## 5 Simulation Results and Analysis
### Simulation Results and Discussions
In that case, setting the threshold to a higher value limits the potential SBSs that can be switched on, hence leading to a lower EE. Adjustment of this ratio greatly aids in enhancing the EE of the system, which consists of SBSs that harvest at an inconsistent rate. Figure 5: EE for various number of SBSs analysed for different BS-Sw methods and BS coordination techniques with \(E_{M}^{HC}=10\) dB and \(E_{S}^{HC}=2\) dB. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No. of SBSs** & **BS-Sw Technique** & **Complexity (\(\mathbf{2^{SBS}-1}\))** \\ \hline \multirow{3}{*}{4} & Binomial & \(2^{4}-1\) \\  & Distance-based & \(2^{2}-1\) \\  & Random & \(1\) \\ \hline \multirow{3}{*}{6} & Binomial & \(2^{6}-1\) \\  & Distance-based & \(2^{3}-1\) \\  & Random & \(1\) \\ \hline \multirow{3}{*}{8} & Binomial & \(2^{8}-1\) \\  & Distance-based & \(2^{4}-1\) \\ \cline{1-1}  & Random & \(1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Computational complexity of the proposed BS-Sw techniques. Figure 7 illustrates the system EE for different HE of SBSs using different BS-Sw algorithms. In general, the EE rises as the HE of the SBSs increases, as a result of the GPC reduction and in turn more HE is used by the system. The proposed binomial method was investigated for both CoMP and Non-CoMP techniques. BS-Sw for the CoMP technique using the binomial method generates an optimal EE solution, but again at the cost of higher computational complexity. On the other hand, CoMP-enabled distance-based solution, which is a sub-optimal method, still yields a satisfactory EE performance that was about 5% lower than the binomial method, but at a much lower complexity. The near-optimal performance gained by the proposed distance-based solution is mainly due to switching off the SBSs nearer to the MBS, which leads to a smaller power requirement by the latter to serve the users. On the other hand, random BS-Sw, which is the least complex scheme, only achieves 85% of the performance achieved by the distance-based method. In fact, the BS-Sw of the Non-CoMP technique using the binomial method performs around 5% better than the CoMP-enabled random BS-Sw. Figure 8 shows the effect of the number of users per cell on the EE of the system. It is observed that the increase in the number of users enhances the EE of the system because the system throughput increases at a higher rate than the increase in the total power consumption in the scenarios with sufficient bandwidths. The average difference between the optimal and sub-optimal method is around 5% whereas the random method performs around 25% lower than the optimal method. Serving the SBS users with sum distance shorter from the MBS boosts the EE performance as the required power is lesser. Since the random method switches off the BSs arbitrarily, it results in an inferior performance. Both the CoMP-enabled non-harvesting as well as Non-CoMP harvesting techniques increase at similar trends and on average, nearly 20% worse than the CoMP-enabled harvesting technique in terms of EE. Once again, it is proven that both data and energy cooperation play a vital role in improving the EE of the system. Figure 6: Impact of \(E_{j,thr}^{R}\) on EE for a different number of users per cell with \(E_{M}^{RC}=10\) dB and \(E_{S}^{HC}\) ranges randomly from 0 dB to 2 dB. Figure 9 shows the impact of HE efficiency on the EE of the system. From the figure, it is worth noting that higher HE results in improved EE. Under different HE efficiency, it is noticed that the EE of the binomial method is around 5% higher than the distance-based method and nearly 15% better than the random method. Typically, while users are near to the serving BS and the number of UEs served by the BS is fewer, the system provides a better EE. The random method does not necessarily switch off the nearest serving SBS or the SBS with fewer UEs, it can even be the other way round, which is the reason for yielding a worse EE.