EXERCISE 4.1.

Consider 2,048 samples of a Gaussian white random process *P* with a unit mean value and a variance of 0.25. Check if it is a Gaussian random variable using different tests.

% Random process generation P=0.5*randn(1,2048)+1; mp = mean(P) vp = var(P)

1^{st} test: the simplest test uses the random process histogram.

bins = 20; hist(P,bins);

It can easily be seen that the obtained histogram is very similar to a Gaussian pdf.

2^{nd} test: Let us calculate 3^{rd} and 4^{th} order cumulants of the random process *P* using equations [4.6] and [4.7].

Cum3 = mean(P.^3) - 3*mean(P)*mean(P.^2)+2* mean(P)^3 Cum4 = mean(P.^4) - 4*mean(P)*mean(P.^3) - 3*mean(P.^2)^2 + 12*mean(P)^2*mean(P.^2)- 6*mean(P)^4 Cum3 = 0.0234 Cum4 = 0.0080

The obtained values for the two cumulants are close to zero, so the Gaussianity hypothesis on *P* is reinforced.

3^{rd} test: Henry line.

nb_bins = 20; [hv,x]=hist(P,nb_bins); F=cumsum(hv)/sum(hv); F(end)=0.999 ;% empirical cdf y=norminv(F,0,1); % percentile of a N(0,1) distributed variable % Graphical matching p=polyfit(x,y,1); % linear matching of x and y s=1/p(1) % standard deviation estimation = inverse of the slope z=polyval(p,x); % values on the Henry line figure; hold on; plot(x,z,'-r'); plot (x,y,'*'); legend.('Henry line', 'Couples (x,t)'); xlabel('x'); ylabel('t');

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