In probability theory, the Rice distribution or Rician distribution (or, less commonly, Ricean distribution) is the probability distribution of the magnitude of a circularly-symmetric bivariate normal random variable, possibly with non-zero mean (noncentral). It was named after Stephen O. Rice (1907–1986).
The probability density function is
where I0(z) is the modified Bessel function of the first kind with order zero.
In the context of Rician fading, the distribution is often also rewritten using the Shape Parameter , defined as the ratio of the power contributions by line-of-sight path to the remaining multipaths, and the Scale parameter , defined as the total power received in all paths.
The characteristic function of the Rice distribution is given as:
where is one of Horn's confluent hypergeometric functions with two variables and convergent for all finite values of and . It is given by:
where
is the rising factorial.
The first few raw moments are:
and, in general, the raw moments are given by
Here Lq(x) denotes a Laguerre polynomial:
where is the confluent hypergeometric function of the first kind. When k is even, the raw moments become simple polynomials in σ and ν, as in the examples above.
For the case q = 1/2:
The second central moment, the variance, is
Note that indicates the square of the Laguerre polynomial , not the generalized Laguerre polynomial
if where and are statistically independent normal random variables and is any real number.
Another case where comes from the following steps:
Generate having a Poisson distribution with parameter (also mean, for a Poisson)
Generate having a chi-squared distribution with 2P + 2 degrees of freedom.
Set
If then has a noncentral chi-squared distribution with two degrees of freedom and noncentrality parameter .
If then has a noncentral chi distribution with two degrees of freedom and noncentrality parameter .
If then , i.e., for the special case of the Rice distribution given by , the distribution becomes the Rayleigh distribution, for which the variance is .
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The Nakagami distribution or the Nakagami-m distribution is a probability distribution related to the gamma distribution. The family of Nakagami distributions has two parameters: a shape parameter and a second parameter controlling spread . Its probability density function (pdf) is where Its cumulative distribution function is where P is the regularized (lower) incomplete gamma function. The parameters and are and An alternative way of fitting the distribution is to re-parametrize and m as σ = Ω/m and m.
In probability theory and statistics, the characteristic function of any real-valued random variable completely defines its probability distribution. If a random variable admits a probability density function, then the characteristic function is the Fourier transform of the probability density function. Thus it provides an alternative route to analytical results compared with working directly with probability density functions or cumulative distribution functions.
In probability theory and statistics, the Rayleigh distribution is a continuous probability distribution for nonnegative-valued random variables. Up to rescaling, it coincides with the chi distribution with two degrees of freedom. The distribution is named after Lord Rayleigh (ˈreɪli). A Rayleigh distribution is often observed when the overall magnitude of a vector in the plane is related to its directional components. One example where the Rayleigh distribution naturally arises is when wind velocity is analyzed in two dimensions.
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