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Information to be requested with ATSI Description The gsmSCF may obtain the settings for one of the call forwarding categories (CF-U, CF-NRc, CF-B, CF-Nry) The gsmSCF may obtain the settings for one of the call barring categories (BAOC, BOIC, BOIC-exHC, BAIC, BIC-Roam) The gsmSCF may obtain an indication of all the ODB categories for the indicated subscriber Both the general ODB categories and the HPLMN-speci c ODB categories may be obtained The gsmSCF may request the subscription data of any of the subscribed CAMEL services (O-CSI, T-CSI, etc) This data element indicates which CAMEL phases are supported in the MSC/VLR for this subscriber The supported CAMEL phases are reported to the HLR during the previous location updated procedure, data restoration procedure or stand-alone insert subscriber data procedure If the subscriber has not yet registered in any MSC, then this data element is not available in HLR This data element indicates which CAMEL phases are supported in the SGSN for this subscriber

E(T S1 + T S2 + + T Sn | N (T ) = n)e T E(T U(1) + T U(2) + + T U(n) )e T

( T )n n!

E(total waiting time up to time T ) =

.

n=1

( T )n n!

Call forwarding subscription Call barring subscription Operator-determined barring CAMEL subscription information Supported CAMEL phases in MSC/VLR

T ( T )n = T , n! 2

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which proves the desired result The result (119) is simple but very useful It is sometimes used in a somewhat different form that can be described as follows Messages arrive at a communication channel according to a Poisson process with rate The messages are stored in a buffer with ample capacity A holding cost at rate h > 0 per unit of time is incurred for each message in the buffer Then, by (119), E(holding costs incurred up to time T ) = h 2 T 2 (1110)

Clustering of Poisson arrival epochs Theorem 115 expresses that Poisson arrival epochs occur completely randomly in time This is in agreement with the lack of memory of the exponential density e x of the interarrival times This density is largest at x = 0 and decreases as x increases Thus short interarrival times are relatively frequent This suggests that the Poisson arrival epochs show a tendency to cluster Indeed this is con rmed by simulation experiments Clustering of points in Poisson processes is of interest in many applications, including risk analysis and telecommunication It is therefore important to have a formula for the probability that a given time interval of length T contains some time window of length w in which n or more Poisson events occur An exact expression for this probability is dif cult to give, but a simple and excellent approximation is provided by 1 P (n 1, w) exp [ 1 w (T w)p(n 1, w)], n

where p(k, w) = e w ( w)k /k! and P (n, w) = n p(k, w) The approxik=0 mation is called Alm s approximation; see Glaz and Balakrishnan (1999) To illustrate the clustering phenomenon, consider the following example In the rst ve months of the year 2000, trams hit and killed seven people in Amsterdam, each

Data elements for ATM Description The gsmSCF may change the settings for one of the call forwarding categories The gsmSCF may change the settings for one of the call barring categories The gsmSCF may activate or de-activate a speci c CSI, except U-CSI and UG-CSI The contents of the CSI, such as gsmSCF address or service key, cannot be modi ed with ATM Neither can the gsmSCF provision or delete a CSI

case caused by the pedestrian s carelessness In the preceding years such accidents occurred on average 37 times per year Is the clustering of accidents in the year 2000 exceptional It is exceptional if seven or more fatal accidents occur during the coming ve months, but it is not exceptional when over a period of ten years (say) seven or more accidents happen in some time window having a length of ve months The above approximation gives the value 0104 for the probability that over a period of ten years there is some time window having a length of ve months in which seven or more fatal accidents occur The exact value of the probability is 0106

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