ing factors will be validated through simulations. After validations, the impacts of various aspects such as the network size (the number of Node Bs), last mile utilization, traffic scenario, etc., on the overbooking are investigated by using the analytical approach as well as through simulations. At the end, the rules of setting appropriate overbooking factor are summarized. Section 8.5.1 presents the overbooking for the ATM-based UTRAN and section 8.5.2 for the IP-based UTRAN. 8.5.1 Overbooking of ATM-Based Iub Backbone Link In Appendix A.19, the impact of overbooking on the overall network and user performance is shown. Moreover, intensive investigations were carried out by extensive simulation to study the overbooking by considering both user-relevant and transport network- relevant QoS requirements in the ATM-based UTRAN, looking at different traffic scenarios, last mile loads, last mile link capacities and the number of Node Bs. The presented simulation results in Appendix A.19 show that they all have strong influences on the achieved overbooking: (1) the overbooking is decreased when the last mile utilization increases; (2); the overbooking usually increases with a larger last mile link rate; (3) when there is more circuit-switched traffic in the network, a higher overbooking can be obtained; (4) the overbooking increases with the increase of the number of Node Bs. Detailed results can be found in Appendix A.19. This section focuses on studying the applicability of the analytical approach for deriving the overbooking to satisfy user-related QoS in an ATM-based multi-lub RAN, and further apply the analytical approach to analyze the impact of different network and traffic settings on 8 Dimensioning for Multi-lub RAN Scenario the overbooking. Section 8.2 and section 8.3 have presented analytical approaches to dimension the last mile link and the backbone link individually in the multi-lub ATM RAN. In this section, they are applied to estimate proper overbooking factor for the lub backbone link. The analytical