# Binary PSO with Classification Trees Algorithm for Enhancing Power Efficiency in 5G Networks
## 5 Numerical Results  
The simulation parameters are presented in Table 1. Various Voronoi SCs are allocated in the coverage area of the MCs. The UEs are randomly deployed within the entire network. The simulations are performed utilizing MATLAB R2018. The swarm size is chosen in the range of 20-50 particles [9; 67; 68; 69; 70; 71]. It is shown in [71] that 25 is the optimum size over 16 different sizes on 3 different cases. The IW is linearly increased from 0.4 to 0.9, which is the range recommended in [33; 72; 73]. The velocity of the particles is chosen in the range of [\(-\)0.6, 0.6] [9; 74; 75]. Adjusting these parameters is essential for the convergence of the BPSO algorithm. The performance of several algorithms is assessed, they are as follow:  
* Always on: SFR is not utilized and all SCs are active. * BPSO only: SFR is not utilized, but SC on/off switching is done via BPSO algorithm. * Random 10%: SFR is not utilized, but 10% of the SCs are randomly chosen to be switched off. The remaining SCs are kept active. * Proposed: the SC on/off switching is decided first using the BPSO algorithm, then the SFR is carried out using CTs. The number of active SCs for various number of UEs is demonstrated in Figure 5. The number of active SCs increases with increasing the number of UEs, as more SCs are being activated to guarantee the minimum required SINR of the UEs allowing the UEs to associate with the SC having the better SINR, enhancing their data rates and improving the system performance. The \"\(\omega\) = 0.4\" has better convergence than the \"\(\omega\) = 0.9\" for the 200, 500, and 900 UEs, respectively, since reducing the IW facilitates in exploring the search space (global search), while raising the IW aids in exploiting (local search) the search space. The \"linearly increasing \(\omega\)\" shows better convergence than the fixed \"\(\omega\) = 0.9\" and \"\(\omega\) = 0.4\", as in the \"linearly increasing \(\omega\)\" case, the IW is lower at the beginning, allowing better exploration of the search space, and then it linearly increases, enhancing the local search. The fixed IW can get trapped during the search and is unable to find the global minimum number of active SCs. Therefore, the linearly increasing IW is chosen to be utilized in the proposed algorithm for the rest of the simulations in this paper. Figure 4: Classification tree (CT) example for three sub-bands. The number of active SCs for various numbers of UEs is presented in Figure 6. The \"always on\" algorithm has the highest number of active SCs, since all the SCs are kept active. The \"random 10%\" algorithm has a lower number of active SCs than the \"always on\" algorithm, since in the \"random 10%\" algorithm, 10% of the SCs are randomly turned off. However, the number of active SCs is constant with regard to the number of UEs. The proposed and \"BPSO only\" algorithms have the least number of active SCs, as the SCs are turned off utilizing the BPSO algorithm. In both algorithms (BPSO only and proposed), BPSO is used for SC switching. That is why the number of active SCs of the two algorithms