## 5 Results and Discussion
In the reassociation process, the user can be rejected if the amount of requested resource blocks exceeds the available resource blocks of suboptimal RRH. The blocking probability is the ratio of the number of rejected users at reassociation to the number of suboptimal served users. For the scenarios presented in Figure 4a,b with Gaussian and uniform distribution, the online algorithm shows approximately 8%-33% fewer sub-optimal served users compared to the offline algorithm. In Figure 4c,d, the online algorithm gets approximately 9%-64% lower blocking probability than the offline algorithm. We consider vehicular mobility environment that leads to more frequent handovers and mobility patterns than pedestrians. In the highly mobile environment, the online algorithm brings better performance, because RRH is switched based on the  
Figure 3: Comparison of the average SINRs. (**a**) San Francisco and (**c**) Beijing datasets with Gaussian distributions; (**b**) San Francisco and (**d**) Beijing datasets with uniform distributions. number of servicing users and the number of minimum requested resource blocks on current served RRHs (offline) or predicted RRHs (online). Figure 5 shows a comparison of the energy efficiencies for the San Francisco dataset for different speeds and distribution scenarios as a function of the number of users. In Figure 5a,b where the speeds are 2x, the proposed algorithm has a higher energy efficiency by approximately 18-64% and 24%-64% compared to A-HN and A-HC, respectively. In Figure 5c,d, where the speeds are 4x, the proposed algorithm has a higher energy efficiency by approximately 10%-60% and 19%-59% compared to A-HN and A-HC, respectively. In all of the scenarios, the proposed algorithm has a higher energy efficiency by approximately 30%-67% compared to FA. Gains in average energy efficiency are observed with the increase in the mobility speed, as the mobility with a higher speed has a more consistent pattern. A mobility with consistent patterns is more likely to be predicted, so that the proposed algorithm can provide a high-quality service based on prediction. However, the other algorithms in the comparison do not consider the mobility, but only the current environments, and perform the switching operation, which explains the higher performance of the proposed algorithm in high-speed scenarios, compared to A-HN and A-HC. Figure 4: Comparison of the online and offline algorithm with 500 UEs. (**a**) The number of suboptimal served users with Gaussian distribution and (**b**) with uniform distribution; (**c**) blocking probability with Gaussian distribution and (**d**) with uniform distribution. Figure 6 shows a comparison of the energy efficiencies in environments with 15 RRHs for different datasets and distribution scenarios as a function of the number of users. Many RRHs offer higher-quality data services; however, there are also interference problems. Therefore, it is important to provide service by optimal RRHs in a highly interfering environment. Simulations were performed to evaluate the changes in energy efficiency when more RRHs were deployed. In Figure 6a,c with Gaussian distributions, the proposed algorithm has a higher energy efficiency by approximately 55%-77% and 52%-77% compared to A-HN and A-HC, respectively. In Figure 6b,d with uniform distributions, the proposed algorithm has a higher energy efficiency by approximately 28%-77% and 50%-77% compared to A-HN and A-HC, respectively. In all of the scenarios, the proposed algorithm has a higher energy efficiency by approximately 31-67% compared to the FA algorithm. The proposed algorithm is better than the other algorithms as it enables users to receive services by optimal RRHs among the various RRHs. In order to evaluate the performance of the proposed algorithm, comparisons were performed with FA and two other algorithms. The proposed algorithm exhibited the best performance in terms of energy efficiency, as the number of UEs receiving services from optimal RRHs was increased.