 method proposed in section 7.3.1 for dimensioning a single IP-based lub link. As can be seen in section 7.3.1, the proposed method already considers the situation of mixing with RT traffic that has a higher priority. Thus, the proposed analytical approach can also be applied quite well here for the mixed traffic scenario. Graph (b) evaluates the deviations of the calculated overbooking factor to the simulated ones. The graph (a) of Figure 8.23 shows the similar conclusion as derived from Figure 8.21: the optimum overbooking factor becomes larger when there are more Node Bs in the RAN. In this example, the accuracy of the proposed analytical approach is also demonstrated for the mixed traffic scenario. The above presented results of different scenarios demonstrate that the analytically derived overbooking factors match well with the ones derived from simulations. The relative errors are all below 10%. It demonstrates the analytical approach proposed in section 8.4.1 can give quite accurate dimensioning results and thus can derive a proper setting for the overbooking for various network scenarios. Moreover, from the above results it can be concluded that the overbooking is strongly related to the number of Node Bs, last mile utilizations, and the traffic types. The relation of these factors with the setting of optimum overbooking factor are similar to conclusions obtained in the ATM-based multi-lub UTRAN. 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 be applied to analytically evaluate the relation of overbooking factor and different network or traffic configurations (such as network size and traffic demands). Figure 8.24, Figure 8.25 and Figure 8.26 show examples of pure elastic traffic scenario. Figure 8.27 shows an example of a mixed traffic scenario. The values of optimum overbooking factor shown in these graphs are derived from the analytical approach. In Figure 8.24, the r