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How to calculate the variance of the noise samples $n[j]$ in terms of $N_0$ and $B$, where $n[j]$=$n_f(jT_s)$ and $T_s$ is the sampling period? Do you know how to calculate the variance of the process $\{n_f(t) \colon -\infty < t < \infty\}$? No? Hint: it is the area under the power spectral density curve of $\{n_f(t)\colon -\infty < t < \... 1 The best fit time domain solution can be found by constructing two two basis vectors with your known frequency and calculate the coefficients directly. The magnitude and phase can then be directly determined from these values. Let C be a vector of cosine values over your frame and S be a vector of sine values. You then want to find$(a,b)$so that$aC + ...

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To offset or not is simply in your definitions on how you want to do the math involved and can be convenient for further processing. Subtracting a constant, or otherwise scaling the waveform, does not change the signal to noise ratio. Correlation is to multiply and accumulate, and the cross-correlation and auto-correlation functions show this correlation ...

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My understanding is that before calculating the cross-correlation with the C/A code one has to transform (just shift by its average value?) the digitized and demodulated received signal so that there are both positive and negative values in the sequence and the sum of these on average should be zero, is this correct? No, at least not strictly so. Think ...

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Simply observe the noise for a longish while and estimate a PSD of it – for example, simply by doing an FFT and observing the magnitude of that, and calculating the mean square error to the theoretical (triangular) PSD of pink noise. That can be easily implemented only using Python/numpy (fft, abs, mean are all implemented in numpy). Another, pretty ...

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First, let’s separate the Voss part from the McCartney. The first generates a 1/f distribution of random numbers, with power inversely proportional to frequency—pink noise. McCartney proposed a change that gives a more flat computational load. A brief overview of Voss: Start with random number generators, one for each bit in a binary counter. Let’s ...

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So let's look at what the author of the article you linked to says further down; Output samples are on the top row, and are the sum of all the other rows at that time. Output /---\/---\/---\/---\/---\/---\/---\/---\/---\/---\/---\/---\/---\/---\/---\/---\/---\/---\ \___/\___/\___/\___/\___/\___/\___/\___/\___/\___/\___/\___/\___/\___/\___/\___/\...

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