Noise enhanced detection in the restricted Bayesian framework

Bayram, Suat; Gezici, Sinan; Poor, H. Vincent

doi:10.1109/icassp.2010.5495847

Cited by 9 publications

(12 citation statements)

References 17 publications

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“…In the literature, optimization problems that are in similar forms have been investigated in various studies such as [1]- [3]. A similar approach is used to obtain the following proposition: Proposition 1: Assume that γ belongs to a finite closed set and P D (γ) in (5) is a continuous function.…”

Section: Analysis Of Optimum Power Allocation For Average Power Cmentioning

confidence: 99%

Optimum Power Allocation for Average Power Constrained Jammers in the Presence of Non-Gaussian Noise

et al. 2012

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Abstract-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.

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Section: Analysis Of Optimum Power Allocation For Average Power Cmentioning

confidence: 99%

Optimum Power Allocation for Average Power Constrained Jammers in the Presence of Non-Gaussian Noise

et al. 2012

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“…, w K = E{G K (S)}, ∀p S (s), s ∈ S . Similar arguments as in [15], [19], [46], [48] can be used to conclude that set W is equal to the convex hull of U ; that is, W = V . Therefore, due to Carathéodory's theorem [44], [45], any point in V (equivalently, in W ) can be expressed as the convex combination of (K + 2) or fewer points in U since the dimension of U is smaller than or equal to (K + 1).…”

Section: B Proof Of Propositionmentioning

confidence: 97%

“…The proof can be obtained based on Carathéodory's theorem [44], [45] similarly to the proofs in [15], [19], [46]. First, define the following set: U = { (u 0 , u 1 , with s min and s max denoting the minimum and maximum signal amplitude values, respectively.…”

Section: B Proof Of Propositionmentioning

confidence: 99%

Optimal Randomization of Signal Constellations on the Downlink of a Multiuser DS-CDMA System

Tutay

Gezici

Arıkan

2013

IEEE Trans. Wireless Commun.

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Abstract-In this study, the jointly optimal power control with signal constellation randomization is proposed for the downlink of a multiuser communications system. Unlike a conventional system in which a fixed signal constellation is employed for all the bits of a user (for given channel conditions and noise power), power control with signal constellation randomization involves randomization/time-sharing among different signal constellations for each user. A formulation is obtained for the problem of optimal power control with signal constellation randomization, and it is shown that the optimal solution can be represented by a randomization among (K + 1) or fewer distinct signal constellations for each user, where K denotes the number of users. In addition to the original nonconvex formulation, an approximate solution based on convex relaxation is derived. Then, detailed performance analysis is presented when the receivers employ symmetric signaling and sign detectors. Specifically, the maximum asymptotical improvement ratio is shown to be equal to the number of users, and the conditions under which the maximum and minimum asymptotical improvement ratios are achieved are derived. Numerical examples are presented to investigate the theoretical results, and to illustrate performance improvements achieved via the proposed approach.

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“…Optimization problems that are in similar forms to (24) have been studied in the literature, such as in [12] and [11]. First, the following set is defined:…”

Section: Optimal Detector Randomizationmentioning

confidence: 99%

Optimal Detector Randomization for Multiuser Communications Systems

Tutay

Gezici

Arıkan

2013

IEEE Trans. Commun.

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Abstract-Optimal detector randomization is studied for the downlink of a multiuser communications system, in which users can perform time-sharing among multiple detectors. A formulation is provided to obtain optimal signal amplitudes, detectors, and detector randomization factors. It is shown that the solution of this joint optimization problem can be calculated in two steps, resulting in significant reduction in computational complexity. It is proved that the optimal solution is achieved via randomization among at most min{K, N d } detector sets, where K is the number of users and N d is the number of detectors at each receiver. Lower and upper bounds are derived on the performance of optimal detector randomization, and it is proved that the optimal detector randomization approach can reduce the worst-case average probability of error of the optimal approach that employs a single detector for each user by up to K times. Various sufficient conditions are obtained for the improvability and nonimprovability via detector randomization. In the special case of equal crosscorrelations and noise powers, a simple solution is developed for the optimal detector randomization problem, and necessary and sufficient conditions are presented for the uniqueness of that solution. Numerical examples are provided to illustrate the improvements achieved via detector randomization.

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Noise enhanced detection in the restricted Bayesian framework

Cited by 9 publications

References 17 publications

Optimum Power Allocation for Average Power Constrained Jammers in the Presence of Non-Gaussian Noise

Optimum Power Allocation for Average Power Constrained Jammers in the Presence of Non-Gaussian Noise

Optimal Randomization of Signal Constellations on the Downlink of a Multiuser DS-CDMA System

Optimal Detector Randomization for Multiuser Communications Systems

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