## 5 Simulation Results and Analysis
### Simulation Results and Discussions  
Table 5 summarises the complexity of the three BS-Sw methods proposed in this work based on the example given in Figure 3. The complexity term in this context means the number of choices to be considered or computed before deciding which SBSs are to be switched off. Generally, the computational complexity of the binomial technique increases exponentially as the number of SBSs increases. Whereas, the random switching technique induces a constant trivial complexity regardless of the number of SBSs. On the other hand, a distance-based technique incurs a medium computational complexity depending on the distance of SBSs and UEs from the MBS. In the given example with four SBSs, the binomial method is 80% more complex compared to the distance-based method. Additionally, the complexity of different number of SBSs are determined and compared by assuming similar threshold conditions as the former example. While the number of SBSs is 8, the binomial method results in more than 90% complexity compared to the distance-based method. It is proven that the rise in number of SBSs drastically increases the computational complexity of the binomial BSS technique. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Setting** \\ \hline System bandwidth & 10 MHz \\ \hline Noise power density & \(-174\) dBm/Hz \\ \hline Radius of macrocell & 500 m \\ Radius of small cells & 40 m \\ \hline Path loss models & MBS: \(128.1+37.6\log(d_{km})\) dB \\ SBS: \(140.7+36.7\log(d_{km})\) dB \\ \hline Channel fading model & exponentially distributed Rayleigh \\  & zero mean \& unit variance \\ \hline Shadowing model & \begin{tabular}{c} iid log-normal \\ zero mean \& 10 dB standard deviation \\ \end{tabular} \\ \hline Efficiency of power amplifier & \
\begin{tabular}{c} MBS: 39\% \\ SBS: 7\% \\ \end{tabular} \\ \hline Static power &
\begin{tabular}{c} MBS: 130 W \\ SBS: 6.8 W \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Setting of the simulation parameters. Figure 5 depicts the impact of the number of SBSs on the EE of the system. Generally, the EE improves as the number of SBSs increases owing to the fact that the total sum rate rises at a rate faster than the consumed power. The EE of the CoMP-enabled binomial method performs around 5% better than the CoMP-enabled distance-based method but at the expense of much higher computational complexity. As the number of SBSs increases, the complexity rises exponentially since the EE of all the possible combinations of SBSs need to be evaluated before selecting the optimal EE. On the contrary, the sub-optimal method provides a near-optimal (95% of the optimal performance) result at much lower constant computational complexity as the number of SBSs increased. Further, random BS-Sw performs much worse, about 25% worse, than the binomial method in terms of EE, making it a poorer choice of switching technique. The binomial method was further investigated for a CoMP-enabled non-harvesting as well as Non-CoMP harvesting techniques where both the trends coincide with each other and its EE is nearly 20% lower than that of the CoMP-enabled harvesting technique. Both energy harvesting and data cooperation contribute fairly to EE but combining both techniques produce a much higher EE. Figure 6 depicts the impact of the threshold value of the ratio of the SBS harvested energy to the transmit power on the EE of the system. In general, the increase in the number of UEs leads to a rise in EE of the system owing to the fact that the sum rate inflates more rapidly than the total power consumption. When the threshold value \(E_{j,thr}^{R}\), is set to 0.7, the EE of the system is around 5% better than setting it to 0 or 1.2. A threshold value less than 1 indicates that the SBS transmits more power than the harvested energy and vice versa. If the ratio of SBS harvested energy to transmit power is greater than the threshold value, the SBS will be considered to be switched on.