Rice distribution

	Probability density function
	Cumulative distribution function
Parameters	ν ≥ 0 — distance between the reference point and the center of the bivariate distribution,; σ ≥ 0
Support	x ∈ [0, +∞)
PDF
CDF	where Q1 is the Marcum Q-function
Mean
Variance
Skewness	(complicated)
Ex. kurtosis	(complicated)

In the 2D plane, pick a fixed point at distance ν from the origin. Generate a distribution of 2D points centered around that point, where the x and y coordinates are chosen independently from a gaussian distribution with standard deviation σ (blue region). If R is the distance from these points to the origin, then R has a Rice distribution.

In probability theory, the Rice distribution, Rician distribution or Ricean distribution is the probability distribution of the magnitude of a circular bivariate normal random variable with potentially non-zero mean. It was named after Stephen O. Rice.

Characterization

The probability density function is

f(x\mid \nu ,\sigma )={\frac {x}{\sigma ^{2}}}\exp \left({\frac {-(x^{2}+\nu ^{2})}{2\sigma ^{2}}}\right)I_{0}\left({\frac {x\nu }{\sigma ^{2}}}\right),

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 $K={\frac {\nu ^{2}}{2\sigma ^{2}}}$ , defined as the ratio of the power contributions by line-of-sight path to the remaining multipaths, and the Scale parameter $\Omega =\nu ^{2}+2\sigma ^{2}$ , defined as the total power received in all paths.^[1]

The characteristic function of the Rice distribution is given as:^[2]^[3]

{\begin{aligned}&\chi _{X}(t\mid \nu ,\sigma )\\&\quad =\exp \left(-{\frac {\nu ^{2}}{2\sigma ^{2}}}\right)\left[\Psi _{2}\left(1;1,{\frac {1}{2}};{\frac {\nu ^{2}}{2\sigma ^{2}}},-{\frac {1}{2}}\sigma ^{2}t^{2}\right)\right.\\[8pt]&\left.{}\qquad +i{\sqrt {2}}\sigma t\Psi _{2}\left({\frac {3}{2}};1,{\frac {3}{2}};{\frac {\nu ^{2}}{2\sigma ^{2}}},-{\frac {1}{2}}\sigma ^{2}t^{2}\right)\right],\end{aligned}}

where $\Psi _{2}\left(\alpha ;\gamma ,\gamma ';x,y\right)$ is one of Horn's confluent hypergeometric functions with two variables and convergent for all finite values of $x$ and $y$ . It is given by:^[4]^[5]

\Psi _{2}\left(\alpha ;\gamma ,\gamma ';x,y\right)=\sum _{{n=0}}^{{\infty }}\sum _{{m=0}}^{\infty }{\frac {(\alpha )_{{m+n}}}{(\gamma )_{m}(\gamma ')_{n}}}{\frac {x^{m}y^{n}}{m!n!}},

where

(x)_{n}=x(x+1)\cdots (x+n-1)={\frac {\Gamma (x+n)}{\Gamma (x)}}

is the rising factorial.

Properties

Moments

The first few raw moments are:

\mu _{1}^{{'}}=\sigma {\sqrt {\pi /2}}\,\,L_{{1/2}}(-\nu ^{2}/2\sigma ^{2})

\mu _{2}^{{'}}=2\sigma ^{2}+\nu ^{2}\,

\mu _{3}^{{'}}=3\sigma ^{3}{\sqrt {\pi /2}}\,\,L_{{3/2}}(-\nu ^{2}/2\sigma ^{2})

\mu _{4}^{{'}}=8\sigma ^{4}+8\sigma ^{2}\nu ^{2}+\nu ^{4}\,

\mu _{5}^{{'}}=15\sigma ^{5}{\sqrt {\pi /2}}\,\,L_{{5/2}}(-\nu ^{2}/2\sigma ^{2})

