Q-function

A plot of the Q-function.

In statistics, the Q-function is the tail distribution function of the standard normal distribution. In other words, $Q(x)$ is the probability that a normal (Gaussian) random variable will obtain a value larger than $x$ standard deviations above the mean. Equivalently, $Q(x)$ is the probability that a standard normal random variable takes a value larger than $x$.

If $Y$ is a Gaussian random variable with mean $\mu$ and variance $\sigma ^{2}$, then $X={\frac {Y-\mu }{\sigma }}$ is standard normal and

$P(Y>y)=P(X>x)=Q(x)$

where $x={\frac {y-\mu }{\sigma }}$.

Other definitions of the Q-function, all of which are simple transformations of the normal cumulative distribution function, are also used occasionally.

Because of its relation to the cumulative distribution function of the normal distribution, the Q-function can also be expressed in terms of the error function, which is an important function in applied mathematics and physics.

Definition and basic properties

Formally, the Q-function is defined as

$Q(x)={\frac {1}{\sqrt {2\pi }}}\int _{x}^{\infty }\exp \left(-{\frac {u^{2}}{2}}\right)\,du.$

Thus,

$Q(x)=1-Q(-x)=1-\Phi (x)\,\!,$

where $\Phi (x)$ is the cumulative distribution function of the normal Gaussian distribution.

The Q-function can be expressed in terms of the error function, or the complementary error function, as

${\begin{aligned}Q(x)&={\frac {1}{2}}\left({\frac {2}{\sqrt {\pi }}}\int _{x/{\sqrt {2}}}^{\infty }\exp \left(-t^{2}\right)\,dt\right)\\&={\frac {1}{2}}-{\frac {1}{2}}\operatorname {erf} \left({\frac {x}{\sqrt {2}}}\right)~~{\text{ -or-}}\\&={\frac {1}{2}}\operatorname {erfc} \left({\frac {x}{\sqrt {2}}}\right).\end{aligned}}$

An alternative form of the Q-function known as Craig's formula, after its discoverer, is expressed as:

$Q(x)={\frac {1}{\pi }}\int _{0}^{\frac {\pi }{2}}\exp \left(-{\frac {x^{2}}{2\sin ^{2}\theta }}\right)d\theta .$

This expression is valid only for positive values of x, but it can be used in conjunction with Q(x) = 1 − Q(−x) to obtain Q(x) for negative values. This form is advantageous in that the range of integration is fixed and finite.

• The Q-function is not an elementary function. However, the bounds, where $\phi (x)$ is the density function of the standard normal distribution,

$\left({\frac {x}{1+x^{2}}}\right)\phi (x)<Q(x)<{\frac {\phi (x)}{x}},\qquad x>0,$

become increasingly tight for large x, and are often useful.

Using the substitution v =u²/2, the upper bound is derived as follows:

$Q(x)=\int _{x}^{\infty }\phi (u)\,du<\int _{x}^{\infty }{\frac {u}{x}}\phi (u)\,du=\int _{\frac {x^{2}}{2}}^{\infty }{\frac {e^{-v}}{x{\sqrt {2\pi }}}}\,dv=-{\biggl .}{\frac {e^{-v}}{x{\sqrt {2\pi }}}}{\biggr |}_{\frac {x^{2}}{2}}^{\infty }={\frac {\phi (x)}{x}}.$

Similarly, using $\phi '(u)=-u\phi (u)$ and the quotient rule,

$\left(1+{\frac {1}{x^{2}}}\right)Q(x)=\int _{x}^{\infty }\left(1+{\frac {1}{x^{2}}}\right)\phi (u)\,du>\int _{x}^{\infty }\left(1+{\frac {1}{u^{2}}}\right)\phi (u)\,du=-{\biggl .}{\frac {\phi (u)}{u}}{\biggr |}_{x}^{\infty }={\frac {\phi (x)}{x}}.$

Solving for Q(x) provides the lower bound.

• The Chernoff bound of the Q-function is

$Q(x)\leq e^{-{\frac {x^{2}}{2}}},\qquad x>0$

• Improved exponential bounds and a pure exponential approximation are

$Q(x)\leq {\tfrac {1}{4}}e^{-x^{2}}+{\tfrac {1}{4}}e^{-{\frac {x^{2}}{2}}}\leq {\tfrac {1}{2}}e^{-{\frac {x^{2}}{2}}},\qquad x>0$

$Q(x)\approx {\frac {1}{12}}e^{-{\frac {x^{2}}{2}}}+{\frac {1}{4}}e^{-{\frac {2}{3}}x^{2}},\qquad x>0$

• A tight approximation of $Q(x)$ for $x\in [0,\infty )$ is given by Karagiannidis & Lioumpas (2007) who showed for the appropriate choice of parameters $\{A,B\}$ that

$f(x;A,B)={\frac {\left(1-e^{-Ax}\right)e^{-x^{2}}}{B{\sqrt {\pi }}x}}\approx \operatorname {erfc} \left(x\right).$

The absolute error between $f(x;A,B)$ and $\operatorname {erfc} (x)$ over the range $[0,R]$ is minimized by evaluating

$\{A,B\}={\underset {\{A,B\}}{arg\ min}}{\frac {1}{R}}\int _{0}^{R}|f(x;A,B)-\operatorname {erfc} (x)|dx.$

Using $R=20$ and numerically integrating, they found the minimum error occurred when $\{A,B\}=\{1.98,1.135\},$ which gave a good approximation for $\forall x\geq 0.$

Substituting these values and using the relationship between $Q(x)$ and $\operatorname {erfc} (x)$ from above gives

$Q(x)\approx {\frac {\left(1-e^{-1.4x}\right)e^{-{\frac {x^{2}}{2}}}}{1.135{\sqrt {2\pi }}x}},x\geq 0.$

Inverse Q

The inverse Q-function can be related to the inverse error functions:

$Q^{-1}(y)={\sqrt {2}}\ \mathrm {erf} ^{-1}(1-2y)={\sqrt {2}}\ \mathrm {erfc} ^{-1}(2y)$

The function $Q^{-1}(y)$ finds application in digital communications. It is usually expressed in dB and generally called Q-factor:

$\mathrm {Q{\text{-}}factor} =20\log _{10}\!\left(Q^{-1}(y)\right)\!~\mathrm {dB}$

where y is the bit-error rate (BER) of the digitally modulated signal under analysis. For instance, for QPSK in additive white Gaussian noise, the Q-factor defined above coincides with the value in dB of the signal to noise ratio that yields a bit error rate equal to y.

Q-factor vs. bit error rate (BER).

Values

The Q-function is well tabulated and can be computed directly in most of the mathematical software packages such as R and those available in Python, MATLAB and Mathematica. Some values of the Q-function are given below for reference.

Q(0.0)	0.500000000	1/2.0000
Q(0.1)	0.460172163	1/2.1731
Q(0.2)	0.420740291	1/2.3768
Q(0.3)	0.382088578	1/2.6172
Q(0.4)	0.344578258	1/2.9021
Q(0.5)	0.308537539	1/3.2411
Q(0.6)	0.274253118	1/3.6463
Q(0.7)	0.241963652	1/4.1329
Q(0.8)	0.211855399	1/4.7202
Q(0.9)	0.184060125	1/5.4330

Q(1.0)	0.158655254	1/6.3030
Q(1.1)	0.135666061	1/7.3710
Q(1.2)	0.115069670	1/8.6904
Q(1.3)	0.096800485	1/10.3305
Q(1.4)	0.080756659	1/12.3829
Q(1.5)	0.066807201	1/14.9684
Q(1.6)	0.054799292	1/18.2484
Q(1.7)	0.044565463	1/22.4389
Q(1.8)	0.035930319	1/27.8316
Q(1.9)	0.028716560	1/34.8231

Q(2.0)	0.022750132	1/43.9558
Q(2.1)	0.017864421	1/55.9772
Q(2.2)	0.013903448	1/71.9246
Q(2.3)	0.010724110	1/93.2478
Q(2.4)	0.008197536	1/121.9879
Q(2.5)	0.006209665	1/161.0393
Q(2.6)	0.004661188	1/214.5376
Q(2.7)	0.003466974	1/288.4360
Q(2.8)	0.002555130	1/391.3695
Q(2.9)	0.001865813	1/535.9593

Q(3.0)	0.001349898	1/740.7967
Q(3.1)	0.000967603	1/1033.4815
Q(3.2)	0.000687138	1/1455.3119
Q(3.3)	0.000483424	1/2068.5769
Q(3.4)	0.000336929	1/2967.9820
Q(3.5)	0.000232629	1/4298.6887
Q(3.6)	0.000159109	1/6285.0158
Q(3.7)	0.000107800	1/9276.4608
Q(3.8)	0.000072348	1/13822.0738
Q(3.9)	0.000048096	1/20791.6011
Q(4.0)	0.000031671	1/31574.3855

Generalization to high dimensions

The Q-function can be generalized to higher dimensions:

$Q(\mathbf {x} )=\mathbb {P} (\mathbf {X} \geq \mathbf {x} ),$

where $\mathbf {X} \sim {\mathcal {N}}(\mathbf {0} ,\,\Sigma )$ follows the multivariate normal distribution with covariance $\Sigma$ and the threshold is of the form $\mathbf {x} =\gamma \Sigma \mathbf {l} ^{*}$ for some positive vector $\mathbf {l} ^{*}>\mathbf {0}$ and positive constant $\gamma >0$. As in the one dimensional case, there is no simple analytical formula for the Q-function. Nevertheless, the Q-function can be approximated arbitrarily well as $\gamma$ becomes larger and larger.