MATLAB: Adding white gaussian Noise to 1D signals

1d signalagwnsnr

How can I add the same white guassian noise to different signals using awgn ? in fact I need to variate the SNR and compare the results

To produce a unique sequence of white Gaussian noise, you need to reset the sate as follows :

x=sin(2*pi*0.2*(0:0.1:10));
y=awgn(x,10,'measured',10);
y2=awgn(x,10,'measured',10);

the state is set to 10, therefore the signals y and y2 are identical.

Note : the option 'measured' in function's argument means that the function measures the power of input signal before adding the perturbation.

Here are a few pointers which may be able to help you.

Since SNR is the ratio of Signal Power to noise power it should be computed using the following method SNR = snr (x,y-x) since “x” is the original signal and “y” is the noise corrupted signal.
The signal “y” is generated by using “awgn()” function with a random seed there may be some cases where the SNR may not match closely with the pre-defined value.

As a possible workaround you may use any of the following methods:

Specify the ‘seed’ attribute of the “awgn()” function for repetitive generation of noise samples.
Use Monte-Carlo method to generate different “awgn()” noise samples and average over all the SNR values. As the number of Monte- Carlo simulations increase the average SNR closely approximates the predefined SNR.

Please refer to the following documentation link on “awgn()” function

With the syntax

    y = awgn(x,snr);

You generate a white noise vector with a variance of

   variance = 10^(-snr/10);
   noise = sqrt(variance)*randn(size(x));

If you use 'measured', then awgn actually measures the signal power.

For example:

x = cos(pi/4*(0:99));
y = awgn(x,5,'measured');

In this case the variance of the additive white noise is:

    sigp = 10*log10(norm(x,2)^2/numel(x));
    snr = sigp-5;
    noisep = 10^(snr/10);
    noise = sqrt(noisep)*randn(size(x));