Marcum Q-function

In statistics, the generalized Marcum Q-function of order ν {\displaystyle \nu } is defined as

Q ν ( a , b ) = 1 a ν 1 b x ν exp ( x 2 + a 2 2 ) I ν 1 ( a x ) d x {\displaystyle Q_{\nu }(a,b)={\frac {1}{a^{\nu -1}}}\int _{b}^{\infty }x^{\nu }\exp \left(-{\frac {x^{2}+a^{2}}{2}}\right)I_{\nu -1}(ax)\,dx}

where b 0 {\displaystyle b\geq 0} and a , ν > 0 {\displaystyle a,\nu >0} and I ν 1 {\displaystyle I_{\nu -1}} is the modified Bessel function of first kind of order ν 1 {\displaystyle \nu -1} . If b > 0 {\displaystyle b>0} , the integral converges for any ν {\displaystyle \nu } . 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 ν = 1 {\displaystyle \nu =1} , and hence named after, by Jess Marcum for pulsed radars.[1]

Properties

Finite integral representation

Using the fact that Q ν ( a , 0 ) = 1 {\displaystyle Q_{\nu }(a,0)=1} , the generalized Marcum Q-function can alternatively be defined as a finite integral as

Q ν ( a , b ) = 1 1 a ν 1 0 b x ν exp ( x 2 + a 2 2 ) I ν 1 ( a x ) d x . {\displaystyle Q_{\nu }(a,b)=1-{\frac {1}{a^{\nu -1}}}\int _{0}^{b}x^{\nu }\exp \left(-{\frac {x^{2}+a^{2}}{2}}\right)I_{\nu -1}(ax)\,dx.}

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 ν = n {\displaystyle \nu =n} , such a representation is given by the trigonometric integral[2][3]

Q n ( a , b ) = { H n ( a , b ) a < b , 1 2 + H n ( a , a ) a = b , 1 + H n ( a , b ) a > b , {\displaystyle Q_{n}(a,b)=\left\{{\begin{array}{lr}H_{n}(a,b)&a<b,\\{\frac {1}{2}}+H_{n}(a,a)&a=b,\\1+H_{n}(a,b)&a>b,\end{array}}\right.}

where

H n ( a , b ) = ζ 1 n 2 π exp ( a 2 + b 2 2 ) 0 2 π cos ( n 1 ) θ ζ cos n θ 1 2 ζ cos θ + ζ 2 exp ( a b cos θ ) d θ , {\displaystyle H_{n}(a,b)={\frac {\zeta ^{1-n}}{2\pi }}\exp \left(-{\frac {a^{2}+b^{2}}{2}}\right)\int _{0}^{2\pi }{\frac {\cos(n-1)\theta -\zeta \cos n\theta }{1-2\zeta \cos \theta +\zeta ^{2}}}\exp(ab\cos \theta )\mathrm {d} \theta ,}

and the ratio ζ = a / b {\displaystyle \zeta =a/b} is a constant.

For any real ν > 0 {\displaystyle \nu >0} , such finite trigonometric integral is given by[4]

Q ν ( a , b ) = { H ν ( a , b ) + C ν ( a , b ) a < b , 1 2 + H ν ( a , a ) + C ν ( a , b ) a = b , 1 + H ν ( a , b ) + C ν ( a , b ) a > b , {\displaystyle Q_{\nu }(a,b)=\left\{{\begin{array}{lr}H_{\nu }(a,b)+C_{\nu }(a,b)&a<b,\\{\frac {1}{2}}+H_{\nu }(a,a)+C_{\nu }(a,b)&a=b,\\1+H_{\nu }(a,b)+C_{\nu }(a,b)&a>b,\end{array}}\right.}

where H n ( a , b ) {\displaystyle H_{n}(a,b)} is as defined before, ζ = a / b {\displaystyle \zeta =a/b} , and the additional correction term is given by

C ν ( a , b ) = sin ( ν π ) π exp ( a 2 + b 2 2 ) 0 1 ( x / ζ ) ν 1 ζ + x exp [ a b 2 ( x + 1 x ) ] d x . {\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.}

For integer values of ν {\displaystyle \nu } , the correction term C ν ( a , b ) {\displaystyle C_{\nu }(a,b)} tend to vanish.

Monotonicity and log-concavity

  • The generalized Marcum Q-function Q ν ( a , b ) {\displaystyle Q_{\nu }(a,b)} is strictly increasing in ν {\displaystyle \nu } and a {\displaystyle a} for all a 0 {\displaystyle a\geq 0} and b , ν > 0 {\displaystyle b,\nu >0} , and is strictly decreasing in b {\displaystyle b} for all a , b 0 {\displaystyle a,b\geq 0} and ν > 0. {\displaystyle \nu >0.} [5]
  • The function ν Q ν ( a , b ) {\displaystyle \nu \mapsto Q_{\nu }(a,b)} is log-concave on [ 1 , ) {\displaystyle [1,\infty )} for all a , b 0. {\displaystyle a,b\geq 0.} [5]
  • The function b Q ν ( a , b ) {\displaystyle b\mapsto Q_{\nu }(a,b)} is strictly log-concave on ( 0 , ) {\displaystyle (0,\infty )} for all a 0 {\displaystyle a\geq 0} and ν > 1 {\displaystyle \nu >1} , which implies that the generalized Marcum Q-function satisfies the new-is-better-than-used property.[6]
  • The function a 1 Q ν ( a , b ) {\displaystyle a\mapsto 1-Q_{\nu }(a,b)} is log-concave on [ 0 , ) {\displaystyle [0,\infty )} for all b , ν > 0. {\displaystyle b,\nu >0.} [5]

