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Marcum Q-function
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In statistics, the generalized Marcum Q-function of order is defined as
where and and is the modified Bessel function of first kind of order . If , the integral converges for any . The Marcum Q-function occurs as a complementary cumulative distribution function for noncentral chi, noncentral chi-squared, and Rice distributions. In engineering, this function appears in the study of radar systems, communication systems, queueing system, and signal processing. This function was first studied for by, and hence named after, Jess Marcum for pulsed radars.[1]
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Properties
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Perspective
Finite integral representation
Using the fact that , the generalized Marcum Q-function can alternatively be defined as a finite integral as
However, it is preferable to have an integral representation of the Marcum Q-function such that (i) the limits of the integral are independent of the arguments of the function, (ii) and that the limits are finite, (iii) and that the integrand is a Gaussian function of these arguments. For positive integer values of , such a representation is given by the trigonometric integral[2][3]
where
and the ratio is a constant.
For any real , such finite trigonometric integral is given by[4]
where is as defined before, , and the additional correction term is given by
For integer values of , the correction term tend to vanish.
Monotonicity and log-concavity
- The generalized Marcum Q-function is strictly increasing in and for all and , and is strictly decreasing in for all and [5]
- The function is log-concave on for all [5]
- The function is strictly log-concave on for all and , which implies that the generalized Marcum Q-function satisfies the new-is-better-than-used property.[6]
- The function is log-concave on for all [5]
Series representation
- The generalized Marcum Q function of order can be represented using incomplete Gamma function as[7][8][9]
- where is the lower incomplete Gamma function. This is usually called the canonical representation of the -th order generalized Marcum Q-function.
- The generalized Marcum Q function of order can also be represented using generalized Laguerre polynomials as[9]
- where is the generalized Laguerre polynomial of degree and of order .
- The generalized Marcum Q-function of order can also be represented as Neumann series expansions[4][8]
- where the summations are in increments of one. Note that when assumes an integer value, we have .
- For non-negative half-integer values , we have a closed form expression for the generalized Marcum Q-function as[8][10]
- where is the complementary error function. Since Bessel functions with half-integer parameter have finite sum expansions as[4]
- where is non-negative integer, we can exactly represent the generalized Marcum Q-function with half-integer parameter. More precisely, we have[4]
- for non-negative integers , where is the Gaussian Q-function. Alternatively, we can also more compactly express the Bessel functions with half-integer as sum of hyperbolic sine and cosine functions:[11]
- where , , and for any integer value of .
Recurrence relation and generating function
- Integrating by parts, we can show that generalized Marcum Q-function satisfies the following recurrence relation[8][10]
- The above formula is easily generalized as[10]
- for positive integer . The former recurrence can be used to formally define the generalized Marcum Q-function for negative . Taking and for , we obtain the Neumann series representation of the generalized Marcum Q-function.
- The related three-term recurrence relation is given by[7]
- where
- We can eliminate the occurrence of the Bessel function to give the third order recurrence relation[7]
- Another recurrence relationship, relating it with its derivatives, is given by
- The ordinary generating function of for integral is[10]
- where
Symmetry relation
- Using the two Neumann series representations, we can obtain the following symmetry relation for positive integral
- In particular, for we have
Special values
Some specific values of Marcum-Q function are[6]
- For , by subtracting the two forms of Neumann series representations, we have[10]
- which when combined with the recursive formula gives
- for any non-negative integer .
- For , we have
- For we have
Asymptotic forms
- Assuming to be fixed and large, let , then the generalized Marcum-Q function has the following asymptotic form[7]
- where is given by
- The functions and are given by
- The function satisfies the recursion
- for and
- In the first term of the above asymptotic approximation, we have
- Hence, assuming , the first term asymptotic approximation of the generalized Marcum-Q function is[7]
- where is the Gaussian Q-function. Here as
- For the case when , we have[7]
- Here too as
Differentiation
- We can relate the two partial derivatives as
- The n-th partial derivative of with respect to its arguments is given by[10]
Inequalities
- The generalized Marcum-Q function satisfies a Turán-type inequality[5]
- for all and .
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Bounds
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Based on monotonicity and log-concavity
Various upper and lower bounds of generalized Marcum-Q function can be obtained using monotonicity and log-concavity of the function and the fact that we have closed form expression for when is half-integer valued.
Let and denote the pair of half-integer rounding operators that map a real to its nearest left and right half-odd integer, respectively, according to the relations
where and denote the integer floor and ceiling functions.
- However, the relative error of this bound does not tend to zero when .[5] For integral values of , this bound reduces to
- A very good approximation of the generalized Marcum Q-function for integer valued is obtained by taking the arithmetic mean of the upper and lower bound[15]
- A tighter bound can be obtained by exploiting the log-concavity of on as[5]
- where and for . The tightness of this bound improves as either or increases. The relative error of this bound converges to 0 as .[5] For integral values of , this bound reduces to
Cauchy-Schwarz bound
Using the trigonometric integral representation for integer valued , the following Cauchy-Schwarz bound can be obtained[3]
where .
Exponential-type bounds
For analytical purpose, it is often useful to have bounds in simple exponential form, even though they may not be the tightest bounds achievable. Letting , one such bound for integer valued is given as[16][3]
When , the bound simplifies to give
Another such bound obtained via Cauchy-Schwarz inequality is given as[3]
Chernoff-type bound
Chernoff-type bounds for the generalized Marcum Q-function, where is an integer, is given by[16][3]
where the Chernoff parameter has optimum value of
Semi-linear approximation
The first-order Marcum-Q function can be semi-linearly approximated by [17]
where
and
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Equivalent forms for efficient computation
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Perspective
It is convenient to re-express the Marcum Q-function as[18]
The can be interpreted as the detection probability of incoherently integrated received signal samples of constant received signal-to-noise ratio, , with a normalized detection threshold . In this equivalent form of Marcum Q-function, for given and , we have and . Many expressions exist that can represent . However, the five most reliable, accurate, and efficient ones for numerical computation are given below. They are form one:[18]
form two:[18]
form three:[18]
form four:[18]
and form five:[18]
Among these five form, the second form is the most robust.[18]
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Applications
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Perspective
The generalized Marcum Q-function can be used to represent the cumulative distribution function (cdf) of many random variables:
- If is an exponential distribution with rate parameter , then its cdf is given by
- If is a Erlang distribution with shape parameter and rate parameter , then its cdf is given by
- If is a chi-squared distribution with degrees of freedom, then its cdf is given by
- If is a gamma distribution with shape parameter and rate parameter , then its cdf is given by
- If is a Weibull distribution with shape parameters and scale parameter , then its cdf is given by
- If is a generalized gamma distribution with parameters , then its cdf is given by
- If is a non-central chi-squared distribution with non-centrality parameter and degrees of freedom, then its cdf is given by
- If is a Rayleigh distribution with parameter , then its cdf is given by
- If is a Maxwell–Boltzmann distribution with parameter , then its cdf is given by
- If is a chi distribution with degrees of freedom, then its cdf is given by
- If is a Nakagami distribution with as shape parameter and as spread parameter, then its cdf is given by
- If is a Rice distribution with parameters and , then its cdf is given by
- If is a non-central chi distribution with non-centrality parameter and degrees of freedom, then its cdf is given by
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Footnotes
References
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