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]
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.}](./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),}](./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],}](./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],}](./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],}](./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].}](./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})],}](./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}),}](./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}),}](./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].}](./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}.}](./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),}](./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),}](./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).}](./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),}](./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).}](./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,}](./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,}](./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,}](./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.}](./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.}](./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}](./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.}](./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]