Browsing by Author "Öztürk, Cüneyd"

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Open Access
Convexity properties of detection probability for noncoherent detection of a modulated sinusoidal carrier
(Institute of Electrical and Electronics Engineers, 2018) Öztürk, Cüneyd; Dülek, B.; Gezici, Sinan
In this correspondence paper, the problem of noncoherent detection of a sinusoidal carrier is considered in the presence of Gaussian noise. The convexity properties of the detection probability are characterized with respect to the signal-To-noise ratio (SNR). It is proved that the detection probability is a strictly concave function of SNR when the false alarm probability α satisfies α > e-2, and it is first a strictly convex function and then a strictly concave function of SNR for α < e-2. In addition, optimal power allocation strategies are derived under average and peak power constraints. It is shown that on-off signaling can be optimal for α < e-2 depending on the power constraints, whereas transmission at a constant power level that is equal to the average power limit is optimal in all other cases.
Open Access
Eavesdropper and jammer selection in wireless source localization networks
(IEEE, 2021-07-26) Öztürk, Cüneyd; Gezici, Sinan
We consider a wireless source localization network in which a target node emits localization signals that are used by anchor nodes to estimate the target node position. In addition to target and anchor nodes, there can also exist eavesdropper nodes and jammer nodes which aim to estimate the position of the target node and to degrade the accuracy of localization, respectively. We first propose the problem of eavesdropper selection with the goal of optimally placing a given number of eavesdropper nodes to a subset of possible positions to estimate the target node position as accurately as possible. As the performance metric, the Cramér-Rao lower bound (CRLB) related to the estimation of the target node position by eavesdropper nodes is derived, and its convexity and monotonicity properties are investigated. By relaxing the integer constraints, the eavesdropper selection problem is approximated by a convex optimization problem and algorithms are proposed for eavesdropper selection. Then, the problem of jammer selection is proposed where the aim is to optimally place a given number of jammer nodes to a subset of possible positions for degrading the localization accuracy of the network as much as possible. A CRLB expression from the literature is used as the performance metric, and its concavity and monotonicity properties are derived. Also, a convex optimization problem is derived after relaxation. Finally, the joint eavesdropper and jammer selection problem is proposed with the goal of placing certain numbers of eavesdropper and jammer nodes to a subset of possible positions.
Open Access
Eavesdropper selection strategies in wireless source localization networks
(IEEE, 2020) Öztürk, Cüneyd; Gezici, Sinan
We consider a wireless source localization network in which eavesdropper nodes aim to estimate the position of a target node. We formulate the problem of selecting a set of N E positions out of N possible positions for placing eavesdropper nodes in order to estimate the target node position as accurately as possible. The Cramér-Rao lower bound related to the estimation of the target node position by eavesdropper nodes is derived, and its monotonicity and convexity properties are investigated. Via relaxation of the integer constraints, the eavesdropper selection problem is approximated by a convex optimization problem, which is used to propose two algorithms for eavesdropper selection. Moreover, in the presence of parameter uncertainty, a robust version of the eavesdropper selection problem is investigated. Simulation results are presented to examine performance of the proposed algorithms.
