Browsing by Subject "Jamming"

Now showing 1 - 12 of 12

Open Access
Convexity properties of detection probability under additive Gaussian noise: optimal signaling and jamming strategies
(IEEE, 2013) Dulek, B.; Gezici, Sinan; Arıkan, Orhan
In this correspondence, we study the convexity properties for the problem of detecting the presence of a signal emitted from a power constrained transmitter in the presence of additive Gaussian noise under the Neyman-Pearson (NP) framework. It is proved that the detection probability corresponding to the α-level likelihood ratio test (LRT) is either strictly concave or has two inflection points such that the function is strictly concave, strictly convex, and finally strictly concave with respect to increasing values of the signal power. In addition, the analysis is extended from scalar observations to multidimensional colored Gaussian noise corrupted signals. Based on the convexity results, optimal and near-optimal time sharing strategies are proposed for average/peak power constrained transmitters and jammers. Numerical methods with global convergence are also provided to obtain the parameters for the proposed strategies.
Open Access
Durağan olmayan çok bileşenli boğucu sinyaller için yeni bir uyarlanır karışma çıkarıcı analizi
(IEEE, 2005) Durak, L.; Arıkan, Orhan; Song, I.
A novel adaptive short-time Fourier transform (STFT) implementation for the analysis of non-stationary multi-component jammer signals is introduced. The proposed time-frequency distribution is the fusion of optimum STFTs of individual signal components that are based on the recently introduced generalized time-bandwidth product (GTBP) definition. The GTBP optimal STFTs of the components are combined through thresholding and obtaining the individual component support images, which are related with the corresponding GTBP optimal STFTs.
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
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
A game theoretic approach to channel switching in the presence of jamming
(IEEE, 2021-10-14) Bozkurt, Berk; Sezer, A. D.; Gezici, Sinan; Girici, T.
In this letter, a channel switching problem is investigated in the presence of jamming based on a game theoretic approach. First, a convex formulation of the optimal channel switching problem is proposed for a given jamming strategy. Then, considering a fixed channel switching strategy, an explicit solution of the optimal jammer power allocation problem is obtained. Consequently, a game theoretic formulation is proposed and the existence of a pure-strategy Nash equilibrium is shown for the proposed channel switching game between the transmitter and the jammer.
Open Access
Jamming bandits-a novel learning method for optimal jamming
(Institute of Electrical and Electronics Engineers Inc., 2016) Amuru, S.; Tekin, C.; Van Der Schaar, M.; Buehrer, R.M.
Can an intelligent jammer learn and adapt to unknown environments in an electronic warfare-type scenario? In this paper, we answer this question in the positive, by developing a cognitive jammer that adaptively and optimally disrupts the communication between a victim transmitter-receiver pair. We formalize the problem using a multiarmed bandit framework where the jammer can choose various physical layer parameters such as the signaling scheme, power level and the on-off/pulsing duration in an attempt to obtain power efficient jamming strategies. We first present online learning algorithms to maximize the jamming efficacy against static transmitter-receiver pairs and prove that these algorithms converge to the optimal (in terms of the error rate inflicted at the victim and the energy used) jamming strategy. Even more importantly, we prove that the rate of convergence to the optimal jamming strategy is sublinear, i.e., the learning is fast in comparison to existing reinforcement learning algorithms, which is particularly important in dynamically changing wireless environments. Also, we characterize the performance of the proposed bandit-based learning algorithm against multiple static and adaptive transmitter-receiver pairs.
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
Optimal jammer placement in wireless localization networks
(IEEE, 2015-06-07) Gezici, Sinan; Bayram, S.; Gholami, M. R.; Jansson, M.
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 Cramér-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. © 2015 IEEE.
Open Access
Optimal jamming of wireless localization systems
(IEEE, 2015-06) Gezici, Sinan; Gholami, M.R.; Bayram, S.; Jansson, M.
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 Cramér-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 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 performance of the proposed schemes. © 2015 IEEE.
Open Access
Optimal stochastic approaches for signal detection and estimation under inequality constraints
(2012) Dülek, Berkan
Fundamental to the study of signal detection and estimation is the design of optimal procedures that operate on the noisy observations of some random phenomenon. For detection problems, the aim is to decide among a number of statistical hypotheses, whereas estimating certain parameters of the statistical model is required in estimation problems. In both cases, the solution depends on some goodness criterion by which detection (or estimation) performance is measured. Despite being a well-established field, the advances over the last several decades in hardware and digital signal processing have fostered a renewed interest in designing optimal procedures that take more into account the practical considerations. For example, in the detection of binary-valued scalar signals corrupted with additive noise, an analysis on the convexity properties of the error probability with respect to the transmit signal power has suggested that the error performance cannot be improved via signal power randomization/sharing under an average transmit power constraint when the noise has a unimodal distribution (such as the Gaussian distribution). On the contrary, it is demonstrated that performance enhancement is possible in the case of multimodal noise distributions and even under Gaussian noise for three or higher dimensional signal constellations. Motivated by these results, in this dissertation we adopt a structured approach built on concepts called stochastic signaling and detector randomization, and devise optimal detection procedures for power constrained communications systems operating over channels with arbitrary noise distributions. First, we study the problem of jointly designing the transmitted signals, decision rules, and detector randomization factors for an M-ary communications system with multiple detectors at the receiver. For each detector employed at the receiver, it is assumed that the transmitter can randomize its signal constellation (i.e., transmitter can employ stochastic signaling) according to some probability density function (PDF) under an average transmit power constraint. We show that stochastic signaling without detector randomization cannot achieve a smaller average probability of error than detector randomization with deterministic signaling for the same average power constraint and noise statistics when optimal maximum a-posteriori probability (MAP) detectors are employed in both cases. Next, we prove that a randomization between at most two MAP detectors corresponding to two deterministic signal vectors results in the optimal performance. Sufficient conditions are also provided to conclude ahead of time whether the correct decision performance can or cannot be improved by detector randomization. In the literature, the discussions on the benefits of stochastic signaling and detector randomization are severely limited to the Bayesian criterion. Therefore, we study the convexity/concavity properties for the problem of detecting the presence of a signal emitted from a power constrained transmitter in the presence of additive Gaussian noise under the Neyman-Pearson (NP) framework. First, it is proved that the detection probability corresponding to the α−level likelihood ratio test (LRT) is either concave or has two inflection points such that the function is concave, convex and finally concave with respect to increasing values of the signal power. Based on this result, optimal and near-optimal power sharing/randomization strategies are proposed for average and/or peak power constrained transmitters. Using a similar approach, the convexity/concavity properties of the detection probability are also investigated with respect to the jammer power. The results indicate that a weak Gaussian jammer should employ on-off time sharing to degrade the detection performance. Next, the previous analysis for the NP criterion is generalized to channels with arbitrary noise PDFs. Specifically, we address the problem of jointly designing the signaling scheme and the decision rule so that the detection probability is maximized under constraints on the average false alarm probability and average transmit power. In the case of a single detector at the receiver, it is shown that the optimal solution can be obtained by employing randomization between at most two signal values for the on-signal and using the corresponding NP-type LRT at the receiver. When multiple detectors are available at the receiver, the optimal solution involves a randomization among no more than three NP decision rules corresponding to three deterministic signal vectors. Up to this point, we have focused on signal detection problems. In the following, the trade-offs between parameter estimation accuracy and measurement device cost are investigateed under the influence of noise. First, we seek to determine the most favorable allocation of the total cost to measurement devices so that the average Fisher information of the resulting measurements is maximized for arbitrary observation and measurement statistics. Based on a recently proposed measurement device cost model, we present a generic optimization problem without assuming any specific estimator structure. Closed form expressions are obtained in the case of Gaussian observations and measurement noise. Finally, a more elaborate analysis of the relationship between parameter estimation accuracy and measurement device cost is presented. More specifically, novel convex measurement cost minimization problems are proposed based on various estimation accuracy constraints assuming a linear system subject to additive Gaussian noise for the deterministic parameter estimation problem. Robust allocation of the total cost to measurement devices is also considered by assuming a specific uncertainty model on the system matrix. Closed form solutions are obtained in the case of an invertible system matrix for two estimation accuracy criteria. Through numerical examples, various aspects of the proposed optimization problems are compared. Lastly, the discussion is extended to the Bayesian framework assuming that the estimated parameter is Gaussian distributed.
Open Access
Optimum power randomization for the minimization of outage probability
(IEEE, 2013) Dulek, B.; Vanli, N. D.; Gezici, Sinan; Varshney P. K.
The optimum power randomization problem is studied to minimize outage probability in flat block-fading Gaussian channels under an average transmit power constraint and in the presence of channel distribution information at the transmitter. When the probability density function of the channel power gain is continuously differentiable with a finite second moment, it is shown that the outage probability curve is a nonincreasing function of the normalized transmit power with at least one inflection point and the total number of inflection points is odd. Based on this result, it is proved that the optimum power transmission strategy involves randomization between at most two power levels. In the case of a single inflection point, the optimum strategy simplifies to on-off signaling for weak transmitters. Through analytical and numerical discussions, it is shown that the proposed framework can be adapted to a wide variety of scenarios including log-normal shadowing, diversity combining over Rayleigh fading channels, Nakagami-m fading, spectrum sharing, and jamming applications. We also show that power randomization does not necessarily improve the outage performance when the finite second moment assumption is violated by the power distribution of the fading. © 2013 IEEE.
Open Access
Parameter encoding for ecrb minimization in the presence of jamming
(Institute of Electrical and Electronics Engineers Inc., 2022-01-28) Ozturk, Cuneyd; Goken, Cagri; Gezici, Sinan
The optimal encoding 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.