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词条 Complex random variable
释义

  1. Definition

  2. Examples

     Simple example  Uniform distribution  Complex Gaussian random variable 

  3. Cumulative distribution function

  4. Probability density function

  5. Expectation

     Definition  Properties 

  6. Variance and pseudo-variance

     Definition variance  Properties  Definition pseudo-variance 

  7. Covariance and pseudo-covariance

     Definition  Properties  Uncorrelatedness  Orthogonality 

  8. Circular symmetry

     Definition  Theorem 

  9. Proper complex random variables

     Definition  Covariance matrix of the real and imaginary parts  Theorem 

  10. Cauchy-Schwarz inequality

  11. Characteristic function

  12. See also

  13. References

In probability theory and statistics, complex random variables are a generalization of real-valued random variables to complex numbers, i.e. the possible values a complex random variable may take are complex numbers.[1] Complex random variables can always be considered as pairs of real random variables: their real and imaginary parts. Therefore, the distribution of one complex random variable may be interpreted as the joint distribution of two real random variables.

Some concepts of real random variables have a straightforward generalization to complex random variables—e.g., the definition of the mean of a complex random variable. Other concepts are unique to complex random variables.

Applications of complex random variables are found in digital signal processing,[2] quadrature amplitude modulation and information theory.

{{Probability fundamentals}}

Definition

A complex random variable on the probability space is a function such that both its real part and its imaginary part are real random variables on .

Examples

Simple example

Consider a random variable that may take only the three complex values with probabilities as specified in the table. This is a simple example of a complex random variable.

Probability Value

The expectation of this random variable may be simply calculated:

Uniform distribution

Another example of a complex random variable is the uniform distribution over the filled unit circle, i.e. the set . This random variable is an example of a complex random variable for which the probability density function is defined. The density function is shown as the yellow disk and dark blue base in the following figure.

Complex Gaussian random variable

Complex Gaussian random variables are often encountered in applications. They are a straightforward generalization of real Gaussian random variables. The following plot shows an example of the distribution of such a variable.

Cumulative distribution function

The generalization of the cumulative distribution function from real to complex random variables is not obvious because expressions of the form make no sense. However expessions of the form make sense. Therefore, we define the cumulative distribution of a complex random variables via the joint distribution of their real and imaginary parts:

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Probability density function

The probability density function of a complex random variable is defined as , i.e. the value of the density function at a point is defined to be equal to the value of the joint density of the real and imaginary parts of the random variable evaluated at the point .

An equivalent definition is given by where and .

As in the real case the density function may not exist.

Expectation

Definition

The expectation of a complex random variable is defined based on the definition of the expectation of a real random variable:[3]{{rp|p. 112}}

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Note that the expectation of a complex random variable does not exist if or does not exist.

If the complex random variable has a probability density function , then the expectation is given by .

If the complex random variable has a probability mass function , then the expectation is given by .

Properties

Whenever the expectation of a complex random variable exists, taking the expectation and complex conjugation commute:

The expected value operator is linear in the sense that

for any complex coefficients even if and are not independent.

Variance and pseudo-variance

Definition variance

The variance is defined as:[3]{{rp|p. 117}}

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Properties

The variance is always a nonnegative real number. It is equal to the sum of the variances of the real and imaginary part of the complex random variable:

The variance of a linear combination of complex random variables may be calculated using the following formula:

Definition pseudo-variance

The pseudo-variance is a special case of the pseudo-covariance and is given by

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Unlike the variance of , which is always real and positive, the pseudo-variance of is in general complex.

Covariance and pseudo-covariance

Definition

The covariance between two complex random variables is defined as[3]{{rp|p. 119}}

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Notice the complex conjugation of the second factor in the definition. In contrast to real random variables, we also define a pseudo-covariance (also called complementary variance):

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The second order statistics are fully characterized by the covariance and the pseudo-covariance.

Properties

The covariance has the following properties:

  • (Conjugate symmetry)
  • (Sesquilinearity)

Uncorrelatedness

{{main|uncorrelatedness (probability theory)}}

Two complex random variables and are called uncorrelated if

Orthogonality

Two complex random variables and are called orthogonal if

.

Circular symmetry

Definition

A complex random variable is called circularly symmetric if for any deterministic the distribution of equals the distribution of .

A typical example of a circular symmetric complex random variable is the complex Gaussian random variable with zero mean and zero pseudo-covariance matrix.

Theorem

The expectation of a circularly symmetric complex random variable is either zero or it is not defined.

Proper complex random variables

The concept of proper random variables is unique to complex random variables, and has no correspondent concept with real random variables.

Definition

A complex random variable is called proper if the following three conditions are all satisfied:

This definition is equivalent to the following conditions. This means that a complex random variable is proper if, and only if:

Covariance matrix of the real and imaginary parts

For a general complex random variable, the pair has the covariance matrix

However, for a proper complex random variable, the covariance matrix of the pair has the following simple form:

.

Theorem

Every circularly symmetric complex random variable with finite variance is proper.

Cauchy-Schwarz inequality

The Cauchy-Schwarz inequality for complex random variables is

.

Characteristic function

The characteristic function of a complex random variable is a function defined by

See also

  • Complex random vector
  • Complex normal distribution

References

1. ^{{cite paper|first1=Jan|last1=Eriksson|first2=Esa|last2=Ollila|first3=Visa|last3=Koivunen|title=Statistics for complex random variables revisited|year=2009}}
2. ^{{cite book | author=Lapidoth, A.| title=A Foundation in Digital Communication| publisher=Cambridge University Press | year=2009 | isbn=9780521193955}}
3. ^1 2 {{cite book | author=Park,Kun Il| title=Fundamentals of Probability and Stochastic Processes with Applications to Communications| publisher=Springer | year=2018 | isbn=978-3-319-68074-3}}

3 : Probability theory|Randomness|Algebra of random variables
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