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Hence, the PDP context is the data channel between the user and the GGSN The GTP-U messages that are transferred through the PDP context contain the T-PDUs, which are the protocol data units used for payload transfer T-PDU may contain IP packets (or other protocol is used) At the GGSN, the IP packets (or other) from the T-PDU are sent to the PDN When a PDP context is established, various elements may be supplied by the initiator of the PDP context, ie by the UE The SGSN and GGSN may also determine certain elements for the PDP context The PDP context elements that are relevant for CAMEL are listed in Table 513 These

2 + 2 )

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For a long time numerical Laplace inversion had the reputation of being dif cult and numerically unreliable However, contrary to previous impressions, it is nowadays not dif cult to compute probabilities and other quantities of interest in probability models by using reliable Laplace inversion methods This appendix brie y discusses two effective Laplace inversion algorithms These algorithms involve complex calculations There is nothing magic about doing calculations with complex numbers These calculations can be reduced to operations with real numbers by dealing separately with the real part and the imaginary part of the complex numbers Simple facts such as the relation e ix = cos(x) + i sin(x) for any real x and the representation z = rei for any complex number z are typically used in the calculations in addition to the basic rules for adding and multiplying two complex numbers Here i denotes the complex number with i 2 = 1 Certain computer languages such as the language C++ have automatic provision for doing complex calculations In many applied probability problems it is possible to derive an expression for the Laplace transform of some unknown function Let the real-valued function f (t) be an unknown function in the variable t 0 Suppose its Laplace transform f (s) =

.

in the complex variable s is known Assume that the function f (t) satis es the following conditions: 1 f (t) is of bounded variation on any nite interval

2 f (t) is continuous for t 0 3 For any b > 0 the function e bt f (t) is monotone for t t0 (b) for some number t0 (b) 4

.

In probability applications the function f (t) is often the complementary cumulative probability distribution function of a continuous random variable In this case the conditions 1 to 4 are automatically satis ed A basic result from analysis is that a real-valued function f (t) is of bounded variation if and only if it can be written as the difference of two monotone functions Under the above conditions the following version of the Poisson summation formula from Fourier analysis holds:

n=

n=

for any constants h, b > 0 This Poisson summation formula is the basis for the following algorithm of Abate and Whitt (1992)

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