## 3 Numerical Examples  
In this section, the performance of the proposed handover is obtained using simulation developed by JAVA and the parameters used in this simulation are listed in Table 3.  
In our numerical examples, we assume two hot spot areas and each hot spot area has randomly deployed 20 small cells. Ten small cells are randomly deployed outside hot spot areas. 80% of UEs are randomly distributed within 40 small cells in hot spot areas and remaining 20% UEs are randomly distributed outside hot spot areas. In numerical examples, HM represents results obtained from the minimization of the number of handovers and EM represents results obtained from the minimization of energy consumption of BSs. Figure 8 shows the number of handovers and total energy consumption of BSs for varying the number of UEs in a macro cell. As shown in Figure 8, the number of handovers and total energy consumption of BSs in both the CAC-based handover and the proposed handover schemes increase as the number of UEs in a macro cell increases since more UEs result in more handovers and more energy consumption. The number of handovers of the proposed handover scheme is smaller than that of the CAC-based handover in both handover minimization and energy minimization. This is because a UE with low speed and low rate data traffic can be managed by a macro cell and results in the reduction of handovers. Also, the total energy consumption of the proposed handover scheme is smaller than that of the CAC-based handover in both handover minimization and energy minimization. This is because a UE with low speed and low rate data traffic can be managed by a macro cell and more small cells can be turned off to save energy. Both CAC-based handover and the proposed handover have smaller number of handovers when handover minimization is applied than those when \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of small cells & 50 \\ Number of UEs & 250 \\ Radius of a macro cell & 1000 m \\ Radius of a small cell & 50 m \\ transmit power of a macro cell & 46 dBm \\ transmit power of a small cell & 25 dBm \\ RSRP\({}_{\text{th}}\) & \(-13.98\) dBm \\ P\({}_{\text{Macro\_static}}\) & 780 W \\ P\({}_{\text{Small\_static}}\) & 21.6 W \\ P\({}_{\text{Macro\_linear}}\) & 540 W \\ P\({}_{\text{Small\_linear}}\) & 5.4 W \\ Capacity of a macro cell & 500 \\ Capacity of a small cell & 25 \\ C\({}_{\text{req\_low}}\) & 0.05 \\ C\({}_{\text{req\_high}}\) & 1 \\ TH\({}_{\text{HO}}\) & 0.7 \\ Ratio of high rate data traffic & 0.5 \\ v\_threshold & 50 km/h \\ v\_max & 100 km/h \\ L\_threshold & 0.1 \\ PSO Iteration & 500 \\ PSO Swarm Size & 3 \\ c\({}_{1}\) & 0.5 \\ c\({}_{2}\) & 0.5 \\ r\({}_{1}\) & 0.5 \\ r\({}_{2}\) & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation Parameters. energy minimization is applied, respectively. However, both CAC-based handover and the proposed handover have smaller total energy consumption when energy minimization is applied than those when handover minimization is applied. These results show that the proposed handover scheme can efficiently reduce either the number of handovers or the total energy consumption by applying either handover minimization or energy minimization depending on the objective of the application. Figure 9 shows the number of handovers and total energy consumption of BSs for varying the ratio of high rate data traffic. In the CAC-based handover scheme, the number of handover does not depend on the ratio of high rate data traffic, since in the CAC-based handover scheme the handover target cell decision depends on the RSRP, the speed of UE, and the capacity and traffic load of BS and it does not depend on the type of data traffic. In the proposed handover scheme, the handover target cell decision depends on the type of data traffic. Therefore, if the ratio of high rate data traffic is low, most of the data traffic are managed by a macro cell and the number of optimal handovers is small.