I want to ask some details about the SNR estimation techniques. How to estimate SNR/ noise in the signal processing?
Hi...
You always have (in most cases) access to the received power of your signal P.
So, the problem boils down to estimate the noise power N, SNR = P/N.
Estimating N could be done through Maximum Likelihood techniques, Least-Square techniques, in case the noise is additive white Gaussian noise.
In case the noise is coloured (correlated through time), Kalman filtering could be used, higher orders of autoregression.
it really depends on the application you are in.
Thank you for your answer,
Could you please suggest me some papers about the noise power estimation using these methods for both cases (AWGN, coloured noise)
Thanks,
I honestly have no idea on papers, but you could refer to books on estimation applied to communications. You could search for that, instead.
An example might help you to understand the concept ...
In the time domain, if you know that your signal is well approximated locally by a polynomial of degree D, then you would fit that polynomial to the measured waveform, over a finite analysis window. The signal power would be the power of the fitted polynomial (or the sum of squared polynomial coefficients), whereas the noise power would be the sum-of-squared residuals.
Alternatively, if your signal is better modelled as a sinusoid, then use a set of complex sinusoids that are orthonormal over the analysis interval instead of the polynomial. Of course, this is done most efficiently in the frequency domain, using the FFT.
The SNR estimation depends on the communication scenario. There is SNR expressions for equlizers, Rake receiver, MIMO, ..., etc
Numerous studies are available in litterature.
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