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I am new to the Signal processing domain, So my question might sound basic. I am trying to reproduce the results from the paper Decimative subspace-based parameter estimation techniques by Geert Morren, Philippe Lemmerling, Sabine Van Huffel.

I would like to first know what is the unit of the model given here, where $a_k$ amplitudes, $\phi_k$ phases, $\alpha_k$ damping factors, $f_k$ - frequencies in Hz and $f_{sample}$ - sampling frequency. \begin{equation} x(t) = \sum_{k=1}^{K} (a_k e^{(j\phi_k)})(e^{\{(j2\pi f_k - \alpha_k)/f_{sample}\}t}) + Noise(t), \quad t = 0,1,...,N-1 \end{equation}

I am adding circularly symmetric WGN to $x(t)$ using the wgn function from MATLAB (shown below in code), which is in dBW unit. I am not sure whether this is correct, and I am computing SNR also using a snr MATLAB function. However, I want to use the variance of noise and scaling of noise to determine SNR. Could someone explain me clearly how to do this?

f       = [0.02, 0.0205];
a       = [1, 1];
alpha   = [0, 0];
phi     = [0, 0];
K       = length(f);
del_t   = 1;
N       = 1000;
t       = (0:del_t:N-1)';
size_t  = length(t);
f_sample= 1/del_t;
Nruns   = 40;
x       = zeros(size_t,1);
SNR     = zeros(1,Nruns);

for iter = 1:Nruns
    noise = wgn(size_t,1,randi(20),'complex');
    for n = drange(1:size_t)
        for k = 1:K
            x(n) = x(n) + a(k)*exp(phi(k)*1j)*(exp((2*pi*f(k)*1j - alpha(k))/f_sample)).^(n-1);
        end
        x(n) = x(n) + noise(n);
    end
    SNR(iter) = abs(snr((x-noise),noise));
end
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One suggestion is to leverage the vector processing in Matlab and to not add the noise in a for loop, but given the vector of your signal of length $N$ you can simply create the white Gaussian Noise using:

noise = std * sqrt(2)/2 * randn(N,1) + std * sqrt(2)/2* 1j*randn(N,1)

(or randn(1,N) if you prefer row vectors instead of column vectors)

Where std represents the total standard deviation of the noise.

Then the noise can be added to your signal simply using

y = x + noise

Next be aware that the total power as a white noise and the square of the standard deviation std in whatever units are chosen is distributed evenly over the entire frequency range given by -$f_s/2$ to +$f_s/2$ where $f_s$ is the sampling rate. SNR of interest is typically that portion of the noise that shares the same spectrum as the signal. (So if the signal only occupied 1/4 of the total spectrum as sampled, then the noise would be 1/4 or 6 dB lower than that predicted by the total variance of noise).

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  • $\begingroup$ Thank you, Dan Boschen. I read the paper more closely and observed that the SNR is fixed, and the noise is computed for the fixed SNR and signal correspondingly. Based on that, I have written the following code: SNR = -10:5:50; SignalN = awgn(Signal, SNR(l),'measured','dB'); where Signal is the actual signal and SignalN is the noisy signal. The awgn MATLAB function comes handy here. Since I don't know the unit of signal, the signal power is calculated by keyword 'measured' and the unit of SNR is in 'dB' $\endgroup$
    – Neuling
    May 12 '21 at 8:34

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