bandwidths, where congestion happens on both links, i.e. fac_i>1 and fbb>1. For the above case (1) and (2), only one of the links has congestion which will influence the end-to-end delay T1, whereas the other one has a delay factor equaling to 1 and therefore does not contribute additional delays. Therefore, in these two cases, the end-to-end delay is influenced only by the congested link. Thus for estimating the end-to-end delay, the lub transport network can be seen as a single congested link and the M/G/R-PS model can be applied on the congested link (either last mile or backbone link) to calculate the expected sojourn time. In this way, the delay estimation method in these two cases is the same as the methods used for the single link case (see Chapter 6.3). However, for case (3) the end-to-end 8 Dimensioning for Multi-lub RAN Scenario delay T will be influenced by both last mile and backbone links as both links contribute additional delays to the end-to-end delay performance. Therefore, for estimating Tt the impact of the two concatenated links need to be considered at the same time, and thus the methods for a single lub case is not suitable anymore. In the following two methods are proposed to calculate the end-to-end delay factor f for case (3). Method 1: use the maximum delay factor as the end-to-end delay factor, assuming that the end-to-end delay is only determined by the main congested link (i.e. the bottleneck link) but ignoring the impact of the other one. f=max.(facistos) (8.3) In equation (8.3), the delay factor of the last mile link fac_i is calculated with the basic M/G/R-PS model (with equation (6.20)) given the last mile link capacity Cac_i and the carried traffic amount on the last mile link Pi. The delay factor of the backbone link fbb is also calculated with the basic M/G/R-PS model given the backbone link capacity Cbb and the aggregated traffic Ep. From equation (8.3), the main congested link is determined by having a larger delay factor and this delay factor will be chosen for the end-to-en