## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Notation** & **Value** \\ \hline Channel bandwidth & \(B\) & 100 MHz \\ Noise power spectral density & \(N_{0}\) & \(-174\) (dBm/Hz) \\ Requirement threshold of SINR & \(SINR_{th}\) & 0 (dB) \\ Maximum outage probability & \(p_{outage}^{max}\) & 0.05 \\ Number of resource blocks & \(RB\) & 50 \\ Maximum transmission power of RRH & \(p_{t}^{max,\ s}\) & 30 dBm \\ Constant power of active RRH & \(\Phi_{RRH}\) & 6.8 W \\ Power of sleep RRH & \(\Phi_{Sleep}\) & 4.3 W \\ Slope of RRH & \(\Delta s\) & 4.0 \\ Constant power of backhaul link & \(P_{th}\) & 13.25 W3 \\ Power consumption per bit/s of fronthaul link & \(\beta\) & 0.83 W \\ Constant power of fronthaul link & \(\Phi_{fh}\) & 13 W \\ Minimum required data rate of UE & \(d_{u}\) & 512, 1024, 1536, 2048, 2560 kbps \\ Maximum iteration number & \(K\) & 100 \\ Positive step size & \(\alpha\) & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. The proposed algorithm firstly switches off the RRH servicing the smaller number of users or the smaller amount of resource blocks, compared with other RRHs. Therefore, through the reassociation process, the number of users who are serviced from the suboptimal RRHs is reduced. This, in turn, works to enable users to receive higher SINRs and increase the energy efficiency of the networks. Besides, the proposed algorithm increases energy efficiency by predicting user mobility, allowing users to receive higher SINRs. In other words, the proposed algorithm switches the RRHs to enable the users to receive service from the optimal RRHs through mobility prediction before the user moves. This is achieved as A-HN and A-HC control RRHs considering energy consumption, while the proposed algorithm controls the RRHs while maximizing the SINR. Figure 3 shows a comparison of the average SINRs for different datasets and distribution scenarios as a function of the number of users. In Figure 3a,c, where Gaussian distributions are used, the proposed algorithm has an SINR approximately 51%-61% and 41%-74% higher than those of A-HN and A-HC, respectively. In Figure 3b,d, where uniform distributions are used, the SINR of the proposed algorithm is 45%-62% and 54%-63% higher than those of A-HN and A-HC, respectively. Considering all of the scenarios, the proposed algorithm has a better performance by approximately 56%-91% compared to FA. Compared to the offline algorithm, the proposed algorithm performs approximately 26%-39% and 12%-44% higher at San Francisco and Beijing datasets, respectively. Also, the offline algorithm has a 13%-45% and 9%-38% higher SINR than A-HN and A-HC, respectively, in Figure 3.  
Figure 2: Comparison of energy efficiencies. (**a**) San Francisco and (**c**) Beijing datasets with Gaussian distributions; (**b**) San Francisco and (**d**) Beijing datasets with uniform distributions. With the increase in the number of users in the simulation, the average SINR tends to decrease owing to the increased number of active RRHs. This occurs as, upon the increase in the number of active RRHs with the increase in the number of UEs, the SINR determined by the interference among RRHs is reduced. In the proposed algorithm, the RRH which services the small number of users or small amount of resource blocks is preferred to turn off the switch. In the reassociation through the RRH switching operation, the proposed algorithm reduces the associations between UEs and RRHs that correspond to suboptimal SINRs, which explains the higher SINR than those of the other algorithms. Figure 4 shows a comparison of the number of suboptimal served users and blocking probability for different datasets and distribution scenarios as a function of the number of RRHs. Our goal is reducing the number of suboptimal served users and decreasing the blocking probability. The number of suboptimal served users are the number of users who are reassociated with suboptimal RRHs because current serving RRHs have been switched OFF.