 of Node Bs (last mile links) number of Node Bs (last mile links) (a) dimensioning results (b) relative error Figure 8.2: Dimensioning the backbone link for 1% packet loss ratio Moreover the proposed dimensioning approach achieves bandwidth saving over the backbone link, since it takes the multiplexing characteristics of the aggregated traffic into account. This makes the overbooking possible on the backbone link. Figure 8.3 presents the obtained overbooking factor calculated with equation (8.1), given the dimensioned last mile and backbone link bandwidths. overbooking the backbone link for 1% packet loss ratio MMPP/D/1 model simulations number of Node Bs (last mile links) Figure 8.3: Overbooking the backbone link It shows that the obtained overbooking factors are larger than 1 and it increases with the increased number of Node Bs. It can also be seen that the overbooking factor is gradually close to a maximum value when the number of Node Bs continues to increase. This is because that when both the last mile link rate and the offered traffic on each last mile link are fixed, though the aggregated traffic on the backbone link keeps increasing with the increased 8.3 Dimensioning for Elastic Traffic in ATM-Based UTRAN number of Node Bs, the potential multiplexing gain steadily reaches a maximum value, which is equal to 1/the last mile utilization. In this example, the achieved last mile link utilization is 60.8%, thus the maximum overbooking factor is close to 1/0.68 = 1.64. 8.2.3 Summary As a summary, the presented results demonstrate that the proposed analytical approach can be applied for dimensioning the circuit-switched traffic in a multi-lub radio access network. The capacity of the last mile link and the backbone link are calculated separately: the last mile link is dimensioned with the MD-Erlang Model to satisfy a desired CAC connection reject ratio at the last mile; the backbone link is dimensioned with the application of the MMPP/D/1 model for guaranteeing a defined packet loss ratio at the backbone link.