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Probability density function
Nakagami pdf.svg
Cumulative distribution function
Nakagami cdf.svg
Parameters m\ or\ \mu >= 0.5 shape (real)
\Omega\ or\ \omega > 0 spread (real)
Support x > 0\!
pdf \frac{2m^m}{\Gamma(m)\Omega^m} x^{2m-1} \exp\left(-\frac{m}{\Omega}x^2 \right)
CDF \frac{\gamma \left(m,\frac{m}{\Omega} x^2\right)}{\Gamma(m)}
Mean \frac{\Gamma(m+\frac{1}{2})}{\Gamma(m)}\left(\frac{\Omega}{m}\right)^{1/2}
Median \sqrt{\Omega}\!
Mode \frac{\sqrt{2}}{2} \left(\frac{(2m-1)\Omega}{m}\right)^{1/2}
Variance \Omega\left(1-\frac{1}{m}\left(\frac{\Gamma(m+\frac{1}{2})}{\Gamma(m)}\right)^2\right)

The Nakagami distribution or the Nakagami-m distribution is a probability distribution related to the gamma distribution. It has two parameters: a shape parameter m and a second parameter controlling spread, \Omega.


Its probability density function (pdf) is[1]

 f(x;\,m,\Omega) = \frac{2m^m}{\Gamma(m)\Omega^m}x^{2m-1}\exp\left(-\frac{m}{\Omega}x^2\right).

Its cumulative distribution function is[1]

 F(x;\,m,\Omega) = P\left(m, \frac{m}{\Omega}x^2\right)

where P is the incomplete gamma function (regularized).

Differential equation

\left\{x \Omega  f'(x)+f(x) \left(2 m x^2-2 m \Omega +\Omega
   \right)=0,f(1)=\frac{2 m^m e^{-\frac{m}{\Omega }} \Omega ^{-m}}{\Gamma

Parameter estimation[edit]

The parameters m and \Omega are[2]

 m = \frac{\operatorname{E}^2 \left[X^2 \right]}
                   {\operatorname{Var} \left[X^2 \right]},


 \Omega = \operatorname{E} \left[X^2 \right].

An alternative way of fitting the distribution is to re-parametrize  \Omega and m as σ = Ω/m and m.[3] Then, by taking the derivative of log likelihood with respect to each of the new parameters, the following equations are obtained and these can be solved using the Newton-Raphson method:

 \Gamma(m)= \frac{x^{2m}}{\sigma^m},


 \sigma= \frac{x^2}{m}

It is reported by authors[who?] that modelling data with Nakagami distribution and estimating parameters by above mention method results in better performance for low data regime compared to moments based methods.


The Nakagami distribution is related to the gamma distribution. In particular, given a random variable Y \, \sim \textrm{Gamma}(k, \theta), it is possible to obtain a random variable X \, \sim \textrm{Nakagami} (m, \Omega), by setting k=m, \theta=\Omega / m , and taking the square root of Y:

 X = \sqrt{Y} \,.

The Nakagami distribution f(y; \,m,\Omega) can be generated from the chi distribution with parameter k set to 2m and then following it by a scaling transformation of random variables. That is, a Nakagami random variable X is generated by a simple scaling transformation on a Chi-distributed random variable Y \sim \chi(2m) as below:

 X = \sqrt{(\Omega / 2 m)}\, Y.

History and applications[edit]

The Nakagami distribution is relatively new, being first proposed in 1960.[4] It has been used to model attenuation of wireless signals traversing multiple paths.[5]


  1. ^ a b Laurenson, Dave (1994). "Nakagami Distribution". Indoor Radio Channel Propagation Modelling by Ray Tracing Techniques. Retrieved 2007-08-04. 
  2. ^ R. Kolar, R. Jirik, J. Jan (2004) "Estimator Comparison of the Nakagami-m Parameter and Its Application in Echocardiography", Radioengineering, 13 (1), 8–12
  3. ^ Mitra, Rangeet; Mishra, Amit Kumar; Choubisa, Tarun (2012). "Maximum Likelihood Estimate of Parameters of Nakagami-m Distribution". International Conference on Communications, Devices and Intelligent Systems (CODIS), 2012: 9-12. 
  4. ^ Nakagami, M. (1960) "The m-Distribution, a general formula of intensity of rapid fading". In William C. Hoffman, editor, Statistical Methods in Radio Wave Propagation: Proceedings of a Symposium held June 18-20, 1958, pp 3-36. Pergamon Press.
  5. ^ Parsons, J. D. (1992) The Mobile Radio Propagation Channel. New York: Wiley.

Original courtesy of Wikipedia: http://en.wikipedia.org/wiki/Nakagami_distribution — Please support Wikipedia.
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