## 7 Performance Evaluation
### Main Reachable Results  
Figure 8 illustrates how the utility is perceived by users by varying the NSIS-MIP traffic percentages and some prediction input parameters. As illustrated later, it must be outlined that the wireless channel evolution does not depend on the adopted prediction policy, so it has a similar evolution behavior for both static and dynamic case. In fact, in both cases (static and dynamic), if the percentage of NSIS-MIP flows increases the perceived utility is higher, due to the lower number of admitted NSIS-MDP flows, which makes the bandwidth availability higher. Additionally, in this case the static prediction policy offers better performances, while low values of delta are preferred if a dynamic policy is pursued. Figure 7: The simulated map. System utilization for increasing values of NSIS-MIP requests is shown in Figure 9. In both static and dynamic cases, the trend is decreasing because there is a higher presence of passive reservations, and a consequent reduction of NSIS-MDP admitted flows. For NSIS-MIP service requests equal to 0, the utilization reach its maximum value (about 92-93%), because the system will not provide passive reservations. On the other side, the system becomes under-utilized because only NSIS-MIP users make service requests. The maximum gap between static and dynamic schemes is observed for an NSIS-MIP percentage of 60% and it is about 10-12%. The performance of the static scheme are slightly better, except for high percentages of NSIS-MIP flows, because high values of \(\delta\) lead to a system utilization near to 46%. Figure 8: Average perceived utility vs NSIS-MIP traffic percentage. Figure 9: Average system utilization vs NSIS-MIP traffic percentage. Figures 10 and 11 depict the average trend of the number of NSIS-MIP and NSIS-MDP admitted flows, respectively; both in the static case and in the dynamic one there is an obvious increasing behavior of NSIS-MIP admissions for a higher number of NSIS-MIP requests, while the NSIS-MDP ones drastically decrease. For the static prediction case the number of admitted NSIS-MIP flows is often lower than 200 while, in the dynamic case, for \(\delta=0.3\), \(\delta=0.6\) and \(\delta=0.7\) the number of admitted NSIS-MIP flows is not comparable, because it increases to about 780. Both in the static or dynamic cases the NSIS-MDP admission is not affected by the chosen policy and the chosen input parameters. The  
Figure 12 illustrates the average number of dropped NSIS-MIP flows: these curves show how the CAC module and the bandwidth reallocation algorithm guarantee a very low dropping probability to NSIS-MIP flows. In the dynamic prediction case, the number of NSIS-MIP dropped flows is higher than the static one, but if it is compared with the average number of admitted NSIS-MIP flows, the percentage of NSIS-MIP dropped flows maintains at a very low and acceptable level, as well as in the static case. The minimum and maximum NSIS-MIP dropping observed percentages for the static case are 0.0285% and 0.3%, while for the dynamic prediction case they are 0.31% and 0.87%. It can be concluded that both prediction schemes with the CAC and bandwidth management of Section 5 have good performances in terms of percentage of NSIS-MIP dropped flows, which is maintained below 1%. For NSIS-MDP users (not shown) the proposed bandwidth management algorithm gives only an outage intra-cell guarantee, so they may not find the needed bandwidth after a hand-off event; in addition if an NSIS-MIP user need some bandwidth to mitigate its outage condition, the algorithm drops an NSIS-MDP flow if no bandwidth is available. In Figure 13 the average error committed in the prediction of possible next cells for the third hand-off event of NSIS-MIP users in the static and dynamic cases can be seen.