Expected value of complex gaussian

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Discussion Overview

The discussion revolves around the expected value of the expression exp(|z+\mu|), where z is a complex variable defined as z=x+jy, with x and y being independent Gaussian random variables. Participants explore the challenges of calculating this expectation, particularly in obtaining a closed form for the integral involved.

Discussion Character

  • Exploratory
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant poses the question of how to find the expected value of exp(|z+\mu|) and notes difficulties in obtaining a Gaussian integral for the expectation.
  • Another participant suggests a substitution that simplifies one of the integrals, though they have not verified the other integral.
  • A participant presents a complex integral result involving the Q-function and expresses uncertainty about finding a closed form for it.
  • There is a correction made to an earlier result, clarifying the expression involving the Q-function and discussing the implications of taking the integral with respect to different variables.
  • One participant questions the derivation of the Q-function and discusses the implications of a linear substitution on the integral limits.
  • Another participant points out that the lower limit of an integral does not remain zero after expanding the expression, indicating a potential issue with the substitution used.

Areas of Agreement / Disagreement

Participants express differing views on the derivation of the Q-function and the implications of various substitutions. There is no consensus on how to achieve a closed form for the expected value, and the discussion remains unresolved.

Contextual Notes

Participants highlight limitations in their approaches, including unresolved mathematical steps and the dependence on specific substitutions that affect the integral limits.

jashua
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What is the expected value of the following expression

exp(|z+\mu|),

where \mu is a real constant and z=x+jy such that x and y are independent Gaussian random variables each with zero mean and \sigma^2 variance.

When I try to take the expectation, I couldn't obtain a gaussian integral, so I couldn't take the expectation. So, can we obtain the expected value of the above exponential in a closed form?
 
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After a substitution ##y=r\cos(\phi)##, ##x=r \sin(\phi)-\mu##, the one of the two integrals looks possible. I did not check the other integral.
 
Thank you for your reply. Then, I have carried out the following integral:

\frac{1}{2\pi\sigma^2} \int_{0}^{2\pi}\int_{0}^{∞} exp({-\frac{r^2-2r\mu sin(\phi) + 2\sigma^2r + \mu^2}{2\sigma^2}})rdrd\phi

The result is

exp({-\frac{\mu^2}{2\sigma^2}})\frac{1}{\sqrt{2\pi\sigma^2}}\int_0^{2\pi} (\mu sin(\phi) - \sigma^2) exp(\frac{(\mu sin(\phi) - \sigma^2)^2}{2\sigma^2})Q(-\frac{\mu sin(\phi) - \sigma^2}{\sigma})d\phi

If I've correctly found this result, even the first integral does not have a closed form due to the Q-function. So, any idea to find an approximate closed form for this integral?
 
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How do you get that Q function? A linear substitution simplifies the first integral to ##(r+r_0)e^{-r^2}## (neglecting prefactors), which gives a Gaussian integral and a component with an antiderivative.
 
First, let me correct my result that I have given above. The correct version will be as follows:

exp({-\frac{\mu^2}{2\sigma^2}})(1+\frac{1}{\sqrt{2\pi\sigma^2}}\int_0^{2\pi} (\mu sin(\phi) - \sigma^2) exp(\frac{(\mu sin(\phi) - \sigma^2)^2}{2\sigma^2})Q(-\frac{\mu sin(\phi) - \sigma^2}{\sigma})d\phi)The Q-function has the following form:

Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{∞}exp(-r^2/2)dr

So, the term corresponding to r_0e^{-r^2} can be expressed by the Q-function. If we go other way around, I mean if we take integral w.r.t. \phi first, then we get a Bessel function of the first kind. In either case, we don't have a closed form. So, can we get an approximate closed form?
 
Last edited:
I don't see how you get the Q function.
$$\int_0^\infty r_0 e^{-r^2} dr = r_0 \int_0^\infty e^{-r^2} dr = r_0 \frac{\sqrt{\pi}}{2}$$Edit: Oh, that is a result of the linear substitution.
Okay, I see... and I don't know how to avoid that.
 
The lower limit does not become 0 if you expand the expression. I mean the linear substitution changes the lower limit.
 

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