wgn() is specifically meant to create a white noise with a predefined power levels while randn() is meant to generate normally distributed random numbers WITHOUT specifying the power. You will have to scale the values generated from randn() to meet the desired noise power level.

But you say " One should not, however, infer that the random variables in the WGN process are themselves Gaussian random variables". I did not fully understand this. If the random variables aren't Gaussian (and this seems reasonable to me since they have infinite variance), why is the process named Gaussian? $\endgroup$ –

Jan 11, 2023 · 1.-From : An Introduction to Signals and Noise in Electrical Communications. Author : Bruce Carlson. Search for much cheaper copies readily available online.

Jul 1, 2024 · From Steven M. Kay's book fundamentals of statistical signal processing, he derives in chapter 3 the CRLB for a single sinusoidal frequency estimation in WGN when the amplitude and phase are known (example 3.5) and the CRLB for a single sinusoidal frequency, amplitude and phase parameters when all three are unknown (example 3.14).

Jun 1, 2015 · use y=wgn(m,n,p) command in matlab. it generates a mxn matrix of white Gaussian noise. p specifies the power of y in decibels relative to a watt. Share Improve this answer

I am relatively new to signal processing techniques and Matlab and need a bunch of test data in the form of white noise, as defined on the Wikipedia page, with constant, flat power spectrum.

Since the input process has zero mean, so does the output process have zero mean, that is, all the random variables constituting the process have zero mean. For the case of WGN, the filter output is a strictly stationary Gaussian process, meaning that all the random variables are Gaussian random variables. As a special case of all this, if the ...

Apr 11, 2025 · In the time-domain a WGN signal can be defined as a signal that for each instance is probabilistic in nature and that the probability can be described as a normal distribution. That is, if you'd measure/sample the signal multiple times, the distribution approaches the normal or Gaussian one (hence the name)

Jan 17, 2018 · DC level in signal processing refers to the average or the mean value of a signal. So a zero-mean signal will have an average value of zero over its domain of definition.

To do this, I am starting from a white gaussian noise (WGN) and feed with the WGN a transfer function, which will act like a filter. In fact,it's easy to prove that if you choose a PSD of the white noise equal to $1 {\rm unit}^2/{\rm Hz}$ ,then the output is $ H(f) \times 1$ .