Yazar "Bayram, Suat" seçeneğine göre listele

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    Jamming of Wireless Localization Systems
    (Ieee-Inst Electrical Electronics Engineers Inc, 2016) Gezici, Sinan; Gholami, Mohammad Reza; Bayram, Suat; Jansson, Magnus
    In this paper, the optimal jamming of wireless localization systems is investigated. Two optimal power allocation schemes are proposed for jammer nodes in the presence of total and peak power constraints. In the first scheme, power is allocated to jammer nodes in order to maximize the average Cramer-Rao lower bound (CRLB) of target nodes, whereas in the second scheme, the power allocation is performed for the aim of maximizing the minimum CRLB of target nodes. Both the schemes are formulated as linear programs, and a closed-form solution is obtained for the first scheme. For the second scheme, under certain conditions, the property of full total power utilization is specified, and a closed-form solution is obtained when the total power is lower than a specific threshold. In addition, it is shown that non-zero power is allocated to at most NT jammer nodes according to the second scheme in the absence of peak power constraints, where NT is the number of target nodes. In the presence of parameter uncertainty, robust versions of the power allocation schemes are proposed. Simulation results are presented to investigate the performance of the proposed schemes and to illustrate the theoretical results.
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    Noise enhanced hypothesis-testing according to restricted Neyman-Pearson criterion
    (Academic Press Inc Elsevier Science, 2014) Bayram, Suat; Gultekin, San; Gezici, Sinan
    Noise enhanced hypothesis-testing is studied according to the restricted Neyman-Pearson (NP) criterion. First, a problem formulation is presented for obtaining the optimal probability distribution of additive noise in the restricted NP framework. Then, sufficient conditions for improvability and nonimprovability are derived in order to specify if additive noise can or cannot improve detection performance over scenarios in which no additive noise is employed. Also, for the special case of a finite number of possible parameter values under each hypothesis, it is shown that the optimal additive noise can be represented by a discrete random variable with a certain number of point masses. In addition, particular improvability conditions are derived for that special case. Finally, theoretical results are provided for a numerical example and improvements via additive noise are illustrated. (C) 2013 Elsevier Inc. All rights reserved.
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    Optimal Jammer Placement in Wireless Localization Networks
    (Ieee, 2015) Gezici, Sinan; Bayram, Suat; Gholami, Mohammad Reza; Jansson, Magnus
    The optimal jammer placement problem is proposed for a wireless localization network, where the aim is to degrade the accuracy of locating target nodes as much as possible. In particular, the optimal location of a jammer node is obtained in order to maximize the minimum of the Cramer-Rao lower bounds for a number of target nodes under location related constraints for the jammer node. Theoretical results are derived to specify scenarios in which the jammer node should be located as close to a certain target node as possible, or the optimal location of the jammer node is determined by two or three of the target nodes. In addition, explicit expressions for the optimal location of the jammer node are derived in the presence of two target nodes. Numerical examples are presented to illustrate the theoretical results.
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    Optimal Jammer Placement in Wireless Localization Systems
    (Ieee-Inst Electrical Electronics Engineers Inc, 2016) Gezici, Sinan; Bayram, Suat; Kurt, Mehmet Necip; Gholami, Mohammad Reza
    In this study, the optimal jammer placement problem is proposed and analyzed for wireless localization systems. In particular, the optimal location of a jammer node is obtained by maximizing the minimum of the Cramer-Rao lower bounds (CRLBs) for a number of target nodes under location related constraints for the jammer node. For scenarios with more than two target nodes, theoretical results are derived to specify conditions underwhich the jammer node is located as close to a certain target node as possible, or the optimal location of the jammer node is determined by two of the target nodes. Also, explicit expressions are provided for the optimal location of the jammer node in the presence of two target nodes. In addition, in the absence of distance constraints for the jammer node, it is proved, for scenarios with more than two target nodes, that the optimal jammer location lies on the convex hull formed by the locations of the target nodes and is determined by two or three of the target nodes, which have equalized CRLBs. Numerical examples are presented to provide illustrations of the theoretical results in different scenarios.
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    Optimal Jamming of Wireless Localization Systems
    (Ieee, 2015) Gezici, Sinan; Gholami, Mohammad Reza; Bayram, Suat; Jansson, Magnus
    In this study, optimal jamming of wireless localization systems is investigated. Two optimal power allocation schemes are proposed for jammer nodes in the presence of total and peak power constraints. In the first scheme, power is allocated to jammer nodes in order to maximize the average Cramer-Rao lower hound (CRLB) of target nodes whereas in the second scheme the power allocation is performed for the aim of maximizing the minimum CRLB of target nodes. Both schemes are formulated as linear programs, and a closed-form expression is obtained for the first scheme. Also, the full total power utilization property is specified for the second scheme. Simulation results are presented to investigate perlbrmance of the proposed schemes.
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    Performance improvement in decentralized fusion problems via additive dependent noises
    (Institute of Electrical and Electronics Engineers Inc., 2016) Bayram, Suat; Soku, Hakan; Ibrahim, Ahmed Yusuf; Korkmaz, Ekrem
    This paper investigates effects of adding dependent random signals (additive noises) to observations of local sensors in decentralized fusion problems, where the decision of each local sensor is directly transmitted to the fusion center for the final decision according to Neyman-Pearson criterion. Threshold of the fusion center and random signals added to observations of local sensors are modeled as random variables dependent on each other but independent from the observations at the local sensors. Simulation results are presented to investigate performance of the proposed approach. © 2017 Elsevier B.V., All rights reserved.
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    Performance Improvement in Decentralized Fusion Problems via Additive Dependent Noises
    (Ieee, 2015) Bayram, Suat; Soku, Hakan; Ibrahim, Ahmed Yusuf; Korkmaz, Ekrem
    This paper investigates effects of adding dependent random signals (additive noises) to observations of local sensors in decentralized fusion problems, where the decision of each local sensor is directly transmitted to the fusion center for the final decision according to Neyman-Pearson criterion. Threshold of the fusion center and random signals added to observations of local sensors are modeled as random variables dependent on each other but independent from the observations at the local sensors. Simulation results are presented to investigate performance of the proposed approach.
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    Threshold optimization according to the restricted Bayes criterion in decentralized detection problems
    (Tubitak Scientific & Technological Research Council Turkey, 2016) Bayram, Suat; Soku, Hakan
    In this paper, the restricted Bayes approach is studied in a decentralized detection problem. All decisions on which the hypothesis is true are made by local sensors through conditionally independent observations. Then these decisions are transmitted to the fusion center for the final decision. In the conventional approach, all thresholds of local sensors and the fusion center are considered as deterministic variables and optimized according to the given criterion for given test statistics of local sensors and the fusion center. In this paper, it is shown that setting thresholds as random variables instead of deterministic ones can improve the performance according to the restricted Bayes criterion. It is proved that optimal random thresholds are dependent on each other, and the probability density function of each one consists of at most two point masses. Two methods for the implementation of this scheme are proposed. A necessary and sufficient condition for improvability of the conventional approach through replacing optimal deterministic thresholds by optimal random ones is derived. Finally, theoretical results are investigated through simulations.