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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 , and hence named after, by 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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Perspective
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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