Open Access
Estimation theoretic analyses of location secrecy and ris-aided localization under hardware impairments
(2022-06) Öztürk, Cüneyd
In this thesis, we present estimation theoretic analyses of location secrecy and reconfigurable intelligent surface (RIS) aided localization under hardware impairments. First, we consider a wireless source localization network in which a target node emits localization signals that are used by anchor nodes to estimate the target node position. In addition to target and anchor nodes, there can also exist eavesdropper nodes and jammer nodes which aim to estimate the position of the target node and to degrade the accuracy of localization, respectively. We propose the problem of eavesdropper selection with the goal of optimally placing a given number of eavesdropper nodes to a subset of possible positions in the network to estimate the target node position as accurately as possible. As the performance metric, the Cramér-Rao lower bound (CRLB) related to the estimation of the target node position by eavesdropper nodes is derived, and its convexity and monotonicity properties are investigated. By relaxing the integer constraints, the eavesdropper selection problem is approximated by a convex optimization problem and algorithms are proposed for eavesdropper selection. Moreover, in the presence of parameter uncertainty, a robust version of the eavesdropper selection problem is developed. Then, the problem of jammer selection is proposed where the aim is to optimally place a given number of jammer nodes to a subset of possible positions for degrading the localization accuracy of the network as much as possible. A CRLB expression from the literature is used as the performance metric, and its concavity and monotonicity properties are derived. Also, a convex optimization problem and its robust version are derived after relaxation. Moreover, the joint eavesdropper and jammer selection problem is proposed with the goal of placing certain numbers of eavesdropper and jammer nodes to a subset of possible positions. Simulation results are presented to illustrate performance of the proposed algorithms. Second, a wireless source localization network consisting of synchronized target and anchor nodes is considered. An anchor placement problem is formulated to minimize the CRLB on estimation of target node positions by anchor nodes. It is shown that the anchor placement problem can be approximated as a minimization problem of the ratio of two supermodular functions. Due to the lack of a polynomial time algorithm for such problems, an anchor selection problem is proposed to solve the anchor placement problem. Via relaxation of integer constraints, the anchor selection problem is approximated by a convex optimization problem, which is used to propose two algorithms for anchor selection. Furthermore, extensions to quasi-synchronous wireless localization networks are discussed. To examine the performance of the proposed algorithms, various simulation results are presented. Third, we investigate the problem of RIS-aided near-field localization of a user equipment (UE) served by a base station (BS) under phase-dependent amplitude variations at each RIS element. Through a misspecified Cramér -Rao bound (MCRB) analysis and a resulting lower bound (LB) on localization, we show that when the UE is unaware of amplitude variations (i.e., assumes unit-amplitude responses), severe performance penalties can arise, especially at high signal-to-noise ratios (SNRs). Leveraging Jacobi-Anger expansion to decouple range-azimuth-elevation dimensions, we develop a low-complexity approximated mismatched maximum likelihood (AMML) estimator, which is asymptotically tight to the LB. To mitigate performance loss due to model mismatch, we propose to jointly estimate the UE location and the RIS amplitude model parameters. The corresponding Cramér -Rao bound (CRB) is derived, as well as an iterative refinement algorithm, which employs the AMML method as a subroutine and alternatingly updates individual parameters of the RIS amplitude model. Simulation results indicate fast convergence and performance close to the CRB. The proposed method can successfully recover the performance loss of the AMML under a wide range of RIS parameters and effectively calibrate the RIS amplitude model online with the help of a user that has an a-priori unknown location. Fourth, we consider RIS-aided localization scenarios with RIS pixel failures, where individual RIS elements can become faulty due to hardware imperfections. We explore the impact of such failures on the localization performance. To that aim, an MCRB analysis is conducted and numerical results indicate that performance loss for estimating the UE position can be significant in the presence of pixel failures. To remedy this issue, we develop two different diagnosis strategies to determine which pixels are failing, and design robust methods to perform localization in the presence of faulty elements. One strategy is based on the l_1-regularization method, the second one employs a successive approach. Both methods significantly reduce the performance loss due to pixel failures. The successive one performs very close to the theoretical bounds at high SNRs even though it has a higher computational cost than the l_1-regularization based method. In the final part of the dissertation, the optimal encoding strategy of a scalar parameter is performed in the presence of jamming based on an estimation theoretic criterion. Namely, the aim is to obtain the optimal encoding function at the transmitter that minimizes the expectation of the conditional Cramér -Rao bound (ECRB) at the receiver when the jammer has access to the parameter and alters the received signal by sending an encoded version of the parameter. Via calculus of variations, the optimal encoding function at the transmitter is characterized explicitly, and an algorithm is proposed to calculate it. Numerical examples demonstrate benefits of the proposed optimal encoding approach.
Open Access
Jamming strategies in wireless source localization systems
(IEEE, 2019-08) Keskin, Musa Furkan; Öztürk, Cüneyd; Bayram, Suat; Gezici, Sinan
We consider optimal jamming strategies in wireless source localization systems, where anchor nodes estimate positions of target nodes in the presence of jammers that emit zero-mean Gaussian noise. The Cramér-Rao lower bound (CRLB) for target location estimation is derived and the problem of optimal power allocation for jammer nodes is formulated to maximize the average CRLB for target nodes under total and peak power constraints. Exploiting the special problem structure and successive convex approximation techniques, we develop an iterative algorithm that transforms the original non-convex problem into a sequence of convex geometric programs. In addition, we present a closed-form solution that is asymptotically optimal. Numerical results demonstrate the improved jamming performance of the proposed solutions over the uniform power allocation strategy.
