Probability density function
Cumulative distribution function
No simple closed form|
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
Its cumulative distribution function is
where P is the regularized (lower) incomplete gamma function.
The parameters and are
An alternative way of fitting the distribution is to re-parametrize and m as σ = Ω/m and m.
Given independent observations from the Nakagami distribution, the likelihood function is
Its logarithm is
These derivatives vanish only when
and the value of m for which the derivative with respect to m vanishes is found by numerical methods including the Newton–Raphson method.
It can be shown that at the critical point a global maximum is attained, so the critical point is the maximum-likelihood estimate of (m,σ). Because of the equivariance of maximum-likelihood estimation, one then obtains the MLE for Ω as well.
The Nakagami distribution is related to the gamma distribution.
In particular, given a random variable , it is possible to obtain a random variable , by setting , , and taking the square root of :
Alternatively, the Nakagami distribution can be generated from the chi distribution with parameter set to and then following it by a scaling transformation of random variables. That is, a Nakagami random variable is generated by a simple scaling transformation on a Chi-distributed random variable as below.
For a Chi-distribution, the degrees of freedom must be an integer, but for Nakagami the can be any real number greater than 1/2. This is the critical difference and accordingly, Nakagami-m is viewed as a generalization of Chi-distribution, similar to a gamma distribution being considered as a generalization of Chi-squared distributions.
History and applications
The Nakagami distribution is relatively new, being first proposed in 1960. It has been used to model attenuation of wireless signals traversing multiple paths and to study the impact of fading channels on wireless communications.