bove two examples demonstrate that when only one of the link is congested in the lub transport network the corresponding lub interface can be modeled as a single congested link. Thus the average end-to-end application delay can be estimated by applying the M/G/R- PS model for that congested link. Case (3) is a more common scenario, as often in the access network both last mile link and backbone link are configured with limited link bandwidths SO that the congestion can happen on both links. Figure 8.7 validates the two proposed methods to calculate the end-to-end delay for the case (3). In the given example, there are 4 Node Bs each connected to an E1 link (1600kbps used for the user plane). The backbone link is configured with 4.8 Mbps. As shown in Figure 8.7, both proposed methods can estimate the end-to-end application delay with below 10% relative error (shown in the right graph). But by comparing the relative error of each method, it is found that the second method given by equation (8.4) offers a better estimation for the end-to-end delay. The first method, which takes the maximum delay factor as the end-to-end delay factor, leaves out the additional delay caused by the other less congested link. Therefore, it will result in an underestimation of the end-to-end delay as shown in the left graph in Figure 8.7. Hence, the second method given by equation (8.4) is recommended to estimate the end-to-end application performance as it gives a better result. This method has been validated in different simulation scenarios and demonstrated that it has the best estimation accuracy. Thus in the following the second method is applied to perform network dimensioning in a multi-lub RAN. Moreover, it shall be noticed that the second method can also be applied for network case (1) and (2) where the end-to-end delay factor f just equals to the delay factor of the congested link. 8.3 Dimensioning for Elastic Traffic in ATM-Based UTRAN Last mile=1600kbps, backbone=4800kbps Last mile=1600kbps,backbone=4800kbps max.(f1,f2) f=max