Rice distribution

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.

The characteristic function of the Rice distribution is given as: : \begin{align} \chi_X(t\mid\nu,\sigma) = \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. {} + 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{align} 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 . It is given by: : \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: : \begin{align} \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 \end{align} and, in general, the raw moments are given by : \mu_k^{'}=\sigma^k2^{k/2},\Gamma(1!+!k/2),L_{k/2}(-\nu^2/2\sigma^2).

Here denotes a Laguerre polynomial: : L_q(x)=L_q^{(0)}(x)=M(-q,1,x)=,_1F_1(-q;1;x) where M(a,b,z) = _1F_1(a;b;z) is the confluent hypergeometric function of the first kind. When is even, the raw moments become simple polynomials in and , as in the examples above.

For the case : : \begin{align} L_{1/2}(x) &=,_1F_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{align}

The second central moment, the variance, is : \mu_2= 2\sigma^2+\nu^2-(\pi\sigma^2/2),L^2_{1/2}(-\nu^2/2\sigma^2) .

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

Related distributions

• R \sim \mathrm{Rice}\left(|\nu|,\sigma\right) 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) .
  2. Generate X having a chi-squared distribution with degrees of freedom.
  3. Set R = \sigma\sqrt{X}.
• If R \sim \operatorname{Rice}(\nu,1) then R^2 has a noncentral chi-squared distribution with two degrees of freedom and noncentrality parameter .
• If R \sim \operatorname{Rice}(\nu,1) then R has a noncentral chi distribution with two degrees of freedom and noncentrality parameter .
• If R \sim \operatorname{Rice}(0,\sigma) then , i.e., for the special case of the Rice distribution given by \nu = 0, the distribution becomes the Rayleigh distribution, for which the variance is .
• If R \sim \operatorname{Rice}(0,\sigma) then R^2 has an exponential distribution.
• If R \sim \operatorname{Rice}\left(\nu,\sigma\right) then 1/R has an Inverse Rician distribution.
• The folded normal distribution is the univariate restriction of the Rice distribution.

Limiting cases

For large values of the argument, the Laguerre polynomial becomes : \lim_{x \to -\infty}L_\nu(x)=\frac{|x|^\nu}{\Gamma(1+\nu)}.

It is seen that as becomes large or becomes small, the mean becomes and the variance becomes .

The transition to a Gaussian approximation proceeds as follows. From Bessel function theory we have : I_\alpha(z) \to \frac{e^z}{\sqrt{2\pi z}} \left(1 - \frac{4 \alpha^2 - 1}{8z} + \cdots \right) \text { as } z \rightarrow \infty so, in the large x\nu/\sigma^2 region, an asymptotic expansion of the Rician distribution: : \begin{align} f(x,\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) \ \text{ is } \ & \frac{x}{\sigma^2}\exp\left(\frac{-(x^2 + \nu^2)} {2\sigma^2}\right) \sqrt{\frac{\sigma^2}{2\pi x \nu}} \exp \left( \frac {2x \nu}{2\sigma^2} \right) \left(1 + \frac{\sigma^2}{8x\nu} + \cdots \right)\ \rightarrow {} & \frac{1}{\sigma \sqrt{2 \pi}}\exp\left(-\frac{(x - \nu)^2} {2\sigma^2}\right) \sqrt{ \frac{x}{\nu} } , ;;; \text{ as } \frac{x\nu}{\sigma^2} \rightarrow \infty \end{align}

Moreover, when the density is concentrated around \nu and |x - \nu| \ll \sigma because of the Gaussian exponent, we can also write \sqrt{ {x}/{\nu} } \approx 1 and finally get the Normal approximation : f(x,\nu,\sigma) \approx \frac{1}{\sigma \sqrt{2\pi}} \exp\left(- \frac{(x - \nu)^2}{2\sigma^2}\right) , ;;; \frac{\nu}{\sigma} \gg 1 The approximation becomes usable for .

Parameter estimation (Koay inversion technique)

There are three different methods for estimating the parameters of the Rice distribution, (1) method of moments, (2) method of maximum likelihood, and (3) method of least squares. In the first two methods the interest is in estimating the parameters of the distribution, and , 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 and the sample standard deviation is an estimate of .

The following is an efficient method, known as the "Koay inversion technique". 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 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 , i.e., . 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., , 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, , of , an initial solution is selected, , that is greater than the lower bound, which is {\theta}{\text{lower bound}} = 0 and occurs when r = \sqrt{\pi/(4-\pi)} (Notice that this is the r=\mu^{'}1/\mu^{1/2}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, , i times. In practice, we associate the final \theta{n} for some integer n as the fixed point, , i.e., .

Once the fixed point is found, the estimates \nu and \sigma are found through the scaling function, , as follows: : \sigma = \frac{\mu^{1/2}_2}{\sqrt{\xi\left(\theta^{}\right)}}, and : \nu = \sqrt{\left( \mu^{'~2}_1 + \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. This particular approach is highly efficient.

Applications

• The Euclidean norm of a bivariate circularly symmetric normally distributed random vector.
• Rician fading (for multipath interference))
• Effect of sighting error on target shooting.
• Analysis of diversity receivers in radio communications.
• Distribution of eccentricities for models of the inner Solar System after long-term numerical integration.
• Distribution of noise in magnetic resonance imaging images is rician

References

References

1. Abdi, A. and Tepedelenlioglu, C. and Kaveh, M. and Giannakis, G., [https://dx.doi.org/10.1109/4234.913150 "On the estimation of the K parameter for the Rice fading distribution]", ''IEEE Communications Letters'', March 2001, p. 92–94
2. [[#refLiu2007. Liu 2007 (in one of Horn's confluent hypergeometric functions with two variables).]]
3. [[#refAnnamalai2000. Annamalai 2000 (in a sum of infinite series).]]
4. [[#refErdelyi1953. Erdelyi 1953.]]
5. [[#refSrivastava1985. Srivastava 1985.]]
6. Richards, M.A., [http://users.ece.gatech.edu/mrichard/Rice%20power%20pdf.pdf Rice Distribution for RCS], Georgia Institute of Technology (Sep 2006)
7. Jones, Jessica L., Joyce McLaughlin, and Daniel Renzi. [https://iopscience.iop.org/article/10.1088/1361-6420/aa6163/ampdf "The noise distribution in a shear wave speed image computed using arrival times at fixed spatial positions."], Inverse Problems 33.5 (2017): 055012.
8. Abramowitz and Stegun (1968) [http://www.math.sfu.ca/~cbm/aands/page_508.htm §13.5.1]
9. [[#RefTalukdar. Talukdar et al. 1991]]
10. [[#RefBonny. Bonny et al. 1996]]
11. [[#RefSijbers. Sijbers et al. 1998]]
12. [[#RefDekker2014. den Dekker and Sijbers 2014]]
13. Varadarajan and Haldar 2015]]
14. [[#refKoay2006. Koay et al. 2006 (known as the SNR fixed point formula).]]
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17. (March 2009). "New Series Representation for the Trivariate Non-Central Chi-Squared Distribution". IEEE Transactions on Communications.
18. Laskar, J.. (2008-07-01). "Chaotic diffusion in the Solar System". Icarus.
19. (December 1995). "The rician distribution of noisy mri data". Magnetic Resonance in Medicine.