Open Access
On the Impact of hardware impairments on RIS-aided localization
(IEEE, 2022-05) Öztürk, Cüneyd; Keskin, M. F.; Wymeersch, H.; Gezici, Sinan
We investigate a reconfigurable intelligent surface (RIS)-aided near-field localization system with single-antenna user equipment (UE) and base station (BS) under hardware impairments by considering a practical phase-dependent RIS amplitude variations model. To analyze the localization performance under the mismatch between the practical model and the ideal model with unit-amplitude RIS elements, we employ the misspecified Cramér-Rao bound (MCRB). Based on the MCRB derivation, the lower bound (LB) on the mean-squared error for estimation of UE position is evaluated and shown to converge to the MCRB at low signal-to-noise ratios (SNRs). Simulation results indicate more severe performance degradation due to the model misspecification with increasing SNR. In addition, the mismatched maximum likelihood (MML) estimator is derived and found to be tight to the LB in the high SNR regime. Finally, we observe that the model mismatch can lead to an order-of-magnitude localization performance loss at high SNRs. © 2022 IEEE.
Open Access
Optimal decision rules for simple hypothesis testing under general criterion involving error probabilities
(IEEE, 2020) Dulek, B.; Öztürk, Cüneyd; Gezici, Sinan
The problem of simple M-ary hypothesis testing under a generic performance criterion that depends on arbitrary functions of error probabilities is considered. Using results from convex analysis, it is proved that an optimal decision rule can be characterized as a randomization among at most two deterministic decision rules, each of the form reminiscent to Bayes rule, if the boundary points corresponding to each rule have zero probability under each hypothesis. Otherwise, a randomization among at most M(M-1)+1 deterministic decision rules is sufficient. The form of the deterministic decision rules are explicitly specified. Likelihood ratios are shown to be sufficient statistics. Classical performance measures including Bayesian, minimax, Neyman-Pearson, generalized Neyman-Pearson, restricted Bayesian, and prospect theory based approaches are all covered under the proposed formulation. A numerical example is presented for prospect theory based binary hypothesis testing.
Open Access
Optimal fractional fourier filtering for graph signals
(IEEE, 2021-05-19) Öztürk, Cüneyd; Özaktaş, Haldun M.; Gezici, Sinan; Koç, Aykut
Graph signal processing has recently received considerable attention. Several concepts, tools, and applications in signal processing such as filtering, transforming, and sampling have been extended to graph signal processing. One such extension is the optimal filtering problem. The minimum mean-squared error estimate of an original graph signal can be obtained from its distorted and noisy version. However, the best separation of signal and noise, and thus the least error, is not always achieved in the ordinary Fourier domain, but rather a fractional Fourier domain. In this work, the optimal filtering problem for graph signals is extended to fractional Fourier domains, and theoretical analysis and solution of the proposed problem are provided along with computational cost considerations. Numerical results are presented to illustrate the benefits of filtering in fractional Fourier domains.
Open Access
Optimal power allocation and optimal linear encoding for parameter estimation in the presence of a smart eavesdropper
(IEEE, 2022-08-11) Abadi, Erfan Mehdipour; Göken, Çağrı; Öztürk, Cüneyd; Gezici, Sinan
In this article, we consider secure transmission of a deterministic vector parameter from a transmitter to an intended receiver in the presence of a smart eavesdropper. The aim is to determine the optimal power allocation and optimal linear encoding strategies at the transmitter to maximize the estimation performance at the intended receiver under constraints on the estimation performance at the eavesdropper and on the transmit power. First, the A-optimality criterion is adopted by utilizing the Cramér-Rao lower bound as the estimation performance metric, and the optimal power allocation and optimal linear encoding strategies are characterized theoretically. Then, corresponding to the D-optimality criterion, the determinant of the Fisher information matrix is considered as the estimation performance metric. It is shown that the optimal linear encoding and optimal power allocation strategies lead to the same solution for this criterion. In addition, extensions of the theoretical results are provided to cases with statistical knowledge of systems parameters. Numerical examples are provided to investigate the optimal power allocation and optimal linear encoding strategies in different scenarios.