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]
Properties
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
![{\displaystyle C_{\nu }(a,b)={\frac {\sin(\nu \pi )}{\pi }}\exp \left(-{\frac {a^{2}+b^{2}}{2}}\right)\int _{0}^{1}{\frac {(x/\zeta )^{\nu -1}}{\zeta +x}}\exp \left[-{\frac {ab}{2}}\left(x+{\frac {1}{x}}\right)\right]\mathrm {d} x.}](./_assets_/b4bfd0790bab5e68802f462e3ded846e347513c4.svg)
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.

- 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]
![{\displaystyle Q_{n+1/2}(a,b)={\frac {1}{2}}\left[\mathrm {erfc} \left({\frac {b-a}{\sqrt {2}}}\right)+\mathrm {erfc} \left({\frac {b+a}{\sqrt {2}}}\right)\right]+e^{-(a^{2}+b^{2})/2}\sum _{k=1}^{n}\left({\frac {b}{a}}\right)^{k-1/2}I_{k-1/2}(ab),}](./_assets_/5a428bae3ef60ce3fd57d471242e060896dcfcdd.svg)
- where
is the complementary error function. Since Bessel functions with half-integer parameter have finite sum expansions as[4]
![{\displaystyle I_{\pm (n+0.5)}(z)={\frac {1}{\sqrt {\pi }}}\sum _{k=0}^{n}{\frac {(n+k)!}{k!(n-k)!}}\left[{\frac {(-1)^{k}e^{z}\mp (-1)^{n}e^{-z}}{(2z)^{k+0.5}}}\right],}](./_assets_/5cf71534e8902e7bcdcd2f8aad1371d75acd8ded.svg)
- where
is non-negative integer, we can exactly represent the generalized Marcum Q-function with half-integer parameter. More precisely, we have[4]
![{\displaystyle Q_{n+1/2}(a,b)=Q(b-a)+Q(b+a)+{\frac {1}{b{\sqrt {2\pi }}}}\sum _{i=1}^{n}\left({\frac {b}{a}}\right)^{i}\sum _{k=0}^{i-1}{\frac {(i+k-1)!}{k!(i-k-1)!}}\left[{\frac {(-1)^{k}e^{-(a-b)^{2}/2}+(-1)^{i}e^{-(a+b)^{2}/2}}{(2ab)^{k}}}\right],}](./_assets_/cb1b5fea182c9323844f545c9f4fecbd6bbd52f0.svg)
- 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]
![{\displaystyle I_{n+{\frac {1}{2}}}(z)={\sqrt {\frac {2z}{\pi }}}\left[g_{n}(z)\sinh(z)+g_{-n-1}(z)\cosh(z)\right],}](./_assets_/b8203307b336247b9bb82d5a285ae80bf8ae636f.svg)
- 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

![{\displaystyle Q_{n}(a,b)+Q_{n}(b,a)=1+e^{-(a^{2}+b^{2})/2}\left[I_{0}(ab)+\sum _{k=1}^{n-1}{\frac {a^{2k}+b^{2k}}{(ab)^{k}}}I_{k}(ab)\right].}](./_assets_/111af20e0c19f477e3a8dfa8ac10de16fa86c349.svg)
- 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]
![{\displaystyle Q_{1}(a,a)={\frac {1}{2}}[1+e^{-a^{2}}I_{0}(a^{2})],}](./_assets_/4c38df9d1222ad59bb196d3a78a9c777616ed608.svg)
- which when combined with the recursive formula gives
![{\displaystyle Q_{n}(a,a)={\frac {1}{2}}[1+e^{-a^{2}}I_{0}(a^{2})]+e^{-a^{2}}\sum _{k=1}^{n-1}I_{k}(a^{2}),}](./_assets_/68cc4549662df9a89776595cc88a356f69fd6145.svg)
![{\displaystyle Q_{-n}(a,a)={\frac {1}{2}}[1+e^{-a^{2}}I_{0}(a^{2})]-e^{-a^{2}}\sum _{k=1}^{n}I_{k}(a^{2}),}](./_assets_/b52cda4bd72cf9d005a8b44bfac3f06f8256f8a7.svg)
- for any non-negative integer
.
- For
, using the basic integral definition of generalized Marcum Q-function, we have[8][10]
![{\displaystyle Q_{1/2}(a,b)={\frac {1}{2}}\left[\mathrm {erfc} \left({\frac {b-a}{\sqrt {2}}}\right)+\mathrm {erfc} \left({\frac {b+a}{\sqrt {2}}}\right)\right].}](./_assets_/081dd40ae7e5bad0c354d6665df00fd3b2a849f3.svg)
- For
, we have

- For
we have

- Assuming
to be fixed and
large, let
, then the generalized Marcum-Q function has the following asymptotic form[7]

- where
is given by
![{\displaystyle \psi _{n}={\frac {1}{2\zeta ^{\nu }{\sqrt {2\pi }}}}(-1)^{n}\left[A_{n}(\nu -1)-\zeta A_{n}(\nu )\right]\phi _{n}.}](./_assets_/50fcfb6150a24e3d1dce191c58b88e7e7002b544.svg)
- The functions
and
are given by
![{\displaystyle \phi _{n}=\left[{\frac {(b-a)^{2}}{2ab}}\right]^{n-{\frac {1}{2}}}\Gamma \left({\frac {1}{2}}-n,{\frac {(b-a)^{2}}{2}}\right),}](./_assets_/21223323ed47dee3268eeea68fd5f93e901aa862.svg)

- 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
- The partial derivative of
with respect to
and
is given by[12][13]
![{\displaystyle {\frac {\partial }{\partial a}}Q_{\nu }(a,b)=a\left[Q_{\nu +1}(a,b)-Q_{\nu }(a,b)\right]=a\left({\frac {b}{a}}\right)^{\nu }e^{-(a^{2}+b^{2})/2}I_{\nu }(ab),}](./_assets_/8f2cc9b22aba8aa7cb19409f5faf43265b66eaf1.svg)
![{\displaystyle {\frac {\partial }{\partial b}}Q_{\nu }(a,b)=b\left[Q_{\nu -1}(a,b)-Q_{\nu }(a,b)\right]=-b\left({\frac {b}{a}}\right)^{\nu -1}e^{-(a^{2}+b^{2})/2}I_{\nu -1}(ab).}](./_assets_/d88cbcc59b5f3befb338fdf1de70b3c2da92d9f1.svg)
- We can relate the two partial derivatives as

- The n-th partial derivative of
with respect to its arguments is given by[10]
![{\displaystyle {\frac {\partial ^{n}}{\partial a^{n}}}Q_{\nu }(a,b)=n!(-a)^{n}\sum _{k=0}^{[n/2]}{\frac {(-2a^{2})^{-k}}{k!(n-2k)!}}\sum _{p=0}^{n-k}(-1)^{p}{\binom {n-k}{p}}Q_{\nu +p}(a,b),}](./_assets_/c5590991b00232c6727b21e105e569505a6dbf1e.svg)
![{\displaystyle {\frac {\partial ^{n}}{\partial b^{n}}}Q_{\nu }(a,b)={\frac {n!a^{1-\nu }}{2^{n}b^{n-\nu +1}}}e^{-(a^{2}+b^{2})/2}\sum _{k=[n/2]}^{n}{\frac {(-2b^{2})^{k}}{(n-k)!(2k-n)!}}\sum _{p=0}^{k-1}{\binom {k-1}{p}}\left(-{\frac {a}{b}}\right)^{p}I_{\nu -p-1}(ab).}](./_assets_/681bd1a41b56319db7186f3d9a222895769d2fb5.svg)
Inequalities

- for all
and
.
Bounds
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.
- The monotonicity of the function
for all
and
gives us the following simple bound[14][8][15]

- 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]
![{\displaystyle e^{-b^{2}/2}\leq Q_{n}(a,b)\leq \exp \left[-{\frac {1}{2}}(b^{2}+a^{2})\right]{\sqrt {I_{0}(2ab)}}{\sqrt {{\frac {2n-1}{2}}+{\frac {\zeta ^{2(1-n)}}{2(1-\zeta ^{2})}}}},\qquad \zeta <1,}](./_assets_/02d46d206be0ca46610cef7174d050284dbc0dbb.svg)
![{\displaystyle 1-Q_{n}(a,b)\leq \exp \left[-{\frac {1}{2}}(b^{2}+a^{2})\right]{\sqrt {I_{0}(2ab)}}{\sqrt {\frac {\zeta ^{2(1-n)}}{2(\zeta ^{2}-1)}}},\qquad \zeta >1,}](./_assets_/11e778e3829a7ee929fb89c5be6366c74d6dbbcd.svg)
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]
![{\displaystyle e^{-(b+a)^{2}/2}\leq Q_{n}(a,b)\leq e^{-(b-a)^{2}/2}+{\frac {\zeta ^{1-n}-1}{\pi (1-\zeta )}}\left[e^{-(b-a)^{2}/2}-e^{-(b+a)^{2}/2}\right],\qquad \zeta <1,}](./_assets_/0a15652302942f992ebaa2f3bb228fbef2e67873.svg)
![{\displaystyle Q_{n}(a,b)\geq 1-{\frac {1}{2}}\left[e^{-(a-b)^{2}/2}-e^{-(a+b)^{2}/2}\right],\qquad \zeta >1.}](./_assets_/8af3a0d51a033c39d5171aef33749366c3f51e9e.svg)
When
, the bound simplifies to give

![{\displaystyle 1-{\frac {1}{2}}\left[e^{-(a-b)^{2}/2}-e^{-(a+b)^{2}/2}\right]\leq Q_{1}(a,b),\qquad \zeta >1.}](./_assets_/b64cb05867f6c30fa46843b92e83306b4ad28ba6.svg)
Another such bound obtained via Cauchy-Schwarz inequality is given as[3]
![{\displaystyle e^{-b^{2}/2}\leq Q_{n}(a,b)\leq {\frac {1}{2}}{\sqrt {{\frac {2n-1}{2}}+{\frac {\zeta ^{2(1-n)}}{2(1-\zeta ^{2})}}}}\left[e^{-(b-a)^{2}/2}+e^{-(b+a)^{2}/2}\right],\qquad \zeta <1}](./_assets_/725799b4e54a32be7d4fc5b81998dd881564b41b.svg)
![{\displaystyle Q_{n}(a,b)\geq 1-{\frac {1}{2}}{\sqrt {\frac {\zeta ^{2(1-n)}}{2(\zeta ^{2}-1)}}}\left[e^{-(b-a)^{2}/2}+e^{-(b+a)^{2}/2}\right],\qquad \zeta >1.}](./_assets_/4e5c4439c23548bc0ac3e71f95478389b5cdd35f.svg)
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

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]
Applications
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 
- ^ J.I. Marcum (1960). A statistical theory of target detection by pulsed radar: mathematical appendix, IRE Trans. Inform. Theory, vol. 6, 59-267.
- ^ M.K. Simon and M.-S. Alouini (1998). A Unified Approach to the Performance of Digital Communication over Generalized Fading Channels, Proceedings of the IEEE, 86(9), 1860-1877.
- ^ a b c d e A. Annamalai and C. Tellambura (2001). Cauchy-Schwarz bound on the generalized Marcum-Q function with applications, Wireless Communications and Mobile Computing, 1(2), 243-253.
- ^ a b c d A. Annamalai and C. Tellambura (2008). A Simple Exponential Integral Representation of the Generalized Marcum Q-Function QM(a,b) for Real-Order M with Applications. 2008 IEEE Military Communications Conference, San Diego, CA, USA
- ^ a b c d e f g Y. Sun, A. Baricz, and S. Zhou (2010). On the Monotonicity, Log-Concavity, and Tight Bounds of the Generalized Marcum and Nuttall Q-Functions. IEEE Transactions on Information Theory, 56(3), 1166–1186, ISSN 0018-9448
- ^ a b Y. Sun and A. Baricz (2008). Inequalities for the generalized Marcum Q-function. Applied Mathematics and Computation 203(2008) 134-141.
- ^ a b c d e f N.M. Temme (1993). Asymptotic and numerical aspects of the noncentral chi-square distribution. Computers Math. Applic., 25(5), 55-63.
- ^ a b c d e f A. Annamalai, C. Tellambura and John Matyjas (2009). "A New Twist on the Generalized Marcum Q-Function QM(a, b) with Fractional-Order M and its Applications". 2009 6th IEEE Consumer Communications and Networking Conference, 1–5, ISBN 978-1-4244-2308-8
- ^ a b S. Andras, A. Baricz, and Y. Sun (2011) The Generalized Marcum Q-function: An Orthogonal Polynomial Approach. Acta Univ. Sapientiae Mathematica, 3(1), 60-76.
- ^ a b c d e f g Y.A. Brychkov (2012). On some properties of the Marcum Q function. Integral Transforms and Special Functions 23(3), 177-182.
- ^ M. Abramowitz and I.A. Stegun (1972). Formula 10.2.12, Modified Spherical Bessel Functions, Handbook of Mathematical functions, p. 443
- ^ W.K. Pratt (1968). Partial Differentials of Marcum's Q Function. Proceedings of the IEEE, 56(7), 1220-1221.
- ^ R. Esposito (1968). Comment on Partial Differentials of Marcum's Q Function. Proceedings of the IEEE, 56(12), 2195-2195.
- ^ V.M. Kapinas, S.K. Mihos, G.K. Karagiannidis (2009). On the Monotonicity of the Generalized Marcum and Nuttal Q-Functions. IEEE Transactions on Information Theory, 55(8), 3701-3710.
- ^ a b R. Li, P.Y. Kam, and H. Fu (2010). New Representations and Bounds for the Generalized Marcum Q-Function via a Geometric Approach, and an Application. IEEE Trans. Commun., 58(1), 157-169.
- ^ a b M.K. Simon and M.-S. Alouini (2000). Exponential-Type Bounds on the Generalized Marcum Q-Function with Application to Error Probability Analysis over Fading Channels. IEEE Trans. Commun. 48(3), 359-366.
- ^ H. Guo, B. Makki, M. -S. Alouini and T. Svensson, "A Semi-Linear Approximation of the First-Order Marcum Q-Function With Application to Predictor Antenna Systems," in IEEE Open Journal of the Communications Society, vol. 2, pp. 273-286, 2021, doi: 10.1109/OJCOMS.2021.3056393.
- ^ a b c d e f g D.A. Shnidman (1989). The Calculation of the Probability of Detection and the Generalized Marcum Q-Function. IEEE Transactions on Information Theory, 35(2), 389-400.
References
- Marcum, J. I. (1950) "Table of Q Functions". U.S. Air Force RAND Research Memorandum M-339. Santa Monica, CA: Rand Corporation, Jan. 1, 1950.
- Nuttall, Albert H. (1975): Some Integrals Involving the QM Function, IEEE Transactions on Information Theory, 21(1), 95–96, ISSN 0018-9448
- Shnidman, David A. (1989): The Calculation of the Probability of Detection and the Generalized Marcum Q-Function, IEEE Transactions on Information Theory, 35(2), 389-400.
- Weisstein, Eric W. Marcum Q-Function. From MathWorld—A Wolfram Web Resource. [1]