Series representation

  • The generalized Marcum Q function of order ν > 0 {\displaystyle \nu >0} can be represented using incomplete Gamma function as[7][8][9]
Q ν ( a , b ) = 1 e a 2 / 2 k = 0 1 k ! γ ( ν + k , b 2 2 ) Γ ( ν + k ) ( a 2 2 ) k , {\displaystyle Q_{\nu }(a,b)=1-e^{-a^{2}/2}\sum _{k=0}^{\infty }{\frac {1}{k!}}{\frac {\gamma (\nu +k,{\frac {b^{2}}{2}})}{\Gamma (\nu +k)}}\left({\frac {a^{2}}{2}}\right)^{k},}
where γ ( s , x ) {\displaystyle \gamma (s,x)} is the lower incomplete Gamma function. This is usually called the canonical representation of the ν {\displaystyle \nu } -th order generalized Marcum Q-function.
  • The generalized Marcum Q function of order ν > 0 {\displaystyle \nu >0} can also be represented using generalized Laguerre polynomials as[9]
Q ν ( a , b ) = 1 e a 2 / 2 k = 0 ( 1 ) k L k ( ν 1 ) ( a 2 2 ) Γ ( ν + k + 1 ) ( b 2 2 ) k + ν , {\displaystyle Q_{\nu }(a,b)=1-e^{-a^{2}/2}\sum _{k=0}^{\infty }(-1)^{k}{\frac {L_{k}^{(\nu -1)}({\frac {a^{2}}{2}})}{\Gamma (\nu +k+1)}}\left({\frac {b^{2}}{2}}\right)^{k+\nu },}
where L k ( α ) ( ) {\displaystyle L_{k}^{(\alpha )}(\cdot )} is the generalized Laguerre polynomial of degree k {\displaystyle k} and of order α {\displaystyle \alpha } .
  • The generalized Marcum Q-function of order ν > 0 {\displaystyle \nu >0} can also be represented as Neumann series expansions[4][8]
Q ν ( a , b ) = e ( a 2 + b 2 ) / 2 α = 1 ν ( a b ) α I α ( a b ) , {\displaystyle Q_{\nu }(a,b)=e^{-(a^{2}+b^{2})/2}\sum _{\alpha =1-\nu }^{\infty }\left({\frac {a}{b}}\right)^{\alpha }I_{-\alpha }(ab),}
1 Q ν ( a , b ) = e ( a 2 + b 2 ) / 2 α = ν ( b a ) α I α ( a b ) , {\displaystyle 1-Q_{\nu }(a,b)=e^{-(a^{2}+b^{2})/2}\sum _{\alpha =\nu }^{\infty }\left({\frac {b}{a}}\right)^{\alpha }I_{\alpha }(ab),}
where the summations are in increments of one. Note that when α {\displaystyle \alpha } assumes an integer value, we have I α ( a b ) = I α ( a b ) {\displaystyle I_{\alpha }(ab)=I_{-\alpha }(ab)} .
  • For non-negative half-integer values ν = n + 1 / 2 {\displaystyle \nu =n+1/2} , we have a closed form expression for the generalized Marcum Q-function as[8][10]
Q n + 1 / 2 ( a , b ) = 1 2 [ e r f c ( b a 2 ) + e r f c ( b + a 2 ) ] + e ( a 2 + b 2 ) / 2 k = 1 n ( b a ) k 1 / 2 I k 1 / 2 ( a b ) , {\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),}
where e r f c ( ) {\displaystyle \mathrm {erfc} (\cdot )} is the complementary error function. Since Bessel functions with half-integer parameter have finite sum expansions as[4]
I ± ( n + 0.5 ) ( z ) = 1 π k = 0 n ( n + k ) ! k ! ( n k ) ! [ ( 1 ) k e z ( 1 ) n e z ( 2 z ) k + 0.5 ] , {\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],}
where n {\displaystyle n} is non-negative integer, we can exactly represent the generalized Marcum Q-function with half-integer parameter. More precisely, we have[4]
Q n + 1 / 2 ( a , b ) = Q ( b a ) + Q ( b + a ) + 1 b 2 π i = 1 n ( b a ) i k = 0 i 1 ( i + k 1 ) ! k ! ( i k 1 ) ! [ ( 1 ) k e ( a b ) 2 / 2 + ( 1 ) i e ( a + b ) 2 / 2 ( 2 a b ) k ] , {\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],}
for non-negative integers n {\displaystyle n} , where Q ( ) {\displaystyle Q(\cdot )} 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]
I n + 1 2 ( z ) = 2 z π [ g n ( z ) sinh ( z ) + g n 1 ( z ) cosh ( z ) ] , {\displaystyle I_{n+{\frac {1}{2}}}(z)={\sqrt {\frac {2z}{\pi }}}\left[g_{n}(z)\sinh(z)+g_{-n-1}(z)\cosh(z)\right],}
where g 0 ( z ) = z 1 {\displaystyle g_{0}(z)=z^{-1}} , g 1 ( z ) = z 2 {\displaystyle g_{1}(z)=-z^{-2}} , and g n 1 ( z ) g n + 1 ( z ) = ( 2 n + 1 ) z 1 g n ( z ) {\displaystyle g_{n-1}(z)-g_{n+1}(z)=(2n+1)z^{-1}g_{n}(z)} for any integer value of n {\displaystyle n} .

