th due to its less bursty nature than the web traffic. So for this network QoS target the voice traffic can be set to a higher OB _factor than the web traffic. Consequently, the larger the configured OB factor, the higher is the resultant backbone link utilization. PS only voice only 85%PS+15%voice 40%PS+60%voice PS only voice only 85%PS+15%voice 40%PS+60%voice last mile utilization last mile utilization Figure A.33: Optimum OB factor (left), link utilization (right) vs. last mile utilizations Appendix Overbooking under different last mile link capacities Figure A.34 and Figure A.35 present the impact of the last mile link capacity on the selection of the optimum OB _factor. Figure A.34 compares the optimum OB factors over different last mile utilizations under two different last mile link rates, 1.6 Mbps and 2.5 Mbps (for user plane) with 4 Node Bs connected to the backbone link. It is seen that for the packet- switched and packet-switched traffic dominant scenarios, with 2.5 Mbps last mile link the obtained optimum OB _factor is considerably higher than with 1.6 Mbps, but there is no significant gain for the voice only and voice dominant scenarios. For the bursty packet- switched data traffic, the self-similar traffic property allows a high probability of utilizing the spare resources of other users that are not transmitting. Using a higher last mile link resulting in more simultaneous connections can achieve more statistical multiplexing gain, and hence a higher overbooking is allowed. But the voice traffic is Poisson like traffic, i.e. the voice traffic will be smoothed under a high level aggregation, SO with enough number of voice users the variations of the aggregated traffic remain relatively stable, therefore the last mile link capacity does not play an important role on overbooking in this case, SO the statistical multiplexing achieved by 1.6 Mbps is similar to that achieved by 2.5 Mbps link. (1) PS only (2) Voice only 1.6Mbps 1.6Mbps 2.5Mbps 2.5Mbps last mile utilization last mile utilization (3) PS dom