e the impact of different network and traffic configurations on the overbooking. In the following results, the overbooking is calculated for elastic traffic to satisfy a desired application QoS (end-to-end delay factor). Figure 8.19 shows the derived optimum overbooking factor over a different number of Node Bs under different last mile loads, and Figure 8.20 presents the overbooking factor over different delay factors for different offered last mile loads. last mile load = 20% last mile load = 30% last mile load = 40% last mile load = 50% Number of NodeBs Figure 8.19: Overbooking factors over different number of Node Bs (RAB=128) f=1.2) last mile load = 20% last mile load = 30% last mile load = 40% last mile load = 60% last mile load = 80% Delay factor Figure 8.20: Overbooking factors over different QoS requirements (RAB=128) It can be observed in Figure 8.19 and Figure 8.20 that the optimum overbooking factor is influenced by the network size (number of Node Bs), given traffic demands (last mile utilizations) and QoS requirements. The overbooking factor is usually reduced when the last mile utilization increases. When there are more Node Bs in the UTRAN, the overbooking capability is increased due to a higher multiplexing gain. The desired delay factors also have influence on the overbooking results. The higher the delay factor, the higher overbooking can be achieved, but it steadily approaches a maximum value. 8.5 Overbooking of the lub Backbone Link 8.5.2 Overbooking of IP-Based Iub Backbone Link This section investigates the overbooking in the IP-based multi-lub RAN where the DiffServ QoS scheme is used. Same as in the last section, different network configurations are investigated through simulations to find out what are the main factors that will have significant impact on overbooking in the IP-based UTRAN and how their influences are. In addition, the analytical approach, which is proposed in section 8.4.1 for dimensioning the backbone link for elastic traffic, is applied to estimate the appropriate overb