\mu _{6}^{{'}}=48\sigma ^{6}+72\sigma ^{4}\nu ^{2}+18\sigma ^{2}\nu ^{4}+\nu ^{6}\,

and, in general, the raw moments are given by

\mu _{k}^{{'}}=\sigma ^{k}2^{{k/2}}\,\Gamma (1\!+\!k/2)\,L_{{k/2}}(-\nu ^{2}/2\sigma ^{2}).\,

Here L_q(x) denotes a Laguerre polynomial:

L_{q}(x)=L_{q}^{{(0)}}(x)=M(-q,1,x)=\,_{1}F_{1}(-q;1;x)

where $M(a,b,z)=_{1}F_{1}(a;b;z)$ 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:

{\begin{aligned}L_{1/2}(x)&=\,_{1}F_{1}\left(-{\frac {1}{2}};1;x\right)\\&=e^{x/2}\left[\left(1-x\right)I_{0}\left(-{\frac {x}{2}}\right)-xI_{1}\left(-{\frac {x}{2}}\right)\right].\end{aligned}}

The second central moment, the variance, is

\mu _{2}=2\sigma ^{2}+\nu ^{2}-(\pi \sigma ^{2}/2)\,L_{{1/2}}^{2}(-\nu ^{2}/2\sigma ^{2}).

Note that $L_{{1/2}}^{2}(\cdot )$ indicates the square of the Laguerre polynomial $L_{{1/2}}(\cdot )$ , not the generalized Laguerre polynomial $L_{{1/2}}^{{(2)}}(\cdot ).$

Related distributions

$R\sim \mathrm {Rice} \left(|\nu |,\sigma \right)$ has a Rice distribution if $R={\sqrt {X^{2}+Y^{2}}}$ where $X\sim N\left(\nu \cos \theta ,\sigma ^{2}\right)$ and $Y\sim N\left(\nu \sin \theta ,\sigma ^{2}\right)$ are statistically independent normal random variables and $\theta$ is any real number.
Another case where $R\sim {\mathrm {Rice}}\left(\nu ,\sigma \right)$ comes from the following steps:

1. Generate

P

having a Poisson distribution with parameter (also mean, for a Poisson)

\lambda ={\frac {\nu ^{2}}{2\sigma ^{2}}}.

2. Generate

X

having a chi-squared distribution with 2P + 2 degrees of freedom.

3. Set

R=\sigma {\sqrt {X}}.

If $R\sim {\text{Rice}}\left(\nu ,1\right)$ then $R^{2}$ has a noncentral chi-squared distribution with two degrees of freedom and noncentrality parameter $\nu ^{2}$ .
If $R\sim {\text{Rice}}\left(\nu ,1\right)$ then $R$ has a noncentral chi distribution with two degrees of freedom and noncentrality parameter $\nu$ .
If $R\sim {\text{Rice}}\left(0,\sigma \right)$ then $R\sim {\text{Rayleigh}}\left(\sigma \right)$ , i.e., for the special case of the Rice distribution given by ν = 0, the distribution becomes the Rayleigh distribution, for which the variance is $\mu _{2}={\frac {4-\pi }{2}}\sigma ^{2}$ .
If $R\sim {\text{Rice}}\left(0,\sigma \right)$ then $R^{2}$ has an exponential distribution.^[6]

Limiting cases

For large values of the argument, the Laguerre polynomial becomes^[7]

\lim _{{x\rightarrow -\infty }}L_{\nu }(x)={\frac {|x|^{\nu }}{\Gamma (1+\nu )}}.

It is seen that as ν becomes large or σ becomes small the mean becomes ν and the variance becomes σ².

Parameter estimation (the Koay inversion technique)

There are three different methods for estimating the parameters of the Rice distribution, (1) method of moments,^[8]^[9]^[10]^[11] (2) method of maximum likelihood,^[8]^[9]^[10] and (3) method of least squares. In the first two methods the interest is in estimating the parameters of the distribution, ν and σ, from a sample of data. This can be done using the method of moments, e.g., the sample mean and the sample standard deviation. The sample mean is an estimate of μ₁^' and the sample standard deviation is an estimate of μ₂^1/2.

