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The autocorrelation of white Gaussian noise is a delta. When the noise is filtered or band-limited, as is the case here, the autocorrelation becomes a sinc. This has interesting consequences, for example in telecommunications, where the noise at the output of a matched filter is uncorrelated only at certain time delays -- fortunately, the time delays we're ...


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The function filtfilt filters the signal twice (forward and backward) in order to eliminate phase distortions. As a side effect, your signal is not filtered by the transfer function corresponding to the numerator and denominator polynomial coefficients supplied to the routine, but by its squared magnitude. You should use an ordinary filter routine, such as ...


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If the noises signal are mutually uncorrelated the power of the sum is the sum of the powers. For three signals you get $$\sigma_{total} = \sqrt{\sigma_A^2+\sigma_B^2+\sigma_C^2}$$ Works any type of uncorrelated noise, Gaussian or not.


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You can create noise with the same frequency-domain characteristic as your example noise image simply by multiplying a white noise frequency spectrum by the magnitude of the frequency spectrum you are trying to emulate. White noise is expected to have equal power at all frequencies (though this will not be exactly the case), so the multiplication will "...


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So, first of all, a filter in signal processing is analogous to a filter that you'd use on liquids. A coffee filter passes through the stuff you want (liquid coffee), and holds back the stuff you don't want (coffee grounds). It does this because the coffee grounds are bigger than water molecules, and all those yummy flavor molecules (not to mention ...


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Something like Bendat and Piersol's Random Data Analysis might be a good starting point. It's written more for physical scientists rather than people doing estimation and detection theory like the references in the comments (Gray, Kay, etc.). For specifically spectral analysis, Percival and Walden's Spectral analysis for physical applications was recently ...


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I think the problem is caused by the oversampling issue. Let us say $\gamma = E_s/N_o$ is the SNR where $E_s$ is the energy per symbol and $N_o$ is the power of the noise per sample). First thing to be checked is whether the symbol mapping is performed to satisfy unit average energy per symbol. Secondly,please check that your pulse shaping filter taps (array)...


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