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Letting P (k, t) be the m m matrix whose (i, j )th element is Pij (k, t), we have in matrix notation that d P (k, t) = P (k, t)(Q dt

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Multiply componentwise both sides of this matrix equation by zk and sum over k Since the generating function of the convolution of two sequences is the product of the generating functions of the two sequences, it follows that d P (z, t) = P (z, t)D(z) dt For each xed i this equation gives a system of linear differential equations in Pij (z, t) for j = 1, , m Thus, by a standard result from the theory of linear

differential equations, we obtain Pi (z, t) = eD(z)t Pi (z, 0) (144)

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where Pi (z, t) is the ith row of the matrix P (z, t) Since Pi (z, 0) equals the ith unit vector ei = (0, , 1, , 0), it next follows that P (z, t) = eD(z)t , as was to be proved In general it is a formidable task to obtain the numerical values of the probabilities Pij (k, t) from the expression (144), particularly when m is large The numerical approach of the discrete FFT method is only practically feasible when the computation of the matrix eD(z)t is not too burdensome Numerous algorithms for the computation of the matrix exponential eAt have been proposed, but they do not always provide high accuracy The computational work is simpli ed when the m m matrix A has m different eigenvalues 1 , , m (say), as is often the case in applications It is well known from linear algebra that the matrix A can then be diagonalized as A = S S 1 , where the diagonal matrix is given by = diag( 1 , , m ) and the column vectors of the matrix S are the linearly independent eigenvectors associated with the eigenvalues 1 , , m Moreover, by An = S n S 1 , it holds that eAt = S diag(e 1 t , , e m t )S 1 Fast codes for the computation of eigenvalues and eigenvectors of a (complex) matrix are widely available To conclude this section, it is remarked that the matrix D(z) in the matrix exponential eD(z)t has a very simple form for the important case of single arrivals (ie ai(1) = 1 for i = 1, , m) It then follows from (141) and (142) that D(z) = Q + z, |z| 1

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a group of call parties and to control the entire call When a call is established, eg an MO call, a call segment is created in the gsmSSF; see Figure 64 A call segment represents a call connection for one or more call parties For a two-party call, the MSC/gsmSSF typically has one call segment to connect calling party and called party All parties that reside in one call segment have a speech connection with one another The call segment that is created during call establishment is designated call segment 1, also known as the primary call segment Call segment 1 may contain one, two or more call parties All other call segments that exist in a call contain exactly one call party Therefore, to place two or more parties in speech connection with one another, these parties are placed in call segment 1 Each call party is represented by a leg number The following rules apply for the leg numbering Leg 1 this is the calling party in the call; in an NC call, there is no calling party and hence no leg 1; Leg 2 this is the called party, resulting from the call establishment from the calling party; in an NC call, there is neither calling party nor called party and hence, no leg 1 or 2 Leg > 2 any leg that is created by the SCP has leg number 3 or higher

The arrival process with single arrivals is called the Markov modulated Poisson process A special case of this process is the switched Poisson process which has only two arrival rates (m = 2) This model is frequently used in applications In the special case of the switched Poisson process, the following explicit expressions can be given for the generating functions Pij (z, t) :

Pii (z, t) =

1 {r2 (z) ( i (1 z) + i )}e r1 (z)t r2 (z) r1 (z) {r1 (z) ( i (1 z) + i )}e r2 (z)t , i = 1, 2,

.

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