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active for that call in that logical MSC; and CAP v4 is used for the dialled service It is implied by the above conditions that the singe-point-of-control rule prevails; EDS is not allowed when the subscribed service from O-CSI is still active for the call; see Figures 84 and 85 In the example ow in Figure 84, the O-CSI service is still active when the MSC/gsmSSF triggers the D-CSI service Therefore, EDS is not allowed for the subscribed dialled service Likewise, N-CSI may not become an EDS service for this call In Figure 85, the subscribed service (O-CSI) does not retain the CAMEL relationship When the subscribed dialled service is triggered, the MSC/gsmSSF includes in CAP IDP an indication that EDS is allowed ( enhanced dialled services allowed ) The D-CSI service may now arm events and retain the CAMEL dialogue When the serving network dialled service is triggered, the subscribed dialled service is still active, so the MSC/gsmSSF does not include the enhanced dialled services allowed parameter in CAP IDP The gsmSSF determines whether the conditions for EDS are ful lled and informs the SCP when EDS is allowed The subscribed service, the dialled service and the enhanced dialled service use the same application context (AC) It is the responsibility of the SCP to apply EDS only when the enhanced dialled services allowed parameter is present in IDP; this parameter is de ned for CAP v4 only Hence, EDS cannot be used in combination with CAMEL phase 3

This recursion provides an effective method for computing the fj Note that the recursion can be initialized with f0 = 1 E(S), since by Little s formula the long-run fraction of time the server is busy equals E(S) Note that E(S) = (1/ ) j =1 j j Once the probabilities j (=fj ) have been computed by applying (555), the waiting-time probability Wq (x) can be calculated from (554) The expression (554) for Wq (x) is very useful for computational purposes For numerical calculations it is recommended to rewrite (554) as

Wq (x) = 1

.

( x)k k!

fj ,

x 0,

Cause diagnostics Meaning No route to destination Normal call clearing User busy Call rejected Diagnostics Condition Condition CCBS indicator Call rejected condition

(556)

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by interchanging the order of summation The series representation (556) con verges faster than the series (554) Of course j =k+1 fj should be replaced by k 1 j =0 fj in (556) The computational work in (555) and (556) can be reduced

by using asymptotic expansions for fj as j and 1 Wq (x) as x ; see Exercise 526 Example 552 A nite-buffer storage problem Data messages arrive at a transmission channel according to a Poisson process with rate The transmission channel has a buffer to store arriving messages The buffer has a nite capacity K > 0 An arriving message is only stored in the buffer when its length does not exceed the unoccupied buffer capacity, otherwise the whole message is rejected Data are transmitted from the buffer at a constant rate of > 0 The message lengths are independent of each other and are assumed to have a continuous probability distribution function F (x) An important performance measure is the long-run fraction of messages that are rejected This model, which is known as the M/G/1 queue with bounded sojourn time, is very useful It also applies to a nite-capacity production/inventory system in which production occurs at a constant rate as long as the inventory is below its maximum level and the demand process is a compound Poisson process, where demands occurring when the system is out of stock are completely lost A possible approach to solving the model is to discretize the model; see Exercise 99 for another approach In the discretized model a message is represented by a batch consisting of a discrete number of data units The probability of a batch of size k is given by bk ( ) = F (k ) F ((k 1) ), k = 1, 2,

It is assumed that the number is chosen such that K( ) is an integer An arriving message is only stored in the buffer when its batch size does not exceed the number of unoccupied buffer places, otherwise the whole message is rejected The data units are transmitted one at a time at a constant rate of > 0 The key step is now to take an exponential distribution with mean 1/ ( ) = / for the transmission time of a data unit This approach is motivated by Theorem 551 A data unit leaves the buffer as soon as its transmission is completed For the discretized model, let (K) = the long-run fraction of messages that are rejected In view of Theorem 551 one might expect that (K) is an excellent approximation to the rejection probability in the original model when is chosen suf ciently

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