Q-function
In statistics, the Q-function is the tail probability of the standard normal distribution.[1][2] 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.
If the underlying random variable is y, then the proper argument to the tail probability is derived as:
which expresses the number of standard deviations away from the mean.
Other definitions of the Q-function, all of which are simple transformations of the normal cumulative distribution function, are also used occasionally.[3]
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
Thus,
where Φ(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[2]
An alternative and more useful form of the Q-function known as Craig's formula, after its discoverer, is expressed as:[4]
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 finite.
- The Q-function is not an elementary function. However, the bounds
- become increasingly tight for large x, and are often useful.
- Using the substitution v =u2/2, the upper bound is derived as follows:
- Similarly, using φ′(u) = −uφ(u) and the quotient rule,
- Solving for Q(x) provides the lower bound.
- The Chernoff bound of the Q-function is
- A pure exponential approximation is given by Chiani, Dardari & Simon (2003)
Values
The Q-function is well tabulated and can be computed directly in most of the mathematical software packages such as Matlab and Mathematica. Some values of the Q-function are given below for reference.
Q(0.0) = 0.500000000 |
Q(1.0) = 0.158655254 |
Q(2.0) = 0.022750132 |
Q(3.0) = 0.001349898 |
This article needs additional citations for verification. (September 2011) |
References
- ^ The Q-function, from cnx.org
- ^ a b Basic properties of the Q-function
- ^ Normal Distribution Function - from Wolfram MathWorld
- ^ John W. Craig, A new, simple and exact result for calculating the probability of error for two-dimensional signal constellaions, Proc. 1991 IEEE Military Commun. Conf., vol. 2, pp. 571–575.
- Chiani, M.; Dardari, D.; Simon, M. K. (2003), "New Exponential Bounds and Approximations for the Computation of Error Probability in Fading Channels", IEEE Transactions on Wireless Communications, 4 (2): 840–845, doi:10.1109/TWC.2003.814350.
- Karagiannidis, George K.; Lioumpas, Athanasios S. (2007), "An improved approximation for the Gaussian Q-function", IEEE Communications Letters, 11 (8): 644-646.