How does asymptotic approximation follow in this scenario?

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EngWiPy
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Hello,

I am reading a paper, and the author claimed that in asymptotic sense as M goes to infinite:

[tex]\sum_{i=1}^M\sum_{l=0}^L|h_i(l)|^2=M[/tex]

where:

[tex]\sum_{l=0}^L\mathbb{E}\left\{|h_i(l)|^2\right\}=1[/tex].

How is that asymptotic follows?

Thanks in advance
 
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mathman said:
There are undefined symbols. What kind of things are hi(l)? What is the meaning of E?

E is the expectation, and h are random variables.

I got it, it is just by using the law of large numbers.

Thanks
 
mathman said:
In the equation cited, what is asymptotic? Ratio -> 1 (true) or difference -> 0 (false)?

as M goes to infinite.
 
S_David said:
as M goes to infinite.

I know you mean as M -> ∞, but my question is what is supposed to happening as M -> ∞, expression divided by M -> 1 (true) or expression minus M -> 0 (false)?
 
mathman said:
I know you mean as M -> ∞, but my question is what is supposed to happening as M -> ∞, expression divided by M -> 1 (true) or expression minus M -> 0 (false)?

I am sorry, I did not understand you quiet well. Can you say it in different way, please?
 
chiro said:
I have the feeling that he is dividing by M.

If he is dividing by the M the result would be 1 not M.
 
S_David said:
I am sorry, I did not understand you quiet well. Can you say it in different way, please?
Let ∑(M) be the double sum you are talking about. There are two ways of expressing the limit as M -> ∞, {∑(M)}/M -> 1 (true) or ∑(M) - M -> 0 (false).
 
mathman said:
Let ∑(M) be the double sum you are talking about. There are two ways of expressing the limit as M -> ∞, {∑(M)}/M -> 1 (true) or ∑(M) - M -> 0 (false).

[tex]\lim_{M\xrightarrow{}\infty}\frac{1}{M}\sum_{i=1}^M\sum_{l=0}^L|h(l)|^2=1[/tex]
 
S_David said:
[tex]\lim_{M\xrightarrow{}\infty}\frac{1}{M}\sum_{i=1}^M\sum_{l=0}^L|h(l)|^2=1[/tex]
True. The author is making use of one of the fundamental theorems of probability theory - the law of large numbers.
 
mathman said:
True. The author is making use of one of the fundamental theorems of probability theory - the law of large numbers.

Yes, right. Thanks