The following is an efficient method, known as the "Koay inversion technique".^[12] for solving the estimating equations, based on the sample mean and the sample standard deviation, simultaneously . This inversion technique is also known as the fixed point formula of SNR. Earlier works^[8]^[13] on the method of moments usually use a root-finding method to solve the problem, which is not efficient.

First, the ratio of the sample mean to the sample standard deviation is defined as r, i.e., $r=\mu _{1}^{{'}}/\mu _{2}^{{1/2}}$ . The fixed point formula of SNR is expressed as

g(\theta )={\sqrt {\xi {(\theta )}\left[1+r^{2}\right]-2}},

where $\theta$ is the ratio of the parameters, i.e., $\theta ={\frac {\nu }{\sigma }}$ , and $\xi {\left(\theta \right)}$ is given by:

\xi {\left(\theta \right)}=2+\theta ^{2}-{\frac {\pi }{8}}\exp {(-\theta ^{2}/2)}\left[(2+\theta ^{2})I_{0}(\theta ^{2}/4)+\theta ^{2}I_{1}(\theta ^{{2}}/4)\right]^{2},

where $I_{0}$ and $I_{1}$ are modified Bessel functions of the first kind.

Note that $\xi {\left(\theta \right)}$ is a scaling factor of $\sigma$ and is related to $\mu _{{2}}$ by:

\mu _{2}=\xi {\left(\theta \right)}\sigma ^{2}.\,

To find the fixed point, $\theta ^{*}$ , of $g$ , an initial solution is selected, ${\theta }_{{0}}$ , that is greater than the lower bound, which is ${\theta }_{{{\mathrm {lowerbound}}}}=0$ and occurs when $r={\sqrt {\pi /(4-\pi )}}$ ^[12] (Notice that this is the $r=\mu _{1}^{{'}}/\mu _{2}^{{1/2}}$ of a Rayleigh distribution). This provides a starting point for the iteration, which uses functional composition, and this continues until $\left|g^{{i}}\left(\theta _{{0}}\right)-\theta _{{i-1}}\right|$ is less than some small positive value. Here, $g^{{i}}$ denotes the composition of the same function, $g$ , $i$ times. In practice, we associate the final $\theta _{{n}}$ for some integer $n$ as the fixed point, $\theta^{*}$ , i.e., $\theta ^{{*}}=g\left(\theta ^{{*}}\right)$ .

Once the fixed point is found, the estimates $\nu$ and $\sigma$ are found through the scaling function, $\xi {\left(\theta \right)}$ , as follows:

\sigma ={\frac {\mu _{2}^{{1/2}}}{{\sqrt {\xi \left(\theta ^{{*}}\right)}}}},

and

\nu ={\sqrt {\left(\mu _{1}^{{'~2}}+\left(\xi \left(\theta ^{{*}}\right)-2\right)\sigma ^{2}\right)}}.

To speed up the iteration even more, one can use the Newton's method of root-finding.^[12] This particular approach is highly efficient.

Applications

The Euclidean norm of a bivariate normally distributed random vector.
Rician fading
Effect of sighting error on target shooting.^[14]

Notes

↑ Abdi, A. and Tepedelenlioglu, C. and Kaveh, M. and Giannakis, G., "On the estimation of the K parameter for the Rice fading distribution", IEEE Communications Letters, March 2001, p. 92 -94
↑ Liu 2007 (in one of Horn's confluent hypergeometric functions with two variables).
↑ Annamalai 2000 (in a sum of infinite series).
↑ Erdelyi 1953.
↑ Srivastava 1985.
↑ Richards, M.A., Rice Distribution for RCS, Georgia Institute of Technology (Sep 2006)
↑ Abramowitz and Stegun (1968) §13.5.1
1 2 3 Talukdar et al. 1991
1 2 Bonny et al. 1996
1 2 Sijbers et al. 1998
↑ den Dekker and Sijbers 2014
1 2 3 Koay et al. 2006 (known as the SNR fixed point formula).
↑ Abdi 2001
↑ "Ballistipedia". Retrieved 4 May 2014.

