e from the figure that the flows with the low RAB rate achieve longer transaction times than calculated with the M/G/R-PS model, while the high RAB rate flows experience a better delay than calculated. The reason for this unfairness is that the high-peak-rate flows takes advantage of lower RTTs and can attain more network resources, especially at the beginning of the transmission phase. This is further proven in Appendix A.14. The following simulation results investigate the dependency of different bearer (or user) distributions on the application performance. Figure 6.31 compares the average transfer delay of each RAB type for transferring a 50 kbyte file (ftp application), under three different bearer distributions: case 1: 50% RAB 64, 30% RAB 128, and 20% RAB 384 case 2: 33.3% RAB 64, 33.3% RAB 128, 33.3% RAB 384 case 3: 30% RAB 64, 20% RAB 128, and 50% RAB 384 It shows that the performance of each RAB is insensitive to the bearer distributions. This is due to a fair sharing of the available resources is achieved among all ongoing TCP flows present, which is regardless of the peak data of individual flows. Thus the performance of each RAB type is only dependent on its own peak data rate and the total contributed loads 6.3 Dimensioning for Elastic Traffic from all RAB types. From the dimensioning perspective, the accumulation of the loads of different RABs for dimensioning purposes is essential; otherwise the underestimated multiplexing gain per independent bearer type results in an underutilization of the link (over- achieving the delay requirements). Impact of different bearer distributions (FTP with file size 50 kbyte) RAB 64kbps 50% RAB 64 30% RAB 128 case 1 20% RAB 384 33% RAB 64 33% RAB 128 case 2 RAB 128kbps 33% RAB 384 30% RAB 64 RAB 384kbps 20% RAB 128 case 3 50% RAB 384 total load Figure 6.31: The impact of different distributions of various RAB types: ftp scenario, no CAC, constant file size (50 kbyte) The general form of the M/G/R-PS mode presented in section 6.3.7.3 was validated for different bear