Recurrence relation and generating function

  • Integrating by parts, we can show that generalized Marcum Q-function satisfies the following recurrence relation[8][10]
Q ν + 1 ( a , b ) Q ν ( a , b ) = ( b a ) ν e ( a 2 + b 2 ) / 2 I ν ( a b ) . {\displaystyle Q_{\nu +1}(a,b)-Q_{\nu }(a,b)=\left({\frac {b}{a}}\right)^{\nu }e^{-(a^{2}+b^{2})/2}I_{\nu }(ab).}
  • The above formula is easily generalized as[10]
Q ν n ( a , b ) = Q ν ( a , b ) ( b a ) ν e ( a 2 + b 2 ) / 2 k = 1 n ( a b ) k I ν k ( a b ) , {\displaystyle Q_{\nu -n}(a,b)=Q_{\nu }(a,b)-\left({\frac {b}{a}}\right)^{\nu }e^{-(a^{2}+b^{2})/2}\sum _{k=1}^{n}\left({\frac {a}{b}}\right)^{k}I_{\nu -k}(ab),}
Q ν + n ( a , b ) = Q ν ( a , b ) + ( b a ) ν e ( a 2 + b 2 ) / 2 k = 0 n 1 ( b a ) k I ν + k ( a b ) , {\displaystyle Q_{\nu +n}(a,b)=Q_{\nu }(a,b)+\left({\frac {b}{a}}\right)^{\nu }e^{-(a^{2}+b^{2})/2}\sum _{k=0}^{n-1}\left({\frac {b}{a}}\right)^{k}I_{\nu +k}(ab),}
for positive integer n {\displaystyle n} . The former recurrence can be used to formally define the generalized Marcum Q-function for negative ν {\displaystyle \nu } . Taking Q ( a , b ) = 1 {\displaystyle Q_{\infty }(a,b)=1} and Q ( a , b ) = 0 {\displaystyle Q_{-\infty }(a,b)=0} for n = {\displaystyle n=\infty } , we obtain the Neumann series representation of the generalized Marcum Q-function.
  • The related three-term recurrence relation is given by[7]
Q ν + 1 ( a , b ) ( 1 + c ν ( a , b ) ) Q ν ( a , b ) + c ν ( a , b ) Q ν 1 ( a , b ) = 0 , {\displaystyle Q_{\nu +1}(a,b)-(1+c_{\nu }(a,b))Q_{\nu }(a,b)+c_{\nu }(a,b)Q_{\nu -1}(a,b)=0,}
where
c ν ( a , b ) = ( b a ) I ν ( a b ) I ν + 1 ( a b ) . {\displaystyle c_{\nu }(a,b)=\left({\frac {b}{a}}\right){\frac {I_{\nu }(ab)}{I_{\nu +1}(ab)}}.}
We can eliminate the occurrence of the Bessel function to give the third order recurrence relation[7]
a 2 2 Q ν + 2 ( a , b ) = ( a 2 2 ν ) Q ν + 1 ( a , b ) + ( b 2 2 + ν ) Q ν ( a , b ) b 2 2 Q ν 1 ( a , b ) . {\displaystyle {\frac {a^{2}}{2}}Q_{\nu +2}(a,b)=\left({\frac {a^{2}}{2}}-\nu \right)Q_{\nu +1}(a,b)+\left({\frac {b^{2}}{2}}+\nu \right)Q_{\nu }(a,b)-{\frac {b^{2}}{2}}Q_{\nu -1}(a,b).}
  • Another recurrence relationship, relating it with its derivatives, is given by
Q ν + 1 ( a , b ) = Q ν ( a , b ) + 1 a a Q ν ( a , b ) , {\displaystyle Q_{\nu +1}(a,b)=Q_{\nu }(a,b)+{\frac {1}{a}}{\frac {\partial }{\partial a}}Q_{\nu }(a,b),}
Q ν 1 ( a , b ) = Q ν ( a , b ) + 1 b b Q ν ( a , b ) . {\displaystyle Q_{\nu -1}(a,b)=Q_{\nu }(a,b)+{\frac {1}{b}}{\frac {\partial }{\partial b}}Q_{\nu }(a,b).}
  • The ordinary generating function of Q ν ( a , b ) {\displaystyle Q_{\nu }(a,b)} for integral ν {\displaystyle \nu } is[10]
n = t n Q n ( a , b ) = e ( a 2 + b 2 ) / 2 t 1 t e ( b 2 t + a 2 / t ) / 2 , {\displaystyle \sum _{n=-\infty }^{\infty }t^{n}Q_{n}(a,b)=e^{-(a^{2}+b^{2})/2}{\frac {t}{1-t}}e^{(b^{2}t+a^{2}/t)/2},}
where | t | < 1. {\displaystyle |t|<1.}

Symmetry relation

  • Using the two Neumann series representations, we can obtain the following symmetry relation for positive integral ν = n {\displaystyle \nu =n}
Q n ( a , b ) + Q n ( b , a ) = 1 + e ( a 2 + b 2 ) / 2 [ I 0 ( a b ) + k = 1 n 1 a 2 k + b 2 k ( a b ) k I k ( a b ) ] . {\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].}
In particular, for n = 1 {\displaystyle n=1} we have
Q 1 ( a , b ) + Q 1 ( b , a ) = 1 + e ( a 2 + b 2 ) / 2 I 0 ( a b ) . {\displaystyle Q_{1}(a,b)+Q_{1}(b,a)=1+e^{-(a^{2}+b^{2})/2}I_{0}(ab).}

Special values

Some specific values of Marcum-Q function are[6]

