I want to know the difference between white noise and white Gaussian noise.
White noise can be Gaussian, or uniform or even Poisson. It seems to be that the key characteristic of white noise is that each measurement is completely independent of the measurements preceding it. Also, it must be flat on the frequency spectrum.
Ref.
https://www.quora.com/Is-white-noise-same-as-white-gaussian-noise
White noise can be Gaussian, or uniform or even Poisson. It seems to be that the key characteristic of white noise is that each measurement is completely independent of the measurements preceding it. Also, it must be flat on the frequency spectrum.
Ref.
https://www.quora.com/Is-white-noise-same-as-white-gaussian-noise
White noise has equal intensity over the whole frequency domain (i.e. nearly flat curve). Gaussian refers to (time domain) sample distribution: each sample has a normal distribution with zero mean and finite variance (which corresponds to the above mentioned flat curve in a frequency domain).
I will tell you about Gaussian White noise first- Take a microphone (if you have one) and don't speak anything. Just record it using MATLAB and plot it. What you would see is some kind of noise. Now if you plot the histogram(pdf) of it what you would get is a Gaussian distribution. Similarly if plot the frequency spectrum of that, you would get same value in all the frequencies. That's the reason we call this as Gaussian White Noise. Now coming to your question- I don't think Pavg is infinite for white noise. Whatever operation you do, you always limit the bandwidth due to some filtering so that Pavg will never be infinite. I have read somewhere that at some higher frequencies there is no white noise due some kind of quantum effect that I don't remember and it was theorized by Max Planck (It's somewhat related to Planck's constant). So Pavg will never be infinite even though you have an infinite bandwidth.
Source: iVenky
Thanks for your answers, Can any body, please refer some book or research article to study about this matter?
Again thanks a lot.
As you see in my answer, it is a rather basic thing. You can find in any signal processing book. One of the best classical one:
Signals and systems by Alan V. Oppenheim
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