power of a signal

Hello;

How can I measure the power of a signal in MATLAB or simulink?

[Yazan]

I am assuming that you have a sampled time-domain signal. The power then can be estimated through:

% example: sinusoidal signal
fs = 1;                 % sampling frequency
t = 0:1/fs:127;         % time instants at which the signal is measured
x = cos(2*pi*0.1*t);
pwrx1 = rms(x)^2;

The power can be estimated through the spectrum.

pwrx2 = bandpower(x, fs, [0 fs/2])

See some details here.

Answer 1

[Image Analyst]

What are the units of your signal? What definition of power are you using? It all depends on what you're starting with and what your definition is. For example with images, the gray level is a unit of energy so to get power you'd have to divide by the exposure time, but then you have to ask if you want the total power integrated over the whole image, or the power on just individual pixels.

[Tina]

my signal is a gaussian noise which is produced by the rand function of matlab.

[Image Analyst]

What are its units? Volts? Watts? Gray levels? It matters!

[Youssef Khmou]

Watts or Decibel

[Image Analyst]

If the units area already watts, then you already have power. You have a power for each and every element. So then what does "power of the signal" mean in that case? The individual elemental power summed or averaged over all elements???

Answer 2

[Youssef Khmou]

hi,

1)Go to Mathw(c) workspace and type :

>>dpslib

2)then go and click on "Estimation" block ,

3) click on "Power Spectrum Estimation" .

4) You have six components : "Periodogram" , "Magnitude FFT","Yule-Walker Method" ," Covariance Method" ,"Burg Method" , and " modified covariance method", choose one of them and link it in your model with a scope to visualize the frequency and the Power .

Note : the Power of AWGN is its STD ( Standard deviation)

[Youssef Khmou]

does this work? if no, then we can see other way

[Tina]

my signal is a gaussian white noise, so its power is its STD? how do I get its STD then?

[Youssef Khmou]

1)dsplib.

2) Statistics .

3) Standard Deviation .