## 7 Performance Evaluation
### General Simulation Setup and Parameters  
Our network consists of 65 coverage cells, with a coverage radius of about 250 m. The considered geographical region (south of Italy) is a 2.44 km\({}^{2}\) area (Figure 7), and users move according to C4R and CityMob; the APs are wired connected, by a switching subnet, to the net-sender. The performances of the rate adaptation and CAC schemes for NSIS-MIP and NSIS-MDP users are also taken into account, in order to evaluate their conformance to QoS parameters (outage probability, minimum received utility values and a high system utilization). All the results have been obtained by following the theory of the confidence intervals as illustrated in [50,53]: the number of runs has been set to \(N_{sim}=15\), with a simulation time of \(T_{sim}=400\) s. In this way the committed error while considering simulation results has been limited to a maximum value of 5% (confidence of 95%). An exponentially distributed CHT with mean \(\lambda=180\) s was considered. Traffic load was fixed to 15 requests/s because it guarantees a good level of system saturation. When a session starts, packets are generated as in [54], based on Poisson and Pareto distributions using a file size distribution heavy-tailed with Pareto parameter fixed to 1.85. Each AP has a total bandwidth of 11 Mbps and users can receive discrete resource levels, from 512 Kbps up to 896 Kbps (with a gap level of 128 Kbps). The considered utility functions are the same of those illustrated in Section 5. It must be outlined that the current AP dynamically choses the right value of \(B\). In our simulations, traffic load is composed of NSIS-MIP and NSIS-MDP flows in variable percentage. The bandwidth is managed by the policies illustrated in Section 5 and the \(p_{outage}\) value is set to 0.05.  
There are no rules about choosing the value of \(n\): simulations results showed that \(n=6\) is a good trade-off between accuracy and computational complexity of the proposed algorithm; higher values of n make better the approximation of the wireless cell coverage area but make worse the spent computational time. Simulation results of the dynamic-scheme are compared with those of the static-scheme and the obtained enhancements are shown. First of all, the HDP matrix \(M\) was filled up, so a certain number of monitor simulations were launched; in particular 1000 monitor runs were executed with a single duration of \(T_{sim}=400\) s, in order to obtain a smoother distribution curve. So, after a statistical analysis of the obtained values with MATLAB tool, the matrix M has been generated such as shown in Figure 5. The static algorithm was tested with the following input parameters:  
1. constant trend: \(C_{i1}=C_{i2}=C_{i3}=1\);
2. increasing trend: \(C_{i1}=1\), \(C_{i2}=2\), \(C_{i3}=3\);(3) decreasing trend: \(C_{i1}=3\), \(C_{i2}=2\), \(C_{i3}=1\) with \(C_{ij}=1\)\(\forall j\in\{4\dots\ C_{ei-1}\}\);  
The input values of \(C_{ij}\) are different from those of Section 6 because attention was focused only on the first, the second and the third hand-off events.  
In this section, only the results for case (b) of Section 6, with \(\Delta=\delta^{ak}\) are considered, because the employing of the \(\alpha k\) exponent into the dynamic algorithm leads to better results. After a deep analysis of the possible values of \(\alpha\) the value \(\alpha=1\) was chosen, because it guarantees the optimal performances for the chosen exponent function. As for the monitor simulations, the duration was fixed to \(T_{sim}=400\) s for each run. Different campaigns were carried out, also varying the amount of NSIS-MIP and NSIS-MDP traffic percentages; in the following, if 60% is the NSIS-MIP percentage then, obviously, 40% is the percentage of NSIS-MDP traffic.