Parameter-Induced Stochastic Resonance and Baseband Binary Pam Signals Transmission Over an Awgn Channel

Duan, Fabing; Xu, Biao

doi:10.1142/s0218127403006601

Cited by 72 publications

(62 citation statements)

References 34 publications

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“…Researchers [77]- [79] have found noise benefits in DCT-domain watermark decoding based on parameter-induced stochastic resonance [80]. Their approaches differed from ours in two main ways.…”

Section: Watermark Embeddingmentioning

confidence: 85%

Noise Benefits in Quantizer-Array Correlation Detection and Watermark Decoding

Patel

Kosko

2011

IEEE Trans. Signal Process.

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Abstract-Quantizer noise can improve statistical signal detection in array-based nonlinear correlators in Neyman-Pearson and maximum-likelihood (ML) detection. This holds even for infinitevariance symmetric alpha-stable channel noise and for generalized-Gaussian channel noise. Noise-enhanced correlation detection leads to noise-enhanced watermark extraction based on such nonlinear detection at the pixel or bit level. This yields a noise-based algorithm for digital watermark decoding using two new noisebenefit theorems. The first theorem gives a necessary and sufficient condition for quantizer noise to increase the detection probability of a constant signal for a fixed false-alarm probability if the channel noise is symmetric and if the sample size is large. The second theorem shows that the array must contain more than one quantizer for such a stochastic-resonance noise benefit if the symmetric channel noise is unimodal. It also shows that the noise-benefit rate improves in the small-quantizer noise limit as the number of array quantizers increases. The second theorem further shows that symmetric uniform quantizer noise gives the optimal rate for an initial noise benefit among all finite-variance symmetric scalefamily noise. Two corollaries give similar results for stochastic-resonance noise benefits in ML detection of a signal sequence with known shape but unknown amplitude. Fig. 1 shows an SR noise benefit in the ML watermark extraction of the "yin-yang" image embedded in the discrete-cosine transform (DCT-2) coefficients of the "Lena" image [32]. The yin-yang image of Fig. 1(a) is the 64 64 binary watermark message embedded in the midfrequency DCT-2 coefficients of the 512 512 gray-scale Lena image using direct-sequence spread spectrum [33]. Fig. 1(b) shows the result when the yin-yang figure watermarks the Lena image. Figs. 1(c)-1(g) shows that small amounts of additive uniform quantizer noise improve the watermark-extraction performance of the noisy quantizer-array ML detector while too much noise degrades the performance. Uniform quantizer noise with standard deviation reduces more than 33% of the pixel-detection errors in the extracted watermark image. Section VI gives the details of such noise-enhanced watermark decoding. IndexThe quantizer-array detector consists of two parts. It consists of a nonlinear preprocessor that precedes a correlator and a likelihood-ratio test of the correlator's output. This nonlinear detector takes samples of a noise-corrupted signal and then sends each sample to the nonlinear preprocessor array of noisy quantizers connected in parallel. Each quantizer in the array adds its independent quantizer noise to the noisy input sample and then quantizes this doubly noisy data sample into a binary value. The quantizer array output for each sample is just the sum of all quantizer outputs. The correlator then correlates these preprocessed samples with the signal. The detector's final stage applies either the NP likelihood-ratio test in Section II or the ML-ratio test in Section V.Sect...

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Section: Watermark Embeddingmentioning

confidence: 85%

Noise Benefits in Quantizer-Array Correlation Detection and Watermark Decoding

Patel

Kosko

2011

IEEE Trans. Signal Process.

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“…In the field of statistical signal processing, noise benefits are investigated in various studies such as [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. In [2], it is shown that the detection probability of the optimal detector for a described network with nonlinear elements driven by a weak sinusoidal signal in white Gaussian noise is non-monotonic with respect to the noise power and fixed false alarm probability; hence, detection probability enhancements can be achieved via increasing the noise level in certain scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…One approach for realizing noise benefits is to tune the parameters of a nonlinear system, as employed, e.g., in [8][9][10][11][12]. An alternative approach is the injection of a random process independent of both the meaningful information signal (transmitted or hidden signal) and the background noise (undesired signal).…”

Section: Introductionmentioning

confidence: 99%

Noise benefits in joint detection and estimation problems

Akbay

Gezici

2016

Signal Processing

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a b s t r a c tAdding noise to inputs of some suboptimal detectors or estimators can improve their performance under certain conditions. In the literature, noise benefits have been studied for detection and estimation systems separately. In this study, noise benefits are investigated for joint detection and estimation systems. The analysis is performed under the Neyman-Pearson (NP) and Bayesian detection frameworks and according to the Bayesian estimation criterion. The maximization of the system performance is formulated as an optimization problem. The optimal additive noise is shown to have a specific form, which is derived under both NP and Bayesian detection frameworks. In addition, the proposed optimization problem is approximated as a linear programming (LP) problem, and conditions under which the performance of the system can or cannot be improved via additive noise are obtained. With an illustrative numerical example, performance comparison between the noise enhanced system and the original system is presented to support the theoretical analysis.

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“…Hence the BER is a percentage number. Some works, which tried to realize ASR by tuning system parameters to get the minimum BER, are known as parameter-induced ASR [15][16][17]. A Comparison between noise-induced ASR and parameter-induced ASR can be found in Ref.…”

Section: Introductionmentioning

confidence: 99%