ooking for the backbone link which needs to satisfy the defined the QoS of every service class. Figure 8.21 presents overbooking factor related to a different number of Node Bs. In this example, the configured simulation settings are: There are several Node Bs in the RAN, structured as a star topology network architecture. Each last mile link is configured with 1.6 Mbps for the user plane. Each Node B carries five NRT service classes as defined in Table 8.2. Each Node B generates the same traffic demand, having a 30% last mile utilization. All NRT service classes apply the same web traffic model: page size is 50 kbyte of a constant distribution. The maximum number of elastic flows per service class is given in Table 8.6. The desired end-to-end delay factor per service class is also defined in Table 8.6. The configurations for WRED and WFQ functions are given in Table 8.3. Maximum number of Target end-to-end delay factor Service Class elastic traffic flows (QoS criteria) AF11- RAB 64 kbps AF21- RAB 128 kbps AF31- RAB 256 kbps AF41- RAB 384 kbps BE HSPA Table 8.6: Settings for different service classes (overbooking) For satisfying the required QoS of each service class defined in Table 8.6, the recommended optimum overbooking factors for setting the backbone link are shown in the graph (a) of Figure 8.21 for different number of Node Bs. Both the analytical and simulated results are presented. First of all, from graph (a) it can be observed that for a fixed 30% last mile utilization the optimum overbooking factor gets larger with higher number of Node Bs. This observation complies with the rule summarized from the last section for investigating the influence of a different number of Node Bs on the optimum overbooking in the ATM-based UTRAN. The improved overbooking capability is due to a higher multiplexing gain caused by a higher degree of aggregation with the increased number of Node Bs. Secondly, to validate the applied analytical approach, graph (a) also compares the calculated overbooking factor with the simula