convolving noise with noise will get Gaussian? what does that imply?

"kiki" <lunaliu3@yahoo.com> wrote in message news:<cm8mci$sjo$1@news.Stanford.EDU>...
> Suppose I have a segment of data, which is basically random numbers between 
> 0 and 1. I call it "f".
> 
> Doing "f" -- the noise -- self convolution several times, the resultant 
> data, if plotted, is of Gaussian shape.
> 
> I know this is somewhat related to CLT...
> 
> But what does this mean in practice? What does this imply? Does this CLT 
> fact has any implication or application in practice? Any deep thoughts, 
> intuitions?
> 
> Thanks a lot!

Here is something that other have not addressed (or so I thought)
about the application of CLT in general. Couple of direct resultants
of CLT include:- noise that you practically encounter in real systems
is Gaussian.
The fading that signal undergoes in wireless communications is
Rayleigh (when line of sight (LOS) is not available)
-KK