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Additive white Gaussian noise (AWGN) is a basic noise model used in information theory to mimic the effect of many random processes that occur in nature. The modifiers denote specific characteristics: Additive because it is added to any noise that might be intrinsic to the information system.
This model is called a Gaussian white noise signal (or process). In the mathematical field known as white noise analysis , a Gaussian white noise w {\displaystyle w} is defined as a stochastic tempered distribution, i.e. a random variable with values in the space S ′ ( R ) {\displaystyle {\mathcal {S}}'(\mathbb {R} )} of tempered distributions .
A special case is white Gaussian noise, in which the values at any pair of times are identically distributed and statistically independent (and hence uncorrelated). In communication channel testing and modelling, Gaussian noise is used as additive white noise to generate additive white Gaussian noise.
White noise. Additive white Gaussian noise; Black noise; Gaussian noise; Pink noise or flicker noise, with 1/f power spectrum; Brownian noise, with 1/f 2 power spectrum; Contaminated Gaussian noise, whose PDF is a linear mixture of Gaussian PDFs; Power-law noise; Cauchy noise; Multiplicative noise, multiplies or modulates the intended signal
In probability theory, a branch of mathematics, white noise analysis, otherwise known as Hida calculus, is a framework for infinite-dimensional and stochastic calculus, based on the Gaussian white noise probability space, to be compared with Malliavin calculus based on the Wiener process. [1]
The science also shows that sound can play a role in sleep quality with one study showing that white noise helped reduced awakenings to common intensive care unit sounds. Again, both factors are ...
AR noise or "autoregressive noise" is such a model, and generates simple examples of the above noise types, and more. The Federal Standard 1037C Telecommunications Glossary [ 1 ] [ 2 ] defines white, pink, blue, and black noise.
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