References

Abramowitz, M. and Stegun, I. A. (ed.), Handbook of Mathematical Functions, National Bureau of Standards, 1964; reprinted Dover Publications, 1965. ISBN 0-486-61272-4
Rice, S. O., Mathematical Analysis of Random Noise. Bell System Technical Journal 24 (1945) 46–156.
I. Soltani Bozchalooi and Ming Liang (20 November 2007). "A smoothness index-guided approach to wavelet parameter selection in signal de-noising and fault detection". Journal of Sound and Vibration. 308 (1–2): 253–254. doi:10.1016/j.jsv.2007.07.038.
Dong Wang, Qiang Zhou, Kwok-Leung Tsui. On the distribution of the modulus of Gabor wavelet coefficients and the upper bound of the dimensionless smoothness index in the case of additive Gaussian noises: Revisited. Journal of Sound and Vibration. 2017 May 12;395:393-400.
Liu, X. and Hanzo, L., A Unified Exact BER Performance Analysis of Asynchronous DS-CDMA Systems Using BPSK Modulation over Fading Channels, IEEE Transactions on Wireless Communications, Volume 6, Issue 10, October 2007, Pages 3504–3509.
Annamalai, A., Tellambura, C. and Bhargava, V. K., Equal-Gain Diversity Receiver Performance in Wireless Channels, IEEE Transactions on Communications,Volume 48, October 2000, Pages 1732–1745.
Erdelyi, A., Magnus, W., Oberhettinger, F. and Tricomi, F. G., Higher Transcendental Functions, Volume 1. McGraw-Hill Book Company Inc., 1953.
Srivastava, H. M. and Karlsson, P. W., Multiple Gaussian Hypergeometric Series. Ellis Horwood Ltd., 1985.
Sijbers J., den Dekker A. J., Scheunders P. and Van Dyck D., "Maximum Likelihood estimation of Rician distribution parameters", IEEE Transactions on Medical Imaging, Vol. 17, Nr. 3, p. 357–361, (1998)
den Dekker, A.J., and Sijbers, J (December 2014). "Data distributions in magnetic resonance images: a review". Physica Medica. 30 (7): 725–741. doi:10.1016/j.ejmp.2014.05.002.
Koay, C.G. and Basser, P. J., Analytically exact correction scheme for signal extraction from noisy magnitude MR signals, Journal of Magnetic Resonance, Volume 179, Issue = 2, p. 317–322, (2006)
Abdi, A., Tepedelenlioglu, C., Kaveh, M., and Giannakis, G. On the estimation of the K parameter for the Rice fading distribution, IEEE Communications Letters, Volume 5, Number 3, March 2001, Pages 92–94.
Talukdar, K.K., and Lawing, William D. (March 1991). "Estimation of the parameters of the Rice distribution". Journal of the Acoustical Society of America. 89 (3): 1193–1197. doi:10.1121/1.400532.
Bonny,J.M., Renou, J.P., and Zanca, M. (November 1996). "Optimal Measurement of Magnitude and Phase from MR Data". Journal of Magnetic Resonance, Series B. 113 (2): 136–144. doi:10.1006/jmrb.1996.0166.

External links

MATLAB code for Rice/Rician distribution (PDF, mean and variance, and generating random samples)

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[1] Abdi, A. and Tepedelenlioglu, C. and Kaveh, M. and Giannakis, G., "On the estimation of the K parameter for the Rice fading distribution", IEEE Communications Letters, March 2001, p. 92 -94

[2] Liu 2007 (in one of Horn's confluent hypergeometric functions with two variables).

[3] Annamalai 2000 (in a sum of infinite series).

[4] Erdelyi 1953.

[5] Srivastava 1985.

[6] Richards, M.A., Rice Distribution for RCS, Georgia Institute of Technology (Sep 2006)

[7] Abramowitz and Stegun (1968) §13.5.1

[T-8] 1 2 3 Talukdar et al. 1991

[B-9] 1 2 Bonny et al. 1996

[S-10] 1 2 Sijbers et al. 1998

[S2-11] Dekker and Sijbers 2014

[K-12] 1 2 3 Koay et al. 2006 (known as the SNR fixed point formula).

[13] Abdi 2001

[14] "Ballistipedia". Retrieved 4 May 2014.

Probability distributions
List
Discrete univariate with finite support	Benford Bernoulli beta-binomial binomial categorical hypergeometric Poisson binomial Rademacher discrete uniform Zipf Zipf–Mandelbrot
Discrete univariate with infinite support	beta negative binomial Borel Conway–Maxwell–Poisson discrete phase-type Delaporte extended negative binomial Gauss–Kuzmin geometric logarithmic negative binomial parabolic fractal Poisson Skellam Yule–Simon zeta
Continuous univariate supported on a bounded interval	arcsine ARGUS Balding–Nichols Bates beta beta rectangular Irwin–Hall Kumaraswamy logit-normal noncentral beta raised cosine reciprocal triangular U-quadratic uniform Wigner semicircle
Continuous univariate supported on a semi-infinite interval	Benini Benktander 1st kind Benktander 2nd kind beta prime Burr chi-squared chi Dagum Davis exponential-logarithmic Erlang exponential F folded normal Flory–Schulz Fréchet gamma gamma/Gompertz generalized inverse Gaussian Gompertz half-logistic half-normal Hotelling's T-squared hyper-Erlang hyperexponential hypoexponential inverse chi-squared scaled inverse chi-squared inverse Gaussian inverse gamma Kolmogorov Lévy log-Cauchy log-Laplace log-logistic log-normal Lomax matrix-exponential Maxwell–Boltzmann Maxwell–Jüttner Mittag-Leffler Nakagami noncentral chi-squared Pareto phase-type poly-Weibull Rayleigh relativistic Breit–Wigner Rice shifted Gompertz truncated normal type-2 Gumbel Weibull Discrete Weibull Wilks's lambda
Continuous univariate supported on the whole real line	Cauchy exponential power Fisher's z Gaussian q generalized normal generalized hyperbolic geometric stable Gumbel Holtsmark hyperbolic secant Johnson's S_U Landau Laplace asymmetric Laplace logistic noncentral t normal (Gaussian) normal-inverse Gaussian skew normal slash stable Student's t type-1 Gumbel Tracy–Widom variance-gamma Voigt
Continuous univariate with support whose type varies	generalized extreme value generalized Pareto Marchenko–Pastur q-exponential q-Gaussian q-Weibull shifted log-logistic Tukey lambda
Mixed continuous-discrete univariate	rectified Gaussian
Multivariate (joint)	Discrete Ewens multinomial Dirichlet-multinomial negative multinomial Continuous Dirichlet generalized Dirichlet multivariate Laplace multivariate normal multivariate stable multivariate t normal-inverse-gamma normal-gamma Matrix-valued inverse matrix gamma inverse-Wishart matrix normal matrix t matrix gamma normal-inverse-Wishart normal-Wishart Wishart
Directional	Univariate (circular) directional Circular uniform univariate von Mises wrapped normal wrapped Cauchy wrapped exponential wrapped asymmetric Laplace wrapped Lévy Bivariate (spherical) Kent Bivariate (toroidal) bivariate von Mises Multivariate von Mises–Fisher Bingham
Degenerate and singular	Degenerate Dirac delta function Singular Cantor
Families	Circular compound Poisson elliptical exponential natural exponential location–scale maximum entropy mixture Pearson Tweedie wrapped