AWGN

(redirected from Additive white Gaussian noise)
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AcronymDefinition
AWGNAdditive White Gaussian Noise
AWGNAdaptive White Gaussian Noise
AWGNAmerican Willow Growers Network (est. 1988)
References in periodicals archive ?
Encoder is assembled at each relay node to encode separately the corrupted analog signals, and subsequently the encoded digital signals are transmitted to the destination D through the channel with additive white Gaussian noise. The destination receives and jointly decodes the digital signals from all relays and yields the estimation of the original analog signals.
The test image is 256[x.sub.2]56 pixels gray levels image that was distorted by convolution with a Gaussian PSF and additive white Gaussian noise. The differences consist in the fact that the authors use as metric the root mean square error (rms) instead of PSNR and characterize the PSF by Full Width of Half Maximum (FWHM) instead of standard deviation [[sigma].sub.ker].
In additive white Gaussian noise, all the image pixels deviate from their original values following the bell-shaped curve distribution (Gaussian curve).
For detailed comparison of image denoising, additive white Gaussian noise (AWGN) with [sigma] = 0.6 is added as shown in Figure 1(a).
It is common to derive signal-processing algorithms using the additive white Gaussian noise assumption.
This is particularly important in light of common cable-plant and field interruptions like micro-reflections, phase noise, additive white Gaussian noise, and group delay.
3 compare the performance of MEDLL and PF-MEDLL estimators in the presence of additive white Gaussian noise. Assumed integration time is T = 600 ms.
The maximum-likelihood (ML) [4], and nonlinear instantaneous least squares (NILS) [7] estimators can provide very high estimation accuracy, whose estimator variances achieve Cramer-Rao lower bound (CRLB) asymptotically under additive white Gaussian noise [8].
Noise is modeled as additive white Gaussian noise (AWGN), where all the image pixels deviate from their original values following the Gaussian curve.
Unfortunately, in a case of the PLC environment, we can't stay only with the additive white Gaussian noise. The noise scenario is much more complicated, since five general classes of noise can be distinguished in power distribution line channels [4], [5].
At the front end of the receiver additive white Gaussian noise is added.
But soft thresholding procedure is near optimal for the signals corrupted by additive white Gaussian noise, however, some considerations applying the thresholding method (hard or soft thresholding method) to speech signal since the speech signal in the unvoiced region contains relatively lots of high frequency components that can be eliminated during the thresholding process.