 Node Bs with different last mile utilization voice scenario: 4 Node Bs with different last mile utilization MMPP/D/1 model simulations last mile utilization last mile utilization (a) overbooking factor (b) relative error Figure 8.17: Validating the calculated Overbooking factor for a voice traffic scenario In the following, an example of elastic traffic is validated shown in Figure 8.18. The required QoS for dimensioning is the user-related application QoS, i.e. in this context the end- to-end delay factor. Approach (3) Desired E2E delay factor > 1 Approach (3) - Desired E2E delay factor > 1 M/G/R-PS Simulation Target end-to-end delay factor Target end-to-end delay factor (a) overbooking factor (b) relative error Figure 8.18: Validating the calculated Overbooking factor for a web traffic scenario The presented results are taken from the scenario given in section 8.3.3 for validating approach (3). There, the target delay factors for the individual links are assigned according to the suggested approach (3). For the assigned delay factors, the bandwidths for the last mile link and the backbone link are derived separately with the proposed extended M/G/R-PS model. Then based on the dimensioned bandwidths, the overbooking factor can be calculated with equation (8.1). Figure 8.18 compares the overbooking factor derived from the analytical approach and the one from simulations. Based on the results shown in Figure 8.9, Figure 8.10, and Figure 8.18, it can be concluded that the analytical dimensioning approaches presented in section 8.3.2 for elastic traffic can give quite accurate predictions on the bandwidth demands for the individual links and in turn derive a correct overbooking factor for the backbone link, for different desired end-to-end delay factors. 8 Dimensioning for Multi-lub RAN Scenario After validating the proposed analytical approach for deriving the optimum overbooking factors for the lub backbone link under different network scenarios, the analytical approach can also be applied to analytically evaluat