Noise (electronics) | Time series models

Additive white Gaussian noise

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. * White refers to the idea that it has uniform power across the frequency band for the information system. It is an analogy to the color white which has uniform emissions at all frequencies in the visible spectrum. * Gaussian because it has a normal distribution in the time domain with an average time domain value of zero. Wideband noise comes from many natural noise sources, such as the thermal vibrations of atoms in conductors (referred to as thermal noise or Johnson–Nyquist noise), shot noise, black-body radiation from the earth and other warm objects, and from celestial sources such as the Sun. The central limit theorem of probability theory indicates that the summation of many random processes will tend to have distribution called Gaussian or Normal. AWGN is often used as a channel model in which the only impairment to communication is a linear addition of wideband or white noise with a constant spectral density (expressed as watts per hertz of bandwidth) and a Gaussian distribution of amplitude. The model does not account for fading, frequency selectivity, interference, nonlinearity or dispersion. However, it produces simple and tractable mathematical models which are useful for gaining insight into the underlying behavior of a system before these other phenomena are considered. The AWGN channel is a good model for many satellite and deep space communication links. It is not a good model for most terrestrial links because of multipath, terrain blocking, interference, etc. However, for terrestrial path modeling, AWGN is commonly used to simulate background noise of the channel under study, in addition to multipath, terrain blocking, interference, ground clutter and self interference that modern radio systems encounter in terrestrial operation. (Wikipedia).

Additive white Gaussian noise
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What Is White Noise?

Jonathan defines what white noise actually is and how it's used to mask other annoying sounds. Learn more at HowStuffWorks.com: http://science.howstuffworks.com/question47.htm Share on Facebook: http://goo.gl/n7YNrZ Share on Twitter: http://goo.gl/Fq9InS Subscribe: http://goo.gl/ZYI7Gt V

From playlist Episodes hosted by Jonathan

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Spinodal decomposition in the Allen-Cahn equation without and with noise

This simulation compares solutions of the Allen-Cahn equation without and with noise. The left half of the display shows the case without noise, while the right half shows the case with an additional space-time white noise, meaning here that independent Gaussian random variables are added

From playlist Reaction-diffusion equations

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How To Make Smells Not Smell

What if there was a way to create white noise for your sense of smell? Trace is here to explain how scientists were able to successfully mask odors using “white smell.” Read More: White smell: the olfactory equivalent of white noise http://www.newscientist.com/article/dn22514-white-sm

From playlist DNews Favorites

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Time Series Talk : White Noise

Intro to white noise in time series analysis

From playlist Time Series Analysis

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Mixture Models 4: multivariate Gaussians

Full lecture: http://bit.ly/EM-alg We generalise the equations for the case of a multivariate Gaussians. The main difference from the previous video (part 2) is that instead of a scalar variance we now estimate a covariance matrix, using the same posteriors as before.

From playlist Mixture Models

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Analysis of Quantization Error

http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Modeling quantization error as uncorrelated noise. Signal to quantization noise ratio as a function of the number of bits used to represent the sign

From playlist Sampling and Reconstruction of Signals

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Discrete noise filters

I discuss causal and non-causal noise filters: the moving average filter and the exponentially weighted moving average. I show how to do this filtering in Excel and Python

From playlist Discrete

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Lec 1 | MIT 6.451 Principles of Digital Communication II

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From playlist MIT 6.451 Principles of Digital Communication II

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Markov processes and applications-3 by Hugo Touchette

PROGRAM : BANGALORE SCHOOL ON STATISTICAL PHYSICS - XII (ONLINE) ORGANIZERS : Abhishek Dhar (ICTS-TIFR, Bengaluru) and Sanjib Sabhapandit (RRI, Bengaluru) DATE : 28 June 2021 to 09 July 2021 VENUE : Online Due to the ongoing COVID-19 pandemic, the school will be conducted through online

From playlist Bangalore School on Statistical Physics - XII (ONLINE) 2021

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Carsten Chong (Columbia) -- Asymptotic behavior of the stochastic heat equation with Lévy noise

We discuss some recent results about the macroscopic behavior of the solution to the stochastic heat equation with Lévy noise. For a fixed spatial point, we show that the solution develops unusually large peaks as time tends to infinity. As this already occurs under additive noise, we refe

From playlist Northeastern Probability Seminar 2020

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Hubert Lacoin (IMPA) -- The continuum directed polymer in Lévy Noise as a scaling limit

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From playlist Columbia SPDE Seminar

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Alejandro Torres-Forné - Variational models and algorithms for GW denoising and reconstruction

Recorded 29 November 2021. Alejandro Torres-Forné of the University of Valencia presents "Variational models and algorithms for GW denoising and reconstruction: applications" at IPAM's Workshop IV: Big Data in Multi-Messenger Astrophysics. Abstract: In this talk, we will show the applicati

From playlist Workshop: Big Data in Multi-Messenger Astrophysics

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(PP 6.2) Multivariate Gaussian - examples and independence

Degenerate multivariate Gaussians. Some sketches of examples and non-examples of Gaussians. The components of a Gaussian are independent if and only if they are uncorrelated.

From playlist Probability Theory

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Constructing a solution of the 2D Kardar-Parisi-Zhang equation (Lecture - 03) by Sourav Chatterjee

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From playlist Student Competition: Computer Vision Training

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Nicolas Perkowski (FU Berlin) -- Mass asymptotics for parabolic Anderson model with WN potential

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From playlist Columbia Probability Seminar

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Davar Khoshnevisan (Utah) -- Ergodicity and CLT for SPDEs

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From playlist Columbia SPDE Seminar

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From playlist 06. Optics and Quantum Theory

Related pages

Shot noise | White noise | Communication channel | Channel capacity | Johnson–Nyquist noise | Noisy-channel coding theorem | Watt | Mutual information | Sphere packing | Hertz | Independent and identically distributed random variables | Differential entropy | Central limit theorem | Typical set | Information theory | Frequency | Variance | Rayleigh distribution | Interference (communication) | Spectral density | Fano's inequality | Normal distribution | Bandwidth (signal processing) | Probability theory | Signal-to-noise ratio | Phasor | Gaussian process