Marcum Q-function

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.

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

where

and the ratio is a constant.

For any real , such finite trigonometric integral is given by

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

Series representation

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 .
where the summations are in increments of one. Note that when assumes an integer value, we have .
where is the complementary error function. Since Bessel functions with half-integer parameter have finite sum expansions as
where is non-negative integer, we can exactly represent the generalized Marcum Q-function with half-integer parameter. More precisely, we have
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:
where , , and for any integer value of .

Recurrence relation and generating function

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.
where
We can eliminate the occurrence of the Bessel function to give the third order recurrence relation
where

Symmetry relation

In particular, for we have

Special values

Some specific values of Marcum-Q function are

which when combined with the recursive formula gives
for any non-negative integer .

Asymptotic forms

where is given by
The functions and are given by
The function satisfies the recursion
for and
Hence, assuming , the first term asymptotic approximation of the generalized Marcum-Q function is
where is the Gaussian Q-function. Here as
For the case when , we have
Here too as

Differentiation

We can relate the two partial derivatives as

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.

However, the relative error of this bound does not tend to zero when . 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
where and for . The tightness of this bound improves as either or increases. The relative error of this bound converges to 0 as . 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

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

When , the bound simplifies to give

Another such bound obtained via Cauchy-Schwarz inequality is given as

Chernoff-type bound

Chernoff-type bounds for the generalized Marcum Q-function, where is an integer, is given by

where the Chernoff parameter has optimum value of

Semi-linear approximation

The first-order Marcum-Q function can be semi-linearly approximated by

where

and

Equivalent forms for efficient computation

It is convenient to re-express the Marcum Q-function as

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:

form two:

form three:

form four:

and form five:

Among these five form, the second form is the most robust.

Applications

The generalized Marcum Q-function can be used to represent the cumulative distribution function (cdf) of many random variables:

Footnotes

  1. ↑ J.I. Marcum (1960). A statistical theory of target detection by pulsed radar: mathematical appendix, IRE Trans. Inform. Theory, vol. 6, 59-267.
  2. ↑ 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.
  3. 1 2 3 4 5 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.
  4. 1 2 3 4 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
  5. 1 2 3 4 5 6 7 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
  6. 1 2 Y. Sun and A. Baricz (2008). Inequalities for the generalized Marcum Q-function. Applied Mathematics and Computation 203(2008) 134-141.
  7. 1 2 3 4 5 6 N.M. Temme (1993). Asymptotic and numerical aspects of the noncentral chi-square distribution. Computers Math. Applic., 25(5), 55-63.
  8. 1 2 3 4 5 6 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
  9. 1 2 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.
  10. 1 2 3 4 5 6 7 Y.A. Brychkov (2012). On some properties of the Marcum Q function. Integral Transforms and Special Functions 23(3), 177-182.
  11. ↑ M. Abramowitz and I.A. Stegun (1972). Formula 10.2.12, Modified Spherical Bessel Functions, Handbook of Mathematical functions, p. 443
  12. ↑ W.K. Pratt (1968). Partial Differentials of Marcum's Q Function. Proceedings of the IEEE, 56(7), 1220-1221.
  13. ↑ R. Esposito (1968). Comment on Partial Differentials of Marcum's Q Function. Proceedings of the IEEE, 56(12), 2195-2195.
  14. ↑ 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.
  15. 1 2 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.
  16. 1 2 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.
  17. ↑ 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.
  18. 1 2 3 4 5 6 7 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