutions have been proposed for multiple input multiple output (MIMO) Ad Hoc networks. Game theory based methods such as cooperative and non-cooperative joint beamforming and power control for the MIMO ad hoc systems consider the interference and overhead reduction, but have failed to achieve the trade-off between communication overhead and power minimization. Cooperative method using game theory achieves the power minimization, but introduced the overhead. The non-cooperative solution using game theory reduced the overhead, but it takes more power and iterations for convergence. In this paper, a novel game theory based algorithms proposed to achieve the trade-off between power control and communication overhead for multiple antennas enabled wireless ad-hoc networks operating in multiple-users interference environment. The optimized joint iterative power adaption and beamforming method designed to minimize the mutual interference at every wireless node with constant received signal to interference noise ratio (SINR) at every receiver node. First cooperative potential game theory based algorithm designed for the power and interference minimization in which users cluster and binary weight books along used to reduce the overhead. Then the non-cooperative based approach using the reinforcement learning (RL) method is proposed to reduce the number of iterations and power consumption in networks, the proposed RL procedure is fully distributed as every transmit node require only an observation of its instantaneous beamformer label which can be obtained from its receive node. The simulation results of both methods prove the efficient power adaption and beamforming for small and large networks with minimum overhead and interference compared to state-of-art methods. (C) 2019 The Authors. Published by Atlantis Press SARL.
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    Article
    Citation Count: 35
    Energy-efficient routing for correlated data in wireless sensor networks
    (Elsevier, 2012) Zeydan, Engin; Kivanc, Didem; Comaniciu, Cristina; Tureli, Ufuk; Mekatronik / Mechatronics
    In this paper, we investigate the reduction in the total energy consumption of wireless sensor networks using multi-hop data aggregation by constructing energy-efficient data aggregation trees. We propose an adaptive and distributed routing algorithm for correlated data gathering and exploit the data correlation between nodes using a game theoretic framework. Routes are chosen to minimize the total energy expended by the network using best response dynamics to local data. The cost function that is used for the proposed routing algorithm takes into account energy, interference and in-network data aggregation. The iterative algorithm is shown to converge in a finite number of steps. Simulations results show that multi-hop data aggregation can significantly reduce the total energy consumption in the network. (C) 2011 Elsevier B.V. All rights reserved.
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    Conference Object
    Citation Count: 0
    Deep Learning Based Spectrum Sensing Method for Cognitive Radio System
    (Springer Science and Business Media Deutschland GmbH, 2023) Hussein,A.T.; Kivanc,D.; Abdullah,H.; Falih,M.S.; Mekatronik / Mechatronics
    Cognitive radio (CR) network is the promised paradigm to resolve the spectrum shortage and to enable the cooperation in heterogeneous wireless networks in 5G and beyond. CR mainly relays on Spectrum Sensing (SS) strategy by which the vacant spectrum portion is identified. Therefore, the sensing mechanism should be accurate as much as possible, as long as the subsequent cognition steps are mainly depended on it. In this paper, an efficient and blind SS algorithm called Deep Learning Based Spectrum Sensing (DBSS) is proposed. This algorithm utilizes the deep learning approach in SS by using Convolutional Neural Network (CNN) as a detector instead of energy thresholding. In this algorithm, the computed energies of the received samples are used as dataset to feed the optimized CNN model in both training and testing phases. The proposed algorithm is simulated by MATLAB, the simulation scenarios divided into: CNN optimization (training) and SS. The last scenario shows the detection ability of the proposed algorithm for PU under noisy environment. The simulation results show that the proposed algorithm reached high detection probability (Pd) with low sensing errors at low SNR. In addition, high recognition ability to identifying Primary User (PU) signal form noise only signal is achieved as well. Finally, the proposed algorithm is validated with respect to real spectrum data that supported by SDR in an experimental signal transmission and reception scenario. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Conference Object
    Citation Count: 0
    Capacity and association for outdoor small cell LTE-A network;
    (IEEE Computer Society, 2014) Tureli,U.; Kivanc,D.; Mekatronik / Mechatronics
    This paper presents theoretical and numerical analysis of interference and throughput for Long Term Evolution Advanced (LTE-A) outdoor small cells using a detailed simulation. In the simulation program, the downlink from base station to mobile user was studied and results are shown. © 2014 IEEE.
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    Conference Object
    Citation Count: 0
    Distributed Cooperative and Noncooperative Joint Power and Beamforming Adaptation Game for MIMO Sensor Network
    (Institute of Electrical and Electronics Engineers Inc., 2020) Khairullah,A.K.; Tureli,U.; Kivanc,D.; Mekatronik / Mechatronics
    Distributed Joint beamforming and power adaptation algorithms are of interest for MIMO ad hoc networks. Cooperative and non-cooperative games have been presented to decrease interference (mutual) at each sensor node, under receiver signal-to-interference and noise (SINR) constraints. In reduced feedback algorithms, the optimum transmitter node beamformer is selected from a predefined codebook. This paper introduces a cooperative optimal beamformer selection algorithm to minimize the total power consumption for cluster-based network topology under different minimum SINR constraints. The algorithm reduces overhead incurred by 48% and 10% while increasing the convergence rate to the steady-state allocation by 17% and 9% for the cooperative and non-cooperative beamformer selection games, respectively. Simulation results verify the proposed theoretical analysis, and demonstrate the performance of the Enhanced Co-Operative Power Minimization Algorithm (ECOPMA), Reinforcement Learning based Power allocation and Beamformer Algorithm (RLPBA) for the non-cooperative game [1], with state of the art methods and centralized (optimal) solutions as a fair benchmark. © 2020 IEEE.