Browsing by Author "Dulek, B."

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Open Access
Average Fisher information maximisation in presence of cost-constrained measurements
(The Institution of Engineering and Technology, 2011) Dulek, B.; Gezici, Sinan
An optimal estimation framework is considered in the presence of cost-constrained measurements. The aim is to maximise the average Fisher information under a constraint on the total cost of measurement devices. An optimisation problem is formulated to calculate the optimal costs of measurement devices that maximise the average Fisher information for arbitrary observation and measurement statistics. In addition, a closed-form expression is obtained in the case of Gaussian observations and measurement noise. Numerical examples are presented to explain the results.
Open Access
Comments on 'a Representation for the Symbol Error Rate Using Completely Monotone Functions'
(Institute of Electrical and Electronics Engineers Inc., 2014) Dulek, B.
It was shown in the above-titled paper by Rajan and Tepedelenlioglu (see ibid., vol. 59, no. 6, p. 3922-31, June 2013) that the symbol error rate (SER) of an arbitrary multidimensional constellation subject to additive white Gaussian noise is characterized as the product of a completely monotone function with a nonnegative power of signal-to-noise ratio (SNR) under minimum distance detection. In this comment, it is proved that the probability of correct decision of an arbitrary constellation admits a similar representation as well. Based on this fact, it is shown that the stochastic ordering { G α} proposed by the authors as an extension of the existing Laplace transform order to compare the average SERs over two different fading channels actually predicts that the average SERs are equal for any constellation of dimensionality smaller than or equal to 2α. Furthermore, it is noted that there are no positive random variables X1 and X2 such that the proposed stochastic ordering is satisfied in the strict sense, i.e., X1<Gα X2, when α=N/2 for any positive integer N. Additional remarks are noted about the fading scenarios at low SNR and the generalization to additive compound Gaussian noise originally discussed in the subject paper.
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
Cost minimization of measurement devices under estimation accuracy constraints in the presence of Gaussian noise
(Elsevier, 2012) Dulek, B.; Gezici, Sinan
Novel convex measurement cost minimization problems are proposed based on various estimation accuracy constraints for a linear system subject to additive Gaussian noise. Closed form solutions are obtained in the case of an invertible system matrix. In addition, the effects of system matrix uncertainty are studied both from a generic perspective and by employing a specific uncertainty model. The results are extended to the Bayesian estimation framework by treating the unknown parameters as Gaussian distributed random variables. Numerical examples are presented to discuss the theoretical results in detail.
Open Access
Detector randomization and stochastic signaling for minimum probability of error receivers
(Institute of Electrical and Electronics Engineers, 2012) Dulek, B.; Gezici, Sinan
Optimal receiver design is studied for a communications system in which both detector randomization and stochastic signaling can be performed. First, it is proven 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. Then, it is shown that the optimal receiver design results in a randomization between at most two maximum a-posteriori probability (MAP) detectors corresponding to two deterministic signal vectors. Numerical examples are provided to explain the results.
Open Access
Joint detection and decoding in the presence of prior information with uncertainty
(Institute of Electrical and Electronics Engineers Inc., 2016) Bayram, S.; Dulek, B.; Gezici, Sinan
An optimal decision framework is proposed for joint detection and decoding when the prior information is available with some uncertainty. The proposed framework provides tradeoffs between the average inclusive error probability (computed using estimated prior probabilities) and the worst case inclusive error probability according to the amount of uncertainty while satisfying constraints on the probability of false alarm and the maximum probability of miss-detection. Theoretical results that characterize the structure of the optimal decision rule according to the proposed criterion are obtained. The proposed decision rule reduces to some well-known detectors in the case of perfect prior information or when the constraints on the probabilities of miss-detection and false alarm are relaxed. Numerical examples are provided to illustrate the theoretical results. © 2016 IEEE.
Open Access
Optimal channel switching in the presence of stochastic signaling
(IEEE, 2013) Dulek, B.; Varshney P.K.; Tutay, Mehmet Emin; Gezici, Sinan
Optimal channel switching and detector design is studied for M-ary communication systems in the presence of stochastic signaling, which facilitates randomization of signal values transmitted for each information symbol. Considering the presence of multiple additive noise channels (which can have non-Gaussian distributions in general) between a transmitter and a receiver, the joint optimization of the channel switching (timesharing) strategy, stochastic signals, and detectors is performed in order to achieve the minimum average probability of error. It is proved that the optimal solution to this problem corresponds to either (i) switching between at most two channels with deterministic signaling over each channel, or (ii) time-sharing between at most two different signals over a single channel (i.e., stochastic signaling over a single channel). For both cases, the optimal solutions are shown to employ corresponding maximum a posteriori probability (MAP) detectors at the receiver. Numerical results are presented to investigate the proposed approach. © 2013 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 linear mmse estimation under correlation uncertainty in restricted bayesian framework
(Institute of Electrical and Electronics Engineers, 2023-01-31) Dulek, B.; Topaloğlu, Süleyman Taylan; Gezici, Sinan
A restricted Bayes approach is proposed for linear estimation of a scalar random parameter based on a scalar observation under uncertainty regarding the correlation between the parameter and the observation. In particular, the optimal linear estimator that minimizes the average mean-squared error (MSE) is derived under a constraint on the worst-case MSE by considering possible values of the correlation coefficient and its probability distribution. A closed-form expression is derived for the optimal linear estimator in the proposed restricted Bayesian framework by considering a generic statistical characterization of the correlation coefficient. Performance of the proposed estimator is evaluated via numerical examples and its benefits are illustrated in various scenarios. The proposed framework is also extended to the case of vector-valued observation and the properties of the optimal linear estimator are characterized.
Open Access
Optimal power allocation for average detection probability criterion over flat fading channels
(Institute of Electrical and Electronics Engineers Inc., 2017) Sarıtaş, S.; Dulek, B.; Sezer, A. D.; Gezici, Sinan; Yüksel S.
In this paper, the problem of optimal power allocation over flat fading additive white Gaussian noise channels is considered for maximizing the average detection probability of a signal emitted from a power constrained transmitter in the Neyman-Pearson framework. It is assumed that the transmitter can perform power adaptation under peak and average power constraints based on the channel state information fed back by the receiver. Using results from measure theory and convex analysis, it is shown that this optimization problem, which is in general nonconvex, has an equivalent Lagrangian dual that admits no duality gap and can be solved using dual decomposition. Efficient numerical algorithms are proposed to determine the optimal power allocation scheme under peak and average power constraints. Furthermore, the continuity and monotonicity properties of the corresponding optimal power allocation scheme are characterized with respect to the signal-to-noise ratio for any given value of the false alarm probability. Simulation examples are presented to corroborate the theoretical results and illustrate the performance improvements due to the proposed optimal power allocation strategy.
Embargo
Optimal signal design for coherent detection of binary signals in Gaussian noise under power and secrecy constraints
(Elsevier, 2023-04-13) Dulek, B.; Gezici, Sinan
The problem of optimal signal design for coherent detection of binary signals in Gaussian noise is revisited under power and secrecy constraints. In particular, the aim is to select the binary transmitted signals in an optimal manner so that the probability of error is minimized at an intended receiver while the probability of error at an eavesdropper is maintained above a threshold value and the signal powers are limited. It is shown that an optimal solution exists in the form of antipodal signaling along the eigenvector corresponding to the solution of a maximum (possibly generalized) eigenvalue problem, which is specified explicitly based on the channel coefficient matrices and the noise covariance matrices at the intended receiver and the eavesdropper. Furthermore, optimal signal design can be performed in an efficient manner by solving a semidefinite programming (SDP) relaxation followed by a matrix rank-one decomposition. Numerical examples are provided to illustrate optimal solutions for three different but exhaustive cases.
Open Access
Optimal signaling and detector design for M-ary communication systems in the presence of multiple additive noise channels
(Academic Press, 2014-03) Dulek, B.; Tutay, M. E.; Gezici, Sinan; Varshney, P. K.
An M-ary communication system is considered in which the transmitter and the receiver are connected via multiple additive (possibly non-Gaussian) noise channels, any one of which can be utilized for the transmission of a given symbol. Contrary to deterministic signaling (i.e., employing a fixed constellation), a stochastic signaling approach is adopted by treating the signal values transmitted for each information symbol over each channel as random variables. In particular, the joint optimization of the channel switching (i.e., time sharing among different channels) strategy, stochastic signals, and decision rules at the receiver is performed in order to minimize the average probability of error under an average transmit power constraint. It is proved that the solution to this problem involves either one of the following: (i) deterministic signaling over a single channel, (ii) randomizing (time sharing) between two different signal constellations over a single channel, or (iii) switching (time sharing) between two channels with deterministic signaling over each channel. For all cases, the optimal strategies are shown to employ corresponding maximum a posteriori probability (MAP) decision rules at the receiver. In addition, sufficient conditions are derived in order to specify whether the proposed strategy can or cannot improve the error performance over the conventional approach, in which a single channel is employed with deterministic signaling at the average power limit. Finally, numerical examples are presented to illustrate the theoretical results. © 2013 Elsevier Inc.
Open Access
Optimal stochastic signaling under average power and bit rate constraints
(Institute of Electrical and Electronics Engineers, 2018) Goken, C.; Dulek, B.; Gezici, Sinan
The optimal stochastic signaling based on the joint design of prior distribution and signal constellation is investigated under an average bit rate and power constraints. First, an optimization problem is formulated to maximize the average probability of correct decision over the set of joint distribution functions for prior probabilities and the corresponding constellation symbols. Next, an alternative problem formulation, for which the optimal joint distribution is characterized by a randomization among at most three mass points, is provided, and it is shown that both formulations share the same solution. Three special cases of the problem are investigated in detail. First, in the absence of randomization, the optimal prior probability distribution is analyzed for a given signal constellation and a closed-form solution is provided. Second, the optimal deterministic pair of prior probabilities and the corresponding signal levels are considered. Third, a binary communication system with scalar observations is investigated in the presence of a zero-mean additive white Gaussian noise, and the optimal solution is obtained under practical assumptions. Finally, numerical examples are presented to illustrate the theoretical results. It is observed that the proposed approach can provide improvements in terms of average symbol error rate over the classical scheme for certain scenarios.
Open Access
Optimum Power Allocation for Average Power Constrained Jammers in the Presense of Non-Gaussian Noise
(Institute of Electrical and Electronics Engineers, 2012-08) Bayram, S.; Vanli, N. D.; Dulek, B.; Sezer, I.; Gezici, Sinan
We study the problem of determining the optimum power allocation policy for an average power constrained jammer operating over an arbitrary additive noise channel, where the aim is to minimize the detection probability of an instantaneously and fully adaptive receiver employing the Neyman-Pearson (NP) criterion. We show that the optimum jamming performance can be achieved via power randomization between at most two different power levels. We also provide sufficient conditions for the improvability and nonimprovability of the jamming performance via power randomization in comparison to a fixed power jamming scheme. Numerical examples are presented to illustrate theoretical results.
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
Power adaptation for cognitive radio systems under an average sinr loss constraint in the absence of path loss information
(Kluwer Academic Publishers, 2014) Dulek, B.; Gezici, Sinan; Sawai, R.; Kimura, R.
An upper bound is derived on the capacity of a cognitive radio system by considering the effects of path loss and log-normal shadowing simultaneously for a single-cell network. Assuming that the cognitive radio is informed only of the shadow fading between the secondary (cognitive) transmitter and primary receiver, the capacity is achieved via the water-filling power allocation strategy under an average primary signal to secondary interference plus noise ratio loss constraint. Contrary to the perfect channel state information requirement at the secondary system (SS), the transmit power control of the SS is accomplished in the absence of any path loss estimates. For this purpose, a method for estimating the instantaneous value of the shadow fading is also presented. A detailed analysis of the proposed power adaptation strategy is conducted through various numerical simulations.
Open Access
Universal bounds on the derivatives of the symbol error rate for arbitrary constellations
(2014) Dulek, B.
The symbol error rate (SER) of the minimum distance detector under additive white Gaussian noise is studied in terms of generic bounds and higher order derivatives for arbitrary constellations. A general approach is adopted so that the recent results on the convexity/concavity and complete monotonicity properties of the SER can be obtained as special cases. Novel universal bounds on the SER, which depend only on the constellation dimensionality, minimum and maximum constellation distances are obtained. It is shown that the sphere hardening argument in the channel coding theorem can be derived using the proposed bounds. Sufficient conditions based on the positive real roots (with odd multiplicity) of an explicitly-specified polynomial are presented to determine the signs of the SER derivatives of all orders in signal-to-noise ratio. Furthermore, universal bounds are given for the SER derivatives of all orders. As an example, it is shown that the proposed bounds yield a better characterization of the SER for arbitrary two-dimensional constellations over the complete monotonicity property derived recently. © 2014 IEEE.