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here, that is, with at least one PDP-context activation in the measurement window Attached but inactive users are not accounted for Note that in general UMTS capable terminals can also access GPRS RANs outside the UMTS coverage We label as GPRS users those seen exclusively on the GPRS section during the measurement period, while those who accessed UMTS at least once are classi ed as UMTS user A caveat here is that a UMTS user can also generate GPRS traf c While this traf c is correctly accounted for as GPRS, the user count in GPRS only considers GPRS-only users This leads to some overestimation of the average per-user traf c for GPRS In order to avoid such overestimation, a more complex classi cation of users and traf c is required, considering the GPRS traf c produced by UMTS capable MSs in a separate class.

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Solved this problem. Modified the data set by populating the values in the same format. Eg., I appended the object as. 123 456 789. and mapped the data-source​ ...

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For the sake of simplicity we omit such re nement in this work A study of TR1 reveals that fewer than 1% of the subscribers change cell while a packet-switched connection is active Out of this subset fewer than 1% had changed the RAN technology This validates our assumption from above From Table 121 it can be deduced that in TR1 the number of active UMTS users was approximately two orders of magnitude less than GPRS, but the two groups generated comparable traf c volumes In fact at that time a large fraction of UMTS mobile stations were 3G data-cards mounted on laptops coupled with at-rate contracts, while UMTS handheld devices were just starting to spread In TR2 the fraction of UMTS users had increased, as the combined effect of additional UMTS subscribers plus legacy GPRS users upgraded to UMTS handheld terminals.

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The fact that for UMTS the growth factor in the volume share is substantially less than for the user share suggests that most of the new additional terminals were handheld devices rather than 3G data-cards for laptops, under the assumption that hand-held devices generate less.

[34] A Gerdenitsch, S Jakl, M Toeltsch, The Use of genetic algorithms for capacity optimization in UMTS FDD networks , Proc 3rd International Conference on Networking (ICN 04), Guadeloupe, French Caribbean, March 2004 [35] HP Schwefel, Numerical Optimization of Computer Models, Wiley & Sons Ltd/Inc, Chichester, 1981 [36] HP Schwefel, G Rudolph, Contemporary evolution strategies , Advances in Artificail Life Proc 3rd European Conf Artificial Life (ECAL 95), pp 893 907, Springer, Berlin, 1995 [37] A Gerdenitsch, S Jakl, M Toeltsch and T Neubauer, Intelligent Algorithms for System Capacity Optimization of UMTS FDD Networks , Proc 4th International Conference on 3G Mobile Communication Technologies, pp 222 226, London, Great Britain, 25 27 June 2003 [38] A Gerdenitsch, System Capacity Optimization of UMTS FDD Networks, PhD thesis, Technische Universit t Wien, 2004 [39] A Gerdenitsch, S Jakl, YY Chong and M.

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Software Testing: Testing Across the Entire Software Development Life Cycle, by G. D. Everett and R. McLeod, Jr. Copyright 2007 John Wiley & Sons, Inc.

Figure 14.8 Distance between local minima and global one as a function of minimum value (Scenario 3 with 25 Monte-Carlo snapshots).

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