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White noise

About: White noise is a research topic. Over the lifetime, 16496 publications have been published within this topic receiving 318633 citations.


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Journal ArticleDOI
Gerhard Kramer1
TL;DR: This paper considers the case where K messages are transmitted through the network in a point-to-point manner, i.e., each message is encoded by exactly one transmitter and is destined for exactly one receiver.
Abstract: A white Gaussian interference network is a channel with T transmitters and R receivers where the received symbols are linear combinations of the transmitted symbols and white Gaussian noise. This paper considers the case where K messages are transmitted through the network in a point-to-point manner, i.e., each message is encoded by exactly one transmitter and is destined for exactly one receiver. It is further assumed that feedback is available so that each transmitter sees the outputs of the receivers to which it is sending messages. Communication strategies based on the discrete Fourier transform (DFT) are developed that perform well for such networks. For multiple-access channels (K=T, R=1) with equal transmitter powers the strategies achieve the feedback sum-rate capacity if the powers are beyond some threshold. For the same channels with fixed transmitter powers and large K, the achievable sum-rate is approximately (log log K)/2 larger than the sum-rate capacity without feedback. For broadcast channels (T=1, K=R) with strong symmetries, the strategies achieve a monotonically increasing sum-rate with K. For interference channels (K=T=R) with strong interference, the strategies significantly enlarge the no-feedback capacity region by "correlation routing.".

185 citations

Journal ArticleDOI
TL;DR: It is shown that when the noise eigenvalues are not clustered sufficiently closely, then the AIC and the MDL may lead to overmodeling by ignoring an arbitrarily large gap between the signal and the noise Eigenvalues.
Abstract: The Akaike (1974) information criterion (AIC) and the minimum description length (MDL) are two well-known criteria for model order selection in the additive white noise case. Our aim is to study the influence on their behavior of a large gap between the signal and the noise eigenvalues and of the noise eigenvalue dispersion. Our results are mostly qualitative and serve to explain the behavior of the AIC and the MDL in some cases of great practical importance. We show that when the noise eigenvalues are not clustered sufficiently closely, then the AIC and the MDL may lead to overmodeling by ignoring an arbitrarily large gap between the signal and the noise eigenvalues. For fixed number of data samples, overmodeling becomes more likely for increasing the dispersion of the noise eigenvalues. For fixed dispersion, overmodeling becomes more likely for increasing the number of data samples. Undermodeling may happen in the cases where the signal and the noise eigenvalues are not well separated and the noise eigenvalues are clustered sufficiently closely. We illustrate our results by using simulations from the effective channel order determination area.

185 citations

Journal ArticleDOI
TL;DR: A new measure to determine homogeneous blocks and a new structure analyzer for rejecting blocks with structure based on high-pass operators and special masks for corners to stabilize the homogeneity estimation are proposed.
Abstract: Noise can significantly impact the effectiveness of video processing algorithms. This paper proposes a fast white-noise variance estimation that is reliable even in images with large textured areas. This method finds intensity-homogeneous blocks first and then estimates the noise variance in these blocks, taking image structure into account. This paper proposes a new measure to determine homogeneous blocks and a new structure analyzer for rejecting blocks with structure. This analyzer is based on high-pass operators and special masks for corners to stabilize the homogeneity estimation. For typical video quality (PSNR of 20-40 dB), the proposed method outperforms other methods significantly and the worst-case estimation error is 3 dB, which is suitable for real applications such as video broadcasts. The method performs well both in highly noisy and good-quality images. It also works well in images including few uniform blocks.

182 citations

Journal ArticleDOI
TL;DR: In this article, the authors investigated the use of the nonlinear device known as the Clegg integrator in the design of a nonlinear feedback system, which minimizes the effect of white sensor noise on the input to the plant.
Abstract: The problem considered is the design of a feedback system containing a linear, time invariant, minimum phase plant, whose parameters are known only within given bounds, such that the time response of the system remains within specified limits. A quasi-optimal design, for given design constraints, is one which minimizes the effect of white sensor noise on the input to the plant. An investigation was conducted on the use of the non linear device known as the Clegg integrator in the design of such a system. The describing function of the Clegg integrator has the same magnitude characteristic, apart from a scale factor, as the linear integrator, but has 52 deg less phase-lag, at all frequencies, than the linear integrator; thus, when used in a feedback system, it provides a larger stability margin than the linear integrator. This property allows the nonlinear feedback system to be designed so that the sensor noise is attenuated more than in the linear design.

182 citations

Journal ArticleDOI
TL;DR: The extent to which this order of convergence can be improved is investigated, and it is found that better approximations are possible for the case of additive noise, but for multiplicative noise it is shown that no improvements are possible.
Abstract: We consider the numerical solution of the stochastic partial differential equation ∂u/∂t = ∂ 2 u/∂x 2 + σ(u)W(x,t), where W is space-time white noise, using finite differences. For this equation Gyongy has obtained an estimate of the rate of convergence for a simple scheme, based on integrals of W over a rectangular grid. We investigate the extent to which this order of convergence can be improved, and find that better approximations are possible for the case of additive noise (σ(u) = 1) if we wish to estimate space averages of the solution rather than pointwise estimates, or if we are permitted to generate other functionals of the noise. But for multiplicative noise (σ(u) = u) we show that no such improvements are possible.

181 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023238
2022535
2021488
2020541
2019558
2018537