  • Q ν ( 0 , 0 ) = 1 , {\displaystyle Q_{\nu }(0,0)=1,}
  • Q ν ( a , 0 ) = 1 , {\displaystyle Q_{\nu }(a,0)=1,}
  • Q ν ( a , + ) = 0 , {\displaystyle Q_{\nu }(a,+\infty )=0,}
  • Q ν ( 0 , b ) = Γ ( ν , b 2 / 2 ) Γ ( ν ) , {\displaystyle Q_{\nu }(0,b)={\frac {\Gamma (\nu ,b^{2}/2)}{\Gamma (\nu )}},}
  • Q ν ( + , b ) = 1 , {\displaystyle Q_{\nu }(+\infty ,b)=1,}
  • Q ( a , b ) = 1 , {\displaystyle Q_{\infty }(a,b)=1,}
  • For a = b {\displaystyle a=b} , by subtracting the two forms of Neumann series representations, we have[10]
Q 1 ( a , a ) = 1 2 [ 1 + e a 2 I 0 ( a 2 ) ] , {\displaystyle Q_{1}(a,a)={\frac {1}{2}}[1+e^{-a^{2}}I_{0}(a^{2})],}
which when combined with the recursive formula gives
Q n ( a , a ) = 1 2 [ 1 + e a 2 I 0 ( a 2 ) ] + e a 2 k = 1 n 1 I k ( a 2 ) , {\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}),}
Q n ( a , a ) = 1 2 [ 1 + e a 2 I 0 ( a 2 ) ] e a 2 k = 1 n I k ( a 2 ) , {\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}),}
for any non-negative integer n {\displaystyle n} .
  • For ν = 1 / 2 {\displaystyle \nu =1/2} , using the basic integral definition of generalized Marcum Q-function, we have[8][10]
Q 1 / 2 ( a , b ) = 1 2 [ e r f c ( b a 2 ) + e r f c ( b + a 2 ) ] . {\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].}
  • For ν = 3 / 2 {\displaystyle \nu =3/2} , we have
Q 3 / 2 ( a , b ) = Q 1 / 2 ( a , b ) + 2 π sinh ( a b ) a e ( a 2 + b 2 ) / 2 . {\displaystyle Q_{3/2}(a,b)=Q_{1/2}(a,b)+{\sqrt {\frac {2}{\pi }}}\,{\frac {\sinh(ab)}{a}}e^{-(a^{2}+b^{2})/2}.}
  • For ν = 5 / 2 {\displaystyle \nu =5/2} we have
Q 5 / 2 ( a , b ) = Q 3 / 2 ( a , b ) + 2 π a b cosh ( a b ) sinh ( a b ) a 3 e ( a 2 + b 2 ) / 2 . {\displaystyle Q_{5/2}(a,b)=Q_{3/2}(a,b)+{\sqrt {\frac {2}{\pi }}}\,{\frac {ab\cosh(ab)-\sinh(ab)}{a^{3}}}e^{-(a^{2}+b^{2})/2}.}

Asymptotic forms

  • Assuming ν {\displaystyle \nu } to be fixed and a b {\displaystyle ab} large, let ζ = a / b > 0 {\displaystyle \zeta =a/b>0} , then the generalized Marcum-Q function has the following asymptotic form[7]
Q ν ( a , b ) n = 0 ψ n , {\displaystyle Q_{\nu }(a,b)\sim \sum _{n=0}^{\infty }\psi _{n},}
where ψ n {\displaystyle \psi _{n}} is given by
ψ n = 1 2 ζ ν 2 π ( 1 ) n [ A n ( ν 1 ) ζ A n ( ν ) ] ϕ n . {\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}.}
The functions ϕ n {\displaystyle \phi _{n}} and A n {\displaystyle A_{n}} are given by
ϕ n = [ ( b a ) 2 2 a b ] n 1 2 Γ ( 1 2 n , ( b a ) 2 2 ) , {\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),}
A n ( ν ) = 2 n Γ ( 1 2 + ν + n ) n ! Γ ( 1 2 + ν n ) . {\displaystyle A_{n}(\nu )={\frac {2^{-n}\Gamma ({\frac {1}{2}}+\nu +n)}{n!\Gamma ({\frac {1}{2}}+\nu -n)}}.}
The function A n ( ν ) {\displaystyle A_{n}(\nu )} satisfies the recursion
A n + 1 ( ν ) = ( 2 n + 1 ) 2 4 ν 2 8 ( n + 1 ) A n ( ν ) , {\displaystyle A_{n+1}(\nu )=-{\frac {(2n+1)^{2}-4\nu ^{2}}{8(n+1)}}A_{n}(\nu ),}
for n 0 {\displaystyle n\geq 0} and A 0 ( ν ) = 1. {\displaystyle A_{0}(\nu )=1.}
  • In the first term of the above asymptotic approximation, we have
ϕ 0 = 2 π a b b a e r f c ( b a 2 ) . {\displaystyle \phi _{0}={\frac {\sqrt {2\pi ab}}{b-a}}\mathrm {erfc} \left({\frac {b-a}{\sqrt {2}}}\right).}
Hence, assuming b > a {\displaystyle b>a} , the first term asymptotic approximation of the generalized Marcum-Q function is[7]
Q ν ( a , b ) ψ 0 = ( b a ) ν 1 2 Q ( b a ) , {\displaystyle Q_{\nu }(a,b)\sim \psi _{0}=\left({\frac {b}{a}}\right)^{\nu -{\frac {1}{2}}}Q(b-a),}
where Q ( ) {\displaystyle Q(\cdot )} is the Gaussian Q-function. Here Q ν ( a , b ) 0.5 {\displaystyle Q_{\nu }(a,b)\sim 0.5} as a b . {\displaystyle a\uparrow b.}
For the case when a > b {\displaystyle a>b} , we have[7]
Q ν ( a , b ) 1 ψ 0 = 1 ( b a ) ν 1 2 Q ( a b ) . {\displaystyle Q_{\nu }(a,b)\sim 1-\psi _{0}=1-\left({\frac {b}{a}}\right)^{\nu -{\frac {1}{2}}}Q(a-b).}
Here too Q ν ( a , b ) 0.5 {\displaystyle Q_{\nu }(a,b)\sim 0.5} as a b . {\displaystyle a\downarrow b.}

Differentiation

  • The partial derivative of Q ν ( a , b ) {\displaystyle Q_{\nu }(a,b)} with respect to a {\displaystyle a} and b {\displaystyle b} is given by[12][13]
a Q ν ( a , b ) = a [ Q ν + 1 ( a , b ) Q ν ( a , b ) ] = a ( b a ) ν e ( a 2 + b 2 ) / 2 I ν ( a b ) , {\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),}
b Q ν ( a , b ) = b [ Q ν 1 ( a , b ) Q ν ( a , b ) ] = b ( b a ) ν 1 e ( a 2 + b 2 ) / 2 I ν 1 ( a b ) . {\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).}
We can relate the two partial derivatives as
1 a a Q ν ( a , b ) + 1 b b Q ν + 1 ( a , b ) = 0. {\displaystyle {\frac {1}{a}}{\frac {\partial }{\partial a}}Q_{\nu }(a,b)+{\frac {1}{b}}{\frac {\partial }{\partial b}}Q_{\nu +1}(a,b)=0.}
  • The n-th partial derivative of Q ν ( a , b ) {\displaystyle Q_{\nu }(a,b)} with respect to its arguments is given by[10]
n a n Q ν ( a , b ) = n ! ( a ) n k = 0 [ n / 2 ] ( 2 a 2 ) k k ! ( n 2 k ) ! p = 0 n k ( 1 ) p ( n k p ) Q ν + p ( a , b ) , {\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),}
n b n Q ν ( a , b ) = n ! a 1 ν 2 n b n ν + 1 e ( a 2 + b 2 ) / 2 k = [ n / 2 ] n ( 2 b 2 ) k ( n k ) ! ( 2 k n ) ! p = 0 k 1 ( k 1 p ) ( a b ) p I ν p 1 ( a b ) . {\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).}

Inequalities

Q ν 2 ( a , b ) > Q ν 1 ( a , b ) + Q ν + 1 ( a , b ) 2 > Q ν 1 ( a , b ) Q ν + 1 ( a , b ) {\displaystyle Q_{\nu }^{2}(a,b)>{\frac {Q_{\nu -1}(a,b)+Q_{\nu +1}(a,b)}{2}}>Q_{\nu -1}(a,b)Q_{\nu +1}(a,b)}
for all a b > 0 {\displaystyle a\geq b>0} and ν > 1 {\displaystyle \nu >1} .

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 ν Q ν ( a , b ) {\displaystyle \nu \mapsto Q_{\nu }(a,b)} and the fact that we have closed form expression for Q ν ( a , b ) {\displaystyle Q_{\nu }(a,b)} when ν {\displaystyle \nu } is half-integer valued.

Let x 0.5 {\displaystyle \lfloor x\rfloor _{0.5}} and x 0.5 {\displaystyle \lceil x\rceil _{0.5}} denote the pair of half-integer rounding operators that map a real x {\displaystyle x} to its nearest left and right half-odd integer, respectively, according to the relations

x 0.5 = x 0.5 + 0.5 {\displaystyle \lfloor x\rfloor _{0.5}=\lfloor x-0.5\rfloor +0.5}
x 0.5 = x + 0.5 0.5 {\displaystyle \lceil x\rceil _{0.5}=\lceil x+0.5\rceil -0.5}

where x {\displaystyle \lfloor x\rfloor } and x {\displaystyle \lceil x\rceil } denote the integer floor and ceiling functions.

  • The monotonicity of the function ν Q ν ( a , b ) {\displaystyle \nu \mapsto Q_{\nu }(a,b)} for all a 0 {\displaystyle a\geq 0} and b > 0 {\displaystyle b>0} gives us the following simple bound[14][8][15]
Q ν 0.5 ( a , b ) < Q ν ( a , b ) < Q ν 0.5 ( a , b ) . {\displaystyle Q_{\lfloor \nu \rfloor _{0.5}}(a,b)<Q_{\nu }(a,b)<Q_{\lceil \nu \rceil _{0.5}}(a,b).}
However, the relative error of this bound does not tend to zero when b {\displaystyle b\to \infty } .[5] For integral values of ν = n {\displaystyle \nu =n} , this bound reduces to
Q n 0.5 ( a , b ) < Q n ( a , b ) < Q n + 0.5 ( a , b ) . {\displaystyle Q_{n-0.5}(a,b)<Q_{n}(a,b)<Q_{n+0.5}(a,b).}
A very good approximation of the generalized Marcum Q-function for integer valued ν = n {\displaystyle \nu =n} is obtained by taking the arithmetic mean of the upper and lower bound[15]
Q n ( a , b ) Q n 0.5 ( a , b ) + Q n + 0.5 ( a , b ) 2 . {\displaystyle Q_{n}(a,b)\approx {\frac {Q_{n-0.5}(a,b)+Q_{n+0.5}(a,b)}{2}}.}
  • A tighter bound can be obtained by exploiting the log-concavity of ν Q ν ( a , b ) {\displaystyle \nu \mapsto Q_{\nu }(a,b)} on [ 1 , ) {\displaystyle [1,\infty )} as[5]
Q ν 1 ( a , b ) ν 2 v Q ν 2 ( a , b ) v ν 1 < Q ν ( a , b ) < Q ν 2 ( a , b ) ν 2 ν + 1 Q ν 2 + 1 ( a , b ) ν 2 ν , {\displaystyle Q_{\nu _{1}}(a,b)^{\nu _{2}-v}Q_{\nu _{2}}(a,b)^{v-\nu _{1}}<Q_{\nu }(a,b)<{\frac {Q_{\nu _{2}}(a,b)^{\nu _{2}-\nu +1}}{Q_{\nu _{2}+1}(a,b)^{\nu _{2}-\nu }}},}
where ν 1 = ν 0.5 {\displaystyle \nu _{1}=\lfloor \nu \rfloor _{0.5}} and ν 2 = ν 0.5 {\displaystyle \nu _{2}=\lceil \nu \rceil _{0.5}} for ν 1.5 {\displaystyle \nu \geq 1.5} . The tightness of this bound improves as either a {\displaystyle a} or ν {\displaystyle \nu } increases. The relative error of this bound converges to 0 as b {\displaystyle b\to \infty } .[5] For integral values of ν = n {\displaystyle \nu =n} , this bound reduces to
Q n 0.5 ( a , b ) Q n + 0.5 ( a , b ) < Q n ( a , b ) < Q n + 0.5 ( a , b ) Q n + 0.5 ( a , b ) Q n + 1.5 ( a , b ) . {\displaystyle {\sqrt {Q_{n-0.5}(a,b)Q_{n+0.5}(a,b)}}<Q_{n}(a,b)<Q_{n+0.5}(a,b){\sqrt {\frac {Q_{n+0.5}(a,b)}{Q_{n+1.5}(a,b)}}}.}

Cauchy-Schwarz bound

Using the trigonometric integral representation for integer valued ν = n {\displaystyle \nu =n} , the following Cauchy-Schwarz bound can be obtained[3]

e b 2 / 2 Q n ( a , b ) exp [ 1 2 ( b 2 + a 2 ) ] I 0 ( 2 a b ) 2 n 1 2 + ζ 2 ( 1 n ) 2 ( 1 ζ 2 ) , ζ < 1 , {\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,}
1 Q n ( a , b ) exp [ 1 2 ( b 2 + a 2 ) ] I 0 ( 2 a b ) ζ 2 ( 1 n ) 2 ( ζ 2 1 ) , ζ > 1 , {\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,}

where ζ = a / b > 0 {\displaystyle \zeta =a/b>0} .

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 ζ = a / b > 0 {\displaystyle \zeta =a/b>0} , one such bound for integer valued ν = n {\displaystyle \nu =n} is given as[16][3]

e ( b + a ) 2 / 2 Q n ( a , b ) e ( b a ) 2 / 2 + ζ 1 n 1 π ( 1 ζ ) [ e ( b a ) 2 / 2 e ( b + a ) 2 / 2 ] , ζ < 1 , {\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,}
Q n ( a , b ) 1 1 2 [ e ( a b ) 2 / 2 e ( a + b ) 2 / 2 ] , ζ > 1. {\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.}

When n = 1 {\displaystyle n=1} , the bound simplifies to give

e ( b + a ) 2 / 2 Q 1 ( a , b ) e ( b a ) 2 / 2 , ζ < 1 , {\displaystyle e^{-(b+a)^{2}/2}\leq Q_{1}(a,b)\leq e^{-(b-a)^{2}/2},\qquad \zeta <1,}
1 1 2 [ e ( a b ) 2 / 2 e ( a + b ) 2 / 2 ] Q 1 ( a , b ) , ζ > 1. {\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.}

Another such bound obtained via Cauchy-Schwarz inequality is given as[3]

e b 2 / 2 Q n ( a , b ) 1 2 2 n 1 2 + ζ 2 ( 1 n ) 2 ( 1 ζ 2 ) [ e ( b a ) 2 / 2 + e ( b + a ) 2 / 2 ] , ζ < 1 {\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}
Q n ( a , b ) 1 1 2 ζ 2 ( 1 n ) 2 ( ζ 2 1 ) [ e ( b a ) 2 / 2 + e ( b + a ) 2 / 2 ] , ζ > 1. {\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.}

Chernoff-type bound

Chernoff-type bounds for the generalized Marcum Q-function, where ν = n {\displaystyle \nu =n} is an integer, is given by[16][3]

( 1 2 λ ) n exp ( λ b 2 + λ n a 2 1 2 λ ) { Q n ( a , b ) , b 2 > n ( a 2 + 2 ) 1 Q n ( a , b ) , b 2 < n ( a 2 + 2 ) {\displaystyle (1-2\lambda )^{-n}\exp \left(-\lambda b^{2}+{\frac {\lambda na^{2}}{1-2\lambda }}\right)\geq \left\{{\begin{array}{lr}Q_{n}(a,b),&b^{2}>n(a^{2}+2)\\1-Q_{n}(a,b),&b^{2}<n(a^{2}+2)\end{array}}\right.}

where the Chernoff parameter ( 0 < λ < 1 / 2 ) {\displaystyle (0<\lambda <1/2)} has optimum value λ 0 {\displaystyle \lambda _{0}} of

λ 0 = 1 2 ( 1 n b 2 n b 2 1 + ( a b ) 2 n ) . {\displaystyle \lambda _{0}={\frac {1}{2}}\left(1-{\frac {n}{b^{2}}}-{\frac {n}{b^{2}}}{\sqrt {1+{\frac {(ab)^{2}}{n}}}}\right).}

Semi-linear approximation

The first-order Marcum-Q function can be semi-linearly approximated by [17]

Q 1 ( a , b ) = { 1 ,                                                                                                                                                                       i f   b < c 1 β 0 e 1 2 ( a 2 + ( β 0 ) 2 ) I 0 ( a β 0 ) ( b β 0 ) + Q 1 ( a , β 0 ) ,           i f   c 1 b c 2 0 ,                                                                                                                                                                       i f   b > c 2 {\displaystyle {\begin{aligned}Q_{1}(a,b)={\begin{cases}1,~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\mathrm {if} ~b<c_{1}\\-\beta _{0}e^{-{\frac {1}{2}}\left(a^{2}+\left(\beta _{0}\right)^{2}\right)}I_{0}\left(a\beta _{0}\right)\left(b-\beta _{0}\right)+Q_{1}\left(a,\beta _{0}\right),~~~~~\mathrm {if} ~c_{1}\leq b\leq c_{2}\\0,~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\mathrm {if} ~b>c_{2}\end{cases}}\end{aligned}}}

where

β 0 = a + a 2 + 2 2 , {\displaystyle {\begin{aligned}\beta _{0}={\frac {a+{\sqrt {a^{2}+2}}}{2}},\end{aligned}}}
c 1 ( a ) = max ( 0 , β 0 + Q 1 ( a , β 0 ) 1 β 0 e 1 2 ( a 2 + ( β 0 ) 2 ) I 0 ( a β 0 ) ) , {\displaystyle {\begin{aligned}c_{1}(a)=\max {\Bigg (}0,\beta _{0}+{\frac {Q_{1}\left(a,\beta _{0}\right)-1}{\beta _{0}e^{-{\frac {1}{2}}\left(a^{2}+\left(\beta _{0}\right)^{2}\right)}I_{0}\left(a\beta _{0}\right)}}{\Bigg )},\end{aligned}}}

and

c 2 ( a ) = β 0 + Q 1 ( a , β 0 ) β 0 e 1 2 ( a 2 + ( β 0 ) 2 ) I 0 ( a β 0 ) . {\displaystyle {\begin{aligned}c_{2}(a)=\beta _{0}+{\frac {Q_{1}\left(a,\beta _{0}\right)}{\beta _{0}e^{-{\frac {1}{2}}\left(a^{2}+\left(\beta _{0}\right)^{2}\right)}I_{0}\left(a\beta _{0}\right)}}.\end{aligned}}}

Equivalent forms for efficient computation

It is convenient to re-express the Marcum Q-function as[18]

P N ( X , Y ) = Q N ( 2 N X , 2 Y ) . {\displaystyle P_{N}(X,Y)=Q_{N}({\sqrt {2NX}},{\sqrt {2Y}}).}

The P N ( X , Y ) {\displaystyle P_{N}(X,Y)} can be interpreted as the detection probability of N {\displaystyle N} incoherently integrated received signal samples of constant received signal-to-noise ratio, X {\displaystyle X} , with a normalized detection threshold Y {\displaystyle Y} . In this equivalent form of Marcum Q-function, for given a {\displaystyle a} and b {\displaystyle b} , we have X = a 2 / 2 N {\displaystyle X=a^{2}/2N} and Y = b 2 / 2 {\displaystyle Y=b^{2}/2} . Many expressions exist that can represent P N ( X , Y ) {\displaystyle P_{N}(X,Y)} . However, the five most reliable, accurate, and efficient ones for numerical computation are given below. They are form one:[18]

P N ( X , Y ) = k = 0 e N X ( N X ) k k ! m = 0 N 1 + k e Y Y m m ! , {\displaystyle P_{N}(X,Y)=\sum _{k=0}^{\infty }e^{-NX}{\frac {(NX)^{k}}{k!}}\sum _{m=0}^{N-1+k}e^{-Y}{\frac {Y^{m}}{m!}},}

form two:[18]

P N ( X , Y ) = m = 0 N 1 e Y Y m m ! + m = N e Y Y m m ! ( 1 k = 0 m N e N X ( N X ) k k ! ) , {\displaystyle P_{N}(X,Y)=\sum _{m=0}^{N-1}e^{-Y}{\frac {Y^{m}}{m!}}+\sum _{m=N}^{\infty }e^{-Y}{\frac {Y^{m}}{m!}}\left(1-\sum _{k=0}^{m-N}e^{-NX}{\frac {(NX)^{k}}{k!}}\right),}

form three:[18]

1 P N ( X , Y ) = m = N e Y Y m m ! k = 0 m N e N X ( N X ) k k ! , {\displaystyle 1-P_{N}(X,Y)=\sum _{m=N}^{\infty }e^{-Y}{\frac {Y^{m}}{m!}}\sum _{k=0}^{m-N}e^{-NX}{\frac {(NX)^{k}}{k!}},}

form four:[18]

1 P N ( X , Y ) = k = 0 e N X ( N X ) k k ! ( 1 m = 0 N 1 + k e Y Y m m ! ) , {\displaystyle 1-P_{N}(X,Y)=\sum _{k=0}^{\infty }e^{-NX}{\frac {(NX)^{k}}{k!}}\left(1-\sum _{m=0}^{N-1+k}e^{-Y}{\frac {Y^{m}}{m!}}\right),}

and form five:[18]

1 P N ( X , Y ) = e ( N X + Y ) r = N ( Y N X ) r / 2 I r ( 2 N X Y ) . {\displaystyle 1-P_{N}(X,Y)=e^{-(NX+Y)}\sum _{r=N}^{\infty }\left({\frac {Y}{NX}}\right)^{r/2}I_{r}(2{\sqrt {NXY}}).}

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 X E x p ( λ ) {\displaystyle X\sim \mathrm {Exp} (\lambda )} is a exponential distribution with rate parameter λ {\displaystyle \lambda } , then its cdf is given by F X ( x ) = 1 Q 1 ( 0 , 2 λ x ) {\displaystyle F_{X}(x)=1-Q_{1}\left(0,{\sqrt {2\lambda x}}\right)}
  • If X E r l a n g ( k , λ ) {\displaystyle X\sim \mathrm {Erlang} (k,\lambda )} is a Erlang distribution with shape parameter k {\displaystyle k} and rate parameter λ {\displaystyle \lambda } , then its cdf is given by F X ( x ) = 1 Q k ( 0 , 2 λ x ) {\displaystyle F_{X}(x)=1-Q_{k}\left(0,{\sqrt {2\lambda x}}\right)}
  • If X χ k 2 {\displaystyle X\sim \chi _{k}^{2}} is a chi-squared distribution with k {\displaystyle k} degrees of freedom, then its cdf is given by F X ( x ) = 1 Q k / 2 ( 0 , x ) {\displaystyle F_{X}(x)=1-Q_{k/2}(0,{\sqrt {x}})}
  • If X G a m m a ( α , β ) {\displaystyle X\sim \mathrm {Gamma} (\alpha ,\beta )} is a gamma distribution with shape parameter α {\displaystyle \alpha } and rate parameter β {\displaystyle \beta } , then its cdf is given by F X ( x ) = 1 Q α ( 0 , 2 β x ) {\displaystyle F_{X}(x)=1-Q_{\alpha }(0,{\sqrt {2\beta x}})}
  • If X W e i b u l l ( k , λ ) {\displaystyle X\sim \mathrm {Weibull} (k,\lambda )} is a Weibull distribution with shape parameters k {\displaystyle k} and scale parameter λ {\displaystyle \lambda } , then its cdf is given by F X ( x ) = 1 Q 1 ( 0 , 2 ( x λ ) k 2 ) {\displaystyle F_{X}(x)=1-Q_{1}\left(0,{\sqrt {2}}\left({\frac {x}{\lambda }}\right)^{\frac {k}{2}}\right)}
  • If X G G ( a , d , p ) {\displaystyle X\sim \mathrm {GG} (a,d,p)} is a generalized gamma distribution with parameters a , d , p {\displaystyle a,d,p} , then its cdf is given by F X ( x ) = 1 Q d p ( 0 , 2 ( x a ) p 2 ) {\displaystyle F_{X}(x)=1-Q_{\frac {d}{p}}\left(0,{\sqrt {2}}\left({\frac {x}{a}}\right)^{\frac {p}{2}}\right)}
  • If X χ k 2 ( λ ) {\displaystyle X\sim \chi _{k}^{2}(\lambda )} is a non-central chi-squared distribution with non-centrality parameter λ {\displaystyle \lambda } and k {\displaystyle k} degrees of freedom, then its cdf is given by F X ( x ) = 1 Q k / 2 ( λ , x ) {\displaystyle F_{X}(x)=1-Q_{k/2}({\sqrt {\lambda }},{\sqrt {x}})}
  • If X R a y l e i g h ( σ ) {\displaystyle X\sim \mathrm {Rayleigh} (\sigma )} is a Rayleigh distribution with parameter σ {\displaystyle \sigma } , then its cdf is given by F X ( x ) = 1 Q 1 ( 0 , x σ ) {\displaystyle F_{X}(x)=1-Q_{1}\left(0,{\frac {x}{\sigma }}\right)}
  • If X M a x w e l l ( σ ) {\displaystyle X\sim \mathrm {Maxwell} (\sigma )} is a Maxwell–Boltzmann distribution with parameter σ {\displaystyle \sigma } , then its cdf is given by F X ( x ) = 1 Q 3 / 2 ( 0 , x σ ) {\displaystyle F_{X}(x)=1-Q_{3/2}\left(0,{\frac {x}{\sigma }}\right)}
  • If X χ k {\displaystyle X\sim \chi _{k}} is a chi distribution with k {\displaystyle k} degrees of freedom, then its cdf is given by F X ( x ) = 1 Q k / 2 ( 0 , x ) {\displaystyle F_{X}(x)=1-Q_{k/2}(0,x)}
  • If X N a k a g a m i ( m , Ω ) {\displaystyle X\sim \mathrm {Nakagami} (m,\Omega )} is a Nakagami distribution with m {\displaystyle m} as shape parameter and Ω {\displaystyle \Omega } as spread parameter, then its cdf is given by F X ( x ) = 1 Q m ( 0 , 2 m Ω x ) {\displaystyle F_{X}(x)=1-Q_{m}\left(0,{\sqrt {\frac {2m}{\Omega }}}x\right)}
  • If X R i c e ( ν , σ ) {\displaystyle X\sim \mathrm {Rice} (\nu ,\sigma )} is a Rice distribution with parameters ν {\displaystyle \nu } and σ {\displaystyle \sigma } , then its cdf is given by F X ( x ) = 1 Q 1 ( ν σ , x σ ) {\displaystyle F_{X}(x)=1-Q_{1}\left({\frac {\nu }{\sigma }},{\frac {x}{\sigma }}\right)}
  • If X χ k ( λ ) {\displaystyle X\sim \chi _{k}(\lambda )} is a non-central chi distribution with non-centrality parameter λ {\displaystyle \lambda } and k {\displaystyle k} degrees of freedom, then its cdf is given by F X ( x ) = 1 Q k / 2 ( λ , x ) {\displaystyle F_{X}(x)=1-Q_{k/2}(\lambda ,x)}

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. ^ 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.
  4. ^ 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
  5. ^ 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
  6. ^ a b Y. Sun and A. Baricz (2008). Inequalities for the generalized Marcum Q-function. Applied Mathematics and Computation 203(2008) 134-141.
  7. ^ 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.
  8. ^ a b c d e f A. Annamalai, C. Tellambura and John Matyjas (2009). "A New Twist on the Generalized Marcum Q-Function QM(ab) with Fractional-Order M and its Applications". 2009 6th IEEE Consumer Communications and Networking Conference, 1–5, ISBN 978-1-4244-2308-8
  9. ^ 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.
  10. ^ 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.
  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. ^ 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.
  16. ^ 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.
  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. ^ 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]