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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.
The waveform of a Gaussian white noise signal plotted on a graph. In signal processing, white noise is a random signal having equal intensity at different frequencies, giving it a constant power spectral density. [1]
In signal processing theory, Gaussian noise, named after Carl Friedrich Gauss, is a kind of signal noise that has a probability density function (pdf) equal to that of the normal distribution (which is also known as the Gaussian distribution). [1] [2] In other words, the values that the noise can take are Gaussian-distributed.
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
First, white noise is a generalized stochastic process with independent values at each time. [12] Hence it plays the role of a generalized system of independent coordinates, in the sense that in various contexts it has been fruitful to express more general processes occurring e.g. in engineering or mathematical finance, in terms of white noise.
Maximum-ratio combining is the optimum combiner for independent additive white Gaussian noise channels. MRC can restore a signal to its original shape. The technique was invented by American engineer Leonard R. Kahn [ 1 ] in 1954.
It is also known as differentiated white noise, due to its being the result of the differentiation of a white noise signal. Due to the diminished sensitivity of the human ear to high-frequency hiss and the ease with which white noise can be electronically differentiated (high-pass filtered at first order), many early adaptations of dither to ...
The transformation is called "whitening" because it changes the input vector into a white noise vector. Several other transformations are closely related to whitening: the decorrelation transform removes only the correlations but leaves variances intact, the standardization transform sets variances to 1 but leaves correlations intact,