Browsing by Subject "Stochastic resonances"
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Item Open Access Effects of additional independent noise in binary composite hypothesis-testing problems(IEEE, 2009-09) Bayram, Suat; Gezici, SinanPerformance of some suboptimal detectors can be improved by adding independent noise to their observations. In this paper, the effects of adding independent noise to observations of a detector are investigated for binary composite hypothesistesting problems in a generalized Neyman-Pearson framework. Sufficient conditions are derived to determine when performance of a detector can or cannot be improved via additional independent noise. Also, upper and lower limits are derived on the performance of a detector in the presence of additional noise, and statistical characterization of optimal additional noise is provided. In addition, two optimization techniques are proposed to calculate the optimal additional noise. Finally, simulation results are presented to investigate the theoretical results. © 2009 IEEE.Item Open Access Noise enhanced detection in multiple-access environments(IEEE, 2009) Bayram, Suat; Gezici, SinanUnder certain conditions, addition of noise can enhance performance of suboptimal detectors, which is called the stochastic resonance (SR) effect. In this paper, the effects of SR are investigated for conventional detectors in the presence of multiple-access interference. First, conditions under which probability of error performance of the detector can or cannot be improved are obtained. Then, performance of noise enhanced detectors are analyzed, and an upper bound on the amount of performance improvement that can be obtained via SR is derived. Numerical examples are presented to support the theoretical analysis.Item Open Access Noise enhanced detection in the restricted Bayesian framework(IEEE, 2010) Bayram, Suat; Gezici, Sinan; Poor H.V.Effects of additive independent noise are investigated for sub-optimal detectors according to the restricted Bayes criterion. The statistics of optimal additive noise are characterized. Also, sufficient conditions for improvability or nonimprovability of detection via additive noise are obtained. A detection example is presented to study the theoretical results. ©2010 IEEE.Item Open Access Noise-enhanced M-ary hypothesis-testing in the minimax framework(IEEE, 2009-09) Bayram, Suat; Gezici, SinanIn this study, the effects of adding independent noise to observations of a suboptimal detector are studied for M-ary hypothesis-testing problems according to the minimax criterion. It is shown that the optimal additional noise can be represented by a randomization of at most M signal values under certain conditions. In addition, a convex relaxation approach is proposed to obtain an accurate approximation to the noise probability distribution in polynomial time. Furthermore, sufficient conditions are presented to determine when additional noise can or cannot improve the performance of a given detector. Finally, a numerical example is presented. © 2009 IEEE.Item Open Access Stochastic resonance in binary composite hypothesis-testing problems in the Neyman-Pearson framework(Elsevier, 2012-02-20) Bayram, S.; Gezici, SinanPerformance of some suboptimal detectors can be enhanced by adding independent noise to their inputs via the stochastic resonance (SR) effect. In this paper, the effects of SR are studied for binary composite hypothesis-testing problems. A Neyman-Pearson framework is considered, and the maximization of detection performance under a constraint on the maximum probability of false-alarm is studied. The detection performance is quantified in terms of the sum, the minimum, and the maximum of the detection probabilities corresponding to possible parameter values under the alternative hypothesis. Sufficient conditions under which detection performance can or cannot be improved are derived for each case. Also, statistical characterization of optimal additive noise is provided, and the resulting false-alarm probabilities and bounds on detection performance are investigated. In addition, optimization theoretic approaches to obtaining the probability distribution of optimal additive noise are discussed. Finally, a detection example is presented to investigate the theoretical results.