Browsing by Author "Kivanc,D."

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Citation Count: 0
Capacity and association for outdoor small cell LTE-A network;
(IEEE Computer Society, 2014) Tureli,U.; Kıvanç Türeli, Didem; 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.
Citation Count: 0
Deep Learning Based Spectrum Sensing Method for Cognitive Radio System
(Springer Science and Business Media Deutschland GmbH, 2023) Hussein,A.T.; Kıvanç Türeli, Didem; 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.
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.; Kıvanç Türeli